\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
MB3D & & 97.87 \% & 98.77 \% & 93.04 \% & 0.09 s / 1 core & \\
LVP(84.92) & & 97.84 \% & 98.70 \% & 93.07 \% & 0.04 s / 1 core & \\
MM-UniMODE & & 97.69 \% & 98.78 \% & 94.62 \% & 0.04 s / 1 core & \\
UDeerPEP & & 97.57 \% & 98.42 \% & 95.08 \% & 0.1 s / 1 core & Z. Dong, H. Ji, X. Huang, W. Zhang, X. Zhan and J. Chen: PeP: a Point enhanced Painting method for unified point cloud tasks. 2023.\\
OGMMDet & & 97.54 \% & 98.44 \% & 92.88 \% & 0.01 s / 1 core & \\
ECA & & 97.43 \% & 96.23 \% & 92.77 \% & 0.08 s / GPU & \\
VirConv-S & & 97.27 \% & 98.00 \% & 94.53 \% & 0.09 s / 1 core & H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.\\
ANM & & 97.23 \% & 97.67 \% & 94.59 \% & ANM / & \\
GraR-VoI & & 96.38 \% & 96.81 \% & 91.20 \% & 0.07 s / 1 core & H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.\\
VirConv-T & & 96.38 \% & 98.93 \% & 93.56 \% & 0.09 s / 1 core & H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.\\
LFT & & 96.27 \% & 99.29 \% & 88.94 \% & 0.1s / 1 core & \\
GraR-Po & & 96.18 \% & 96.84 \% & 91.11 \% & 0.06 s / 1 core & H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.\\
SFD & & 96.17 \% & 98.97 \% & 91.13 \% & 0.1 s / 1 core & X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.\\
MLF-DET & & 96.17 \% & 96.89 \% & 88.90 \% & 0.09 s / 1 core & Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.\\
VPFNet & & 96.15 \% & 96.64 \% & 91.14 \% & 0.06 s / 2 cores & H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. IEEE Transactions on Multimedia 2022.\\
HDet3D & & 96.12 \% & 96.69 \% & 91.01 \% & 0.07 s / >8 cores & \\
CLOCs & & 96.07 \% & 96.77 \% & 91.11 \% & 0.1 s / 1 core & S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.\\
ACFNet & & 96.06 \% & 96.68 \% & 93.36 \% & 0.11 s / 1 core & Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.\\
PVFusion & & 96.06 \% & 96.78 \% & 91.07 \% & 0.01 s / 1 core & \\
RDIoU & & 96.05 \% & 98.79 \% & 91.03 \% & 0.03 s / 1 core & H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X. Hua, M. Zhao and G. Lee: Rethinking IoU-based Optimization for Single- stage 3D Object Detection. ECCV 2022.\\
GraR-Vo & & 96.05 \% & 96.67 \% & 93.01 \% & 0.04 s / 1 core & H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.\\
TED & & 96.03 \% & 96.64 \% & 93.35 \% & 0.1 s / 1 core & H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.\\
CLOCs\_PVCas & & 95.96 \% & 96.76 \% & 91.08 \% & 0.1 s / 1 core & S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.\\
PVT-SSD & & 95.90 \% & 96.75 \% & 90.69 \% & 0.05 s / 1 core & H. Yang, W. Wang, M. Chen, B. Lin, T. He, H. Chen, X. He and W. Ouyang: PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer. CVPR 2023.\\
UPIDet & & 95.89 \% & 96.25 \% & 93.25 \% & 0.11 s / 1 core & Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch for Cross-modal 3D Object Detection. Thirty-seventh Conference on Neural Information Processing Systems 2023.\\
GraR-Pi & & 95.89 \% & 98.59 \% & 92.85 \% & 0.03 s / 1 core & H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.\\
MPCF & & 95.87 \% & 98.95 \% & 90.98 \% & 0.08 s / 1 core & \\
DiffCandiDet & & 95.85 \% & 96.59 \% & 93.03 \% & 0.06 s / GPU & \\
OcTr & & 95.84 \% & 96.48 \% & 90.99 \% & 0.06 s / GPU & C. Zhou, Y. Zhang, J. Chen and D. Huang: OcTr: Octree-based Transformer for 3D Object Detection. CVPR 2023.\\
DDF & & 95.83 \% & 96.53 \% & 93.25 \% & 0.1 s / 1 core & \\
CDF & & 95.83 \% & 96.22 \% & 90.77 \% & 0.08 s / 1 core & \\
VPA & & 95.82 \% & 96.71 \% & 90.95 \% & 0.01 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
3D Dual-Fusion & & 95.82 \% & 96.54 \% & 93.11 \% & 0.1 s / 1 core & Y. Kim, K. Park, M. Kim, D. Kum and J. Choi: 3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection. arXiv preprint arXiv:2211.13529 2022.\\
NIV-SSD & & 95.82 \% & 98.68 \% & 90.80 \% & 0.03 s / 1 core & \\
URFormer & & 95.81 \% & 98.52 \% & 93.03 \% & 0.1 s / 1 core & \\
GLENet-VR & & 95.81 \% & 96.85 \% & 90.91 \% & 0.04 s / 1 core & Y. Zhang, Q. Zhang, Z. Zhu, J. Hou and Y. Yuan: GLENet: Boosting 3D object detectors with generative label uncertainty estimation. International Journal of Computer Vision 2023.Y. Zhang, J. Hou and Y. Yuan: A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks. International Journal of Computer Vision 2023.\\
TSSTDet & & 95.81 \% & 96.65 \% & 93.05 \% & 0.08 s / 1 core & H. Hoang, D. Bui and M. Yoo: TSSTDet: Transformation-Based 3-D Object Detection via a Spatial Shape Transformer. IEEE Sensors Journal 2024.\\
test & & 95.80 \% & 98.39 \% & 92.86 \% & 0.1 s / 1 core & \\
DVF-V & & 95.77 \% & 96.60 \% & 90.89 \% & 0.1 s / 1 core & A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.\\
HAF-PVP\_test & & 95.76 \% & 98.86 \% & 92.95 \% & 0.09 s / 1 core & \\
Fast-CLOCs & & 95.75 \% & 96.69 \% & 90.95 \% & 0.1 s / GPU & S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.\\
3D HANet & & 95.73 \% & 98.61 \% & 92.96 \% & 0.1 s / 1 core & Q. Xia, Y. Chen, G. Cai, G. Chen, D. Xie, J. Su and Z. Wang: 3D HANet: A Flexible 3D Heatmap Auxiliary Network for Object Detection. IEEE Transactions on Geoscience and Remote Sensing 2023.\\
MAK & & 95.71 \% & 96.68 \% & 90.86 \% & 0.03 s / GPU & \\
DSGN++ & st & 95.70 \% & 98.08 \% & 88.27 \% & 0.2 s / & Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.\\
MAK\_VOXEL\_RCNN & & 95.67 \% & 98.63 \% & 92.97 \% & 0.03 s / 1 core & \\
LVP & & 95.67 \% & 98.48 \% & 92.88 \% & 0.04 s / 1 core & \\
CasA & & 95.62 \% & 96.52 \% & 92.86 \% & 0.1 s / 1 core & H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.\\
BADet & & 95.61 \% & 98.75 \% & 90.64 \% & 0.14 s / 1 core & R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.\\
SE-SSD & la & 95.60 \% & 96.69 \% & 90.53 \% & 0.03 s / 1 core & W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.\\
FEIF3D & la & 95.59 \% & 96.44 \% & 92.95 \% & 0.1 s / GPU & \\
PA-Det3D & & 95.59 \% & 96.37 \% & 90.97 \% & 0.06 s / 1 core & \\
PSMS-Net & la & 95.58 \% & 96.70 \% & 90.76 \% & 0.1 s / 1 core & \\
spark2 & & 95.58 \% & 96.41 \% & 92.87 \% & 0.1 s / 1 core & \\
FARP-Net & & 95.57 \% & 96.11 \% & 93.07 \% & 0.06 s / GPU & T. Xie, L. Wang, K. Wang, R. Li, X. Zhang, H. Zhang, L. Yang, H. Liu and J. Li: FARP-Net: Local-Global Feature Aggregation and Relation-Aware Proposals for 3D Object Detection. IEEE Transactions on Multimedia 2023.\\
voxel\_spark & & 95.55 \% & 96.38 \% & 92.86 \% & 0.04 s / GPU & \\
LoGoNet & & 95.55 \% & 96.60 \% & 93.07 \% & 0.1 s / 1 core & X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.\\
spark\_voxel\_rcnn & & 95.55 \% & 96.41 \% & 92.84 \% & 0.04 s / 1 core & \\
GD-MAE & & 95.54 \% & 98.38 \% & 90.42 \% & 0.07 s / 1 core & H. Yang, T. He, J. Liu, H. Chen, B. Wu, B. Lin, X. He and W. Ouyang: GD-MAE: Generative Decoder for MAE Pre- training on LiDAR Point Clouds. CVPR 2023.\\
spark & & 95.53 \% & 96.34 \% & 92.84 \% & 0.1 s / 1 core & \\
DVF-PV & & 95.49 \% & 96.42 \% & 92.57 \% & 0.1 s / 1 core & A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.\\
HS-fusion & & 95.48 \% & 98.33 \% & 92.66 \% & - s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SFD++ & & 95.48 \% & 98.40 \% & 92.61 \% & 0.12 s / 1 core & \\
SSLFusion & & 95.46 \% & 98.55 \% & 92.89 \% & 0.5 s / 1 core & \\
SPANet & & 95.46 \% & 96.54 \% & 90.47 \% & 0.06 s / 1 core & Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.\\
Voxel\_Spark\_focal\_we & & 95.45 \% & 96.37 \% & 92.77 \% & 0.08 s / 1 core & \\
Anonymous & & 95.44 \% & 96.41 \% & 92.85 \% & 0.1 s / 1 core & \\
LGNet-Car & & 95.43 \% & 96.52 \% & 92.73 \% & 0.11 s / 1 core & \\
PG-RCNN & & 95.40 \% & 96.66 \% & 90.55 \% & 0.06 s / GPU & I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.\\
SASA & la & 95.35 \% & 96.01 \% & 92.53 \% & 0.04 s / 1 core & C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.\\
FIRM-Net & & 95.35 \% & 96.29 \% & 92.68 \% & 0.07 s / 1 core & \\
TED-S Reproduced & & 95.33 \% & 98.45 \% & 92.75 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SPG\_mini & la & 95.32 \% & 96.23 \% & 92.68 \% & 0.09 s / GPU & Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.\\
EQ-PVRCNN & & 95.32 \% & 98.23 \% & 92.65 \% & 0.2 s / GPU & Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.\\
DEF-Model & & 95.30 \% & 96.28 \% & 92.48 \% & 0.03 s / 1 core & \\
Focals Conv & & 95.28 \% & 96.30 \% & 92.69 \% & 0.1 s / 1 core & Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.\\
CasA++ & & 95.28 \% & 95.83 \% & 94.28 \% & 0.1 s / 1 core & H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.\\
TED\_S\_baseline & & 95.26 \% & 96.25 \% & 92.62 \% & 0.09 s / 1 core & \\
VoxSeT & & 95.23 \% & 96.16 \% & 90.49 \% & 33 ms / 1 core & C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.\\
LGSLNet & & 95.22 \% & 98.00 \% & 92.72 \% & 0.1 s / GPU & \\
voxel-rcnn+++ & & 95.22 \% & 96.40 \% & 92.37 \% & 0.08 s / GPU & \\
PC-CNN-V2 & la & 95.20 \% & 96.06 \% & 89.37 \% & 0.5 s / GPU & X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.\\
PR-SSD & & 95.18 \% & 97.64 \% & 92.48 \% & 0.02 s / GPU & \\
RagNet3D & & 95.17 \% & 96.27 \% & 92.66 \% & 0.05 s / 1 core & \\
VPFNet & & 95.17 \% & 96.06 \% & 92.66 \% & 0.2 s / 1 core & C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.C. Wang, H. Chen, Y. Chen, P. Hsiao and L. Fu: VoPiFNet: Voxel-Pixel Fusion Network for Multi-Class 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2024.\\
F-PointNet & la & 95.17 \% & 95.85 \% & 85.42 \% & 0.17 s / GPU & C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.\\
EPNet++ & & 95.17 \% & 96.73 \% & 92.10 \% & 0.1 s / GPU & Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.\\
SA-SSD & & 95.16 \% & 97.92 \% & 90.15 \% & 0.04 s / 1 core & C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.\\
HMFI & & 95.16 \% & 96.29 \% & 92.45 \% & 0.1 s / 1 core & X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.\\
RBEV-Voxel & & 95.15 \% & 96.43 \% & 90.32 \% & 0.08 s / GPU & \\
USVLab BSAODet & & 95.15 \% & 96.26 \% & 92.62 \% & 0.04 s / 1 core & W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.\\
Pyramid R-CNN & & 95.13 \% & 95.88 \% & 92.62 \% & 0.07 s / 1 core & J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.\\
Voxel R-CNN & & 95.11 \% & 96.49 \% & 92.45 \% & 0.04 s / GPU & J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.\\
3DSSD & & 95.10 \% & 97.69 \% & 92.18 \% & 0.04 s / GPU & Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.\\
GF-pointnet & & 95.08 \% & 95.93 \% & 92.36 \% & 0.02 s / 1 core & \\
RAFDet & & 95.04 \% & 95.96 \% & 92.41 \% & 0.1 s / 1 core & \\
MonoSample (DID-M3D) & & 95.02 \% & 96.45 \% & 85.58 \% & 0.2 s / 1 core & \\
BPG3D & & 95.02 \% & 97.98 \% & 92.38 \% & 0.05 s / 1 core & \\
PDV & & 95.00 \% & 96.07 \% & 92.44 \% & 0.1 s / 1 core & J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.\\
MVRA + I-FRCNN+ & & 94.98 \% & 95.87 \% & 82.52 \% & 0.18 s / GPU & H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.\\
SIENet & & 94.97 \% & 96.02 \% & 92.40 \% & 0.08 s / 1 core & Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.\\
VoTr-TSD & & 94.94 \% & 95.97 \% & 92.44 \% & 0.07 s / 1 core & J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.\\
L-AUG & & 94.92 \% & 95.84 \% & 92.22 \% & 0.1 s / 1 core & T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.\\
SQD & & 94.92 \% & 98.21 \% & 92.37 \% & 0.06 s / 1 core & \\
AMVFNet & & 94.87 \% & 96.12 \% & 92.33 \% & 0.04 s / GPU & \\
GraphAlign(ICCV2023) & & 94.87 \% & 98.06 \% & 92.47 \% & 0.03 s / GPU & Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature alignment by graph matching for multi-modal 3D object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.\\
CZY\_PPF\_Net & & 94.86 \% & 98.07 \% & 92.22 \% & 0.1 s / 1 core & \\
M3DeTR & & 94.83 \% & 97.39 \% & 92.10 \% & n/a s / GPU & T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.\\
StructuralIF & & 94.81 \% & 96.14 \% & 92.12 \% & 0.02 s / 8 cores & J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.\\
spark\_second\_focal\_w & & 94.80 \% & 95.45 \% & 92.02 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
MLFusion-VS & & 94.79 \% & 98.26 \% & 92.43 \% & 0.06 s / 1 core & \\
U\_PV\_V2\_ep100\_80 & & 94.78 \% & 95.82 \% & 92.27 \% & 0... s / 1 core & \\
focalnet & & 94.78 \% & 98.07 \% & 92.32 \% & 0.05 s / 1 core & \\
XView & & 94.77 \% & 95.89 \% & 92.23 \% & 0.1 s / 1 core & L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.\\
U\_PV\_V2\_ep\_100\_100 & & 94.76 \% & 95.76 \% & 92.18 \% & 0.1 s / 1 core & \\
Spark\_partA22 & & 94.76 \% & 96.00 \% & 92.09 \% & 10 s / 1 core & \\
LGNet-3classes & & 94.76 \% & 98.13 \% & 92.15 \% & 0.11 s / 1 core & \\
focalnet & & 94.75 \% & 98.09 \% & 92.31 \% & 0.05 s / 1 core & \\
HA-PillarNet & & 94.75 \% & 95.91 \% & 92.16 \% & 0.05 s / 1 core & \\
Spark\_PartA2\_Soft\_fo & & 94.74 \% & 95.80 \% & 92.13 \% & 0.1 s / 1 core & \\
P2V-RCNN & & 94.73 \% & 96.03 \% & 92.34 \% & 0.1 s / 1 core & J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.\\
SP\_SECOND\_IOU & & 94.72 \% & 95.85 \% & 92.19 \% & 0.04 s / 1 core & \\
spark\_second & & 94.72 \% & 95.40 \% & 91.93 \% & . s / 1 core & \\
sec\_spark & & 94.71 \% & 95.37 \% & 91.93 \% & 0.03 s / GPU & \\
spark\_second2 & & 94.71 \% & 95.33 \% & 91.97 \% & 10 s / 1 core & \\
SPG & la & 94.71 \% & 97.80 \% & 92.19 \% & 0.09 s / 1 core & Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.\\
CAT-Det & & 94.71 \% & 95.97 \% & 92.07 \% & 0.3 s / GPU & Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.\\
bs & & 94.70 \% & 96.07 \% & 91.96 \% & 0.1 s / 1 core & \\
MMLab PV-RCNN & la & 94.70 \% & 98.17 \% & 92.04 \% & 0.08 s / 1 core & S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.\\
spark-part2 & & 94.69 \% & 95.71 \% & 92.09 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SDGUFusion & & 94.68 \% & 98.17 \% & 92.29 \% & 0.5 s / 1 core & \\
OFFNet & & 94.68 \% & 96.18 \% & 92.07 \% & 0.1 s / GPU & \\
SVGA-Net & & 94.67 \% & 96.05 \% & 91.86 \% & 0.03s / 1 core & Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.\\
RangeDet (Official) & & 94.64 \% & 95.50 \% & 91.77 \% & 0.02 s / 1 core & L. Fan, X. Xiong, F. Wang, N. Wang and Z. Zhang: RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.\\
DSA-PV-RCNN & la & 94.64 \% & 95.86 \% & 92.10 \% & 0.08 s / 1 core & P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.\\
CG-SSD & & 94.63 \% & 95.97 \% & 92.08 \% & 0.01 s / 1 core & \\
PV-RCNN-Plus & & 94.63 \% & 95.76 \% & 92.17 \% & 1 s / 1 core & \\
second\_iou\_baseline & & 94.62 \% & 95.74 \% & 91.97 \% & 0.05 s / 1 core & \\
RangeIoUDet & la & 94.61 \% & 95.74 \% & 91.98 \% & 0.02 s / GPU & Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.\\
PASS-PV-RCNN-Plus & & 94.59 \% & 95.79 \% & 92.10 \% & 1 s / 1 core & Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.\\
af & & 94.59 \% & 95.79 \% & 92.17 \% & 1 s / GPU & \\
DVFENet & & 94.57 \% & 95.35 \% & 91.77 \% & 0.05 s / 1 core & Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.\\
VoxelFSD & & 94.55 \% & 95.74 \% & 91.98 \% & 0.08 s / 1 core & \\
GeVo & & 94.55 \% & 95.89 \% & 92.03 \% & 0.05 s / 1 core & \\
AAMVFNet & & 94.53 \% & 95.89 \% & 91.98 \% & 0.04 s / GPU & \\
MFB3D & & 94.49 \% & 97.64 \% & 91.83 \% & 0.14 s / 1 core & \\
TuSimple & & 94.47 \% & 95.12 \% & 86.45 \% & 1.6 s / GPU & F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.\\
EPNet & & 94.44 \% & 96.15 \% & 89.99 \% & 0.1 s / 1 core & T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.\\
SERCNN & la & 94.42 \% & 96.33 \% & 89.96 \% & 0.1 s / 1 core & D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.\\
Second\_baseline & & 94.41 \% & 95.19 \% & 91.51 \% & 0.03 s / 1 core & \\
focal & & 94.41 \% & 98.28 \% & 92.09 \% & 100 s / 1 core & \\
MSIT-Det & & 94.39 \% & 97.21 \% & 86.85 \% & 0.06 s / 1 core & \\
UberATG-MMF & la & 94.25 \% & 97.41 \% & 89.87 \% & 0.08 s / GPU & M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.\\
pointpillar\_spark\_fo & & 94.24 \% & 96.44 \% & 91.33 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SRDL & & 94.24 \% & 95.86 \% & 91.80 \% & 0.05 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SC-SSD & & 94.19 \% & 95.06 \% & 91.17 \% & 1 s / 1 core & \\
u\_second\_v4\_epoch\_10 & & 94.13 \% & 95.33 \% & 91.36 \% & 0.1 s / 1 core & \\
pointpillars\_spark & & 94.04 \% & 96.88 \% & 91.17 \% & 0.02 s / GPU & \\
RangeRCNN & la & 94.03 \% & 95.48 \% & 91.74 \% & 0.06 s / GPU & Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.\\
SFA-GCL(80) & & 94.01 \% & 96.93 \% & 91.12 \% & 0.04 s / 1 core & \\
Faraway-Frustum & la & 93.99 \% & 95.81 \% & 91.72 \% & 0.1 s / GPU & H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.\\
DD3D & & 93.99 \% & 94.69 \% & 89.37 \% & n/a s / 1 core & D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .\\
spark\_pointpillar & & 93.98 \% & 96.88 \% & 91.11 \% & 0.02 s / GPU & \\
SFA-GCL & & 93.97 \% & 96.90 \% & 91.09 \% & 0.04 s / 1 core & \\
spark\_pointpillar2 & & 93.97 \% & 96.66 \% & 91.03 \% & 10 s / 1 core & \\
SFA-GCL(80, k=4) & & 93.96 \% & 96.88 \% & 91.07 \% & 0.04 s / 1 core & \\
U\_second\_v4\_ep\_100\_8 & & 93.96 \% & 94.85 \% & 91.14 \% & 0.1 s / 1 core & \\
SIF & & 93.95 \% & 95.51 \% & 91.57 \% & 0.1 s / 1 core & P. An: SIF. Submitted to CVIU 2021.\\
Anonymous & & 93.90 \% & 96.83 \% & 88.84 \% & 0.04 s / 1 core & \\
MGAF-3DSSD & & 93.87 \% & 94.45 \% & 86.37 \% & 0.1 s / 1 core & J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.\\
3ONet & & 93.87 \% & 96.97 \% & 88.84 \% & 0.1 s / 1 core & H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.\\
LPCG-Monoflex & & 93.86 \% & 96.90 \% & 83.94 \% & 0.03 s / 1 core & L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.\\
MMLAB LIGA-Stereo & st & 93.82 \% & 96.43 \% & 86.19 \% & 0.4 s / 1 core & X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.\\
SFA-GCL(baseline) & & 93.79 \% & 96.84 \% & 90.88 \% & 0.04 s / 1 core & \\
SFA-GCL\_dataaug & & 93.78 \% & 96.75 \% & 90.85 \% & 0.04 s / 1 core & \\
SFA-GCL & & 93.78 \% & 96.87 \% & 90.84 \% & 0.04 s / 1 core & \\
Sem-Aug & la & 93.77 \% & 96.79 \% & 88.78 \% & 0.1 s / GPU & L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.\\
DGEnhCL & & 93.76 \% & 96.77 \% & 90.84 \% & 0.04 s / 1 core & \\
Patches - EMP & la & 93.75 \% & 97.91 \% & 90.56 \% & 0.5 s / GPU & J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.\\
KPTr & & 93.73 \% & 96.55 \% & 90.84 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
CIA-SSD & la & 93.72 \% & 96.87 \% & 86.20 \% & 0.03 s / 1 core & W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.\\
IIOU & & 93.72 \% & 96.45 \% & 90.91 \% & 0.1 s / GPU & \\
QD-3DT & on & 93.66 \% & 94.26 \% & 83.63 \% & 0.03 s / GPU & H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.\\
MVAF-Net & & 93.66 \% & 95.37 \% & 90.90 \% & 0.06 s / 1 core & G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.\\
SSL-PointGNN & & 93.65 \% & 96.61 \% & 88.53 \% & 0.56 s / GPU & E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.\\
PA3DNet & & 93.62 \% & 96.57 \% & 88.65 \% & 0.1 s / GPU & M. Wang, L. Zhao and Y. Yue: PA3DNet: 3-D Vehicle Detection with Pseudo Shape Segmentation and Adaptive Camera- LiDAR Fusion. IEEE Transactions on Industrial Informatics 2023.\\
IA-SSD (multi) & & 93.56 \% & 96.10 \% & 90.68 \% & 0.014 s / 1 core & Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.\\
MonoLiG & & 93.56 \% & 96.70 \% & 83.74 \% & 0.03 s / 1 core & A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.\\
MonoPair & & 93.55 \% & 96.61 \% & 83.55 \% & 0.06 s / GPU & Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.\\
MMAE & la & 93.55 \% & 96.52 \% & 90.53 \% & 0.07 s / 1 core & \\
SCNet3D & & 93.54 \% & 96.55 \% & 90.89 \% & 0.08 s / 1 core & \\
IA-SSD (single) & & 93.54 \% & 96.26 \% & 88.49 \% & 0.013 s / 1 core & Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.\\
EBM3DOD & & 93.54 \% & 96.81 \% & 88.33 \% & 0.12 s / 1 core & F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.\\
casxv1 & & 93.52 \% & 96.67 \% & 90.85 \% & 0.01 s / 1 core & \\
SeSame-point & & 93.50 \% & 95.22 \% & 90.44 \% & N/A s / TITAN RTX & \\
Deep MANTA & & 93.50 \% & 98.89 \% & 83.21 \% & 0.7 s / GPU & F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.\\
VoxelFSD-S & & 93.50 \% & 94.70 \% & 90.40 \% & 0.05 s / 1 core & \\
Point-GNN & la & 93.50 \% & 96.58 \% & 88.35 \% & 0.6 s / GPU & W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.\\
BtcDet & la & 93.47 \% & 96.23 \% & 88.55 \% & 0.09 s / GPU & Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.\\
MOPNet & & 93.47 \% & 96.57 \% & 83.62 \% & 0.1 s / 1 core & \\
LVFSD & & 93.45 \% & 95.28 \% & 90.73 \% & 0.06 s / & ERROR: Wrong syntax in BIBTEX file.\\
Struc info fusion II & & 93.45 \% & 96.72 \% & 88.31 \% & 0.05 s / GPU & P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.\\
EBM3DOD baseline & & 93.45 \% & 96.72 \% & 88.25 \% & 0.05 s / 1 core & F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.\\
IOUFusion & & 93.43 \% & 96.38 \% & 90.63 \% & 0.1 s / GPU & \\
StereoDistill & & 93.43 \% & 97.61 \% & 87.71 \% & 0.4 s / 1 core & Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.\\
MonoLSS & & 93.42 \% & 96.19 \% & 83.62 \% & 0.04 s / 1 core & Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.\\
RRC & & 93.40 \% & 95.68 \% & 87.37 \% & 3.6 s / GPU & J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.\\
GSG-FPS & & 93.38 \% & 95.73 \% & 90.49 \% & 0.01 s / 1 core & \\
pointpillar\_baseline & & 93.37 \% & 95.17 \% & 88.94 \% & 0.01 s / 1 core & \\
3D-CVF at SPA & la & 93.36 \% & 96.78 \% & 86.11 \% & 0.06 s / 1 core & J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.\\
IIOU\_LDR & & 93.33 \% & 96.33 \% & 88.22 \% & 0.03 s / 1 core & \\
RAFDet & & 93.33 \% & 95.89 \% & 90.51 \% & 0.01 s / 1 core & \\
SNVC & st & 93.32 \% & 96.33 \% & 85.81 \% & 1 s / GPU & S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.\\
DFAF3D & & 93.32 \% & 96.58 \% & 90.24 \% & 0.05 s / 1 core & Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.\\
Struc info fusion I & & 93.31 \% & 96.59 \% & 88.23 \% & 0.05 s / 1 core & P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.\\
ROT\_S3D & & 93.30 \% & 96.28 \% & 88.20 \% & 0.1 s / GPU & \\
CityBrainLab-CT3D & & 93.30 \% & 96.28 \% & 90.58 \% & 0.07 s / 1 core & H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.\\
Test\_dif & & 93.24 \% & 95.78 \% & 90.40 \% & 0.01 s / 1 core & \\
STD & & 93.22 \% & 96.14 \% & 90.53 \% & 0.08 s / GPU & Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.\\
SARPNET & & 93.21 \% & 96.07 \% & 88.09 \% & 0.05 s / 1 core & Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.\\
H^23D R-CNN & & 93.20 \% & 96.20 \% & 90.55 \% & 0.03 s / 1 core & J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.\\
Fast Point R-CNN & la & 93.18 \% & 96.13 \% & 87.68 \% & 0.06 s / GPU & Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.\\
RAFDet & & 93.18 \% & 95.70 \% & 90.40 \% & 0.01 s / 1 core & \\
sensekitti & & 93.17 \% & 94.79 \% & 84.38 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
P2P & & 93.14 \% & 96.67 \% & 88.01 \% & 0.1 s / GPU & \\
SJTU-HW & & 93.11 \% & 96.30 \% & 82.21 \% & 0.85s / GPU & S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.\\
FromVoxelToPoint & & 93.06 \% & 96.08 \% & 90.53 \% & 0.1 s / 1 core & J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.\\
GA-RCNN & & 93.03 \% & 96.10 \% & 90.41 \% & 47ms / 1 core & \\
MG & & 93.01 \% & 96.27 \% & 90.15 \% & 0.1 s / 1 core & \\
CLOCs\_SecCas & & 92.95 \% & 95.43 \% & 89.21 \% & 0.1 s / 1 core & S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.\\
centerpoint\_pcdet & & 92.94 \% & 94.73 \% & 89.50 \% & 0.06 s / 1 core & \\
WA & & 92.93 \% & 96.02 \% & 87.41 \% & 0.3 s / GPU & \\
MonoCD & & 92.91 \% & 96.43 \% & 85.55 \% & n/a s / 1 core & L. Yan, P. Yan, S. Xiong, X. Xiang and Y. Tan: MonoCD: Monocular 3D Object Detection with Complementary Depths. CVPR 2024.\\
SFEBEV & & 92.84 \% & 97.88 \% & 89.66 \% & 0.01 s / 1 core & \\
ACDet & & 92.84 \% & 96.18 \% & 89.83 \% & 0.05 s / 1 core & J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.\\
HotSpotNet & & 92.81 \% & 96.21 \% & 89.80 \% & 0.04 s / 1 core & Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.\\
PUDet & & 92.77 \% & 96.32 \% & 90.02 \% & 0.3 s / GPU & \\
SegVoxelNet & & 92.73 \% & 96.00 \% & 87.60 \% & 0.04 s / 1 core & H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.\\
Patches & la & 92.72 \% & 96.34 \% & 87.63 \% & 0.15 s / GPU & J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.\\
Cube R-CNN & & 92.72 \% & 95.78 \% & 84.81 \% & 0.05 s / GPU & G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.\\
CenterNet3D & & 92.69 \% & 95.76 \% & 89.81 \% & 0.04 s / GPU & G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.\\
R-GCN & & 92.67 \% & 96.19 \% & 87.66 \% & 0.16 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
PI-RCNN & & 92.66 \% & 96.17 \% & 87.68 \% & 0.1 s / 1 core & L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.\\
PointPainting & la & 92.58 \% & 98.39 \% & 89.71 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
MMpointpillars & & 92.57 \% & 95.47 \% & 87.45 \% & 0.05 s / 1 core & \\
BAPartA2S-4h & & 92.53 \% & 95.82 \% & 89.80 \% & 0.1 s / 1 core & \\
DASS & & 92.53 \% & 96.23 \% & 87.75 \% & 0.09 s / 1 core & O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.\\
3D IoU-Net & & 92.47 \% & 96.31 \% & 87.67 \% & 0.1 s / 1 core & J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.\\
Associate-3Ddet & & 92.45 \% & 95.61 \% & 87.32 \% & 0.05 s / 1 core & L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.\\
S-AT GCN & & 92.44 \% & 95.06 \% & 90.78 \% & 0.02 s / GPU & L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.\\
voxelnext\_pcdet & & 92.38 \% & 94.01 \% & 89.60 \% & 0.05 s / 1 core & \\
CAT2 & & 92.37 \% & 95.77 \% & 87.27 \% & 1 s / 1 core & \\
HA PillarNet & & 92.37 \% & 95.38 \% & 87.40 \% & 0.05 s / 1 core & \\
PointRGCN & & 92.33 \% & 97.51 \% & 87.07 \% & 0.26 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
Sem-Aug-PointRCNN++ & & 92.32 \% & 95.65 \% & 87.62 \% & 0.1 s / 8 cores & L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.\\
TF-PartA2 & & 92.31 \% & 95.57 \% & 89.50 \% & 0.1 s / 1 core & \\
Harmonic PointPillar & & 92.25 \% & 95.16 \% & 89.11 \% & 0.01 s / 1 core & H. Zhang, J. Mekala, Z. Nain, J. Park and H. Jung: 3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection for V2X Orchestration. will submit to IEEE Transactions on Vehicular Technology 2022.\\
F-ConvNet & la & 92.19 \% & 95.85 \% & 80.09 \% & 0.47 s / GPU & Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.\\
PFF3D & la & 92.15 \% & 95.37 \% & 87.54 \% & 0.05 s / GPU & L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.\\
PASS-PointPillar & & 92.09 \% & 95.20 \% & 88.73 \% & 1 s / 1 core & Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.\\
PartA2\_basline & & 92.07 \% & 95.65 \% & 89.54 \% & 0.09 s / 1 core & \\
SDP+RPN & & 92.03 \% & 95.16 \% & 79.16 \% & 0.4 s / GPU & F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.\\
AB3DMOT & la on & 92.00 \% & 95.88 \% & 86.98 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
MMpp & & 91.97 \% & 95.21 \% & 87.04 \% & 0.05 s / 1 core & \\
XT-PartA2 & & 91.92 \% & 95.38 \% & 89.23 \% & 0.1 s / GPU & \\
MMLab-PointRCNN & la & 91.90 \% & 95.92 \% & 87.11 \% & 0.1 s / GPU & S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.\\
mm3d\_PartA2 & & 91.88 \% & 95.27 \% & 89.21 \% & 0.1 s / GPU & \\
MMLab-PartA^2 & la & 91.86 \% & 95.03 \% & 89.06 \% & 0.08 s / GPU & S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.\\
mmFUSION & & 91.84 \% & 95.69 \% & 87.05 \% & 1s / 1 core & J. Ahmad and A. Del Bue: mmFUSION: Multimodal Fusion for 3D Objects Detection. arXiv preprint arXiv:2311.04058 2023.\\
WeakM3D & & 91.81 \% & 94.51 \% & 85.35 \% & 0.08 s / 1 core & L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.\\
epBRM & la & 91.77 \% & 94.59 \% & 88.45 \% & 0.1 s / GPU & K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.\\
C-GCN & & 91.73 \% & 95.64 \% & 86.37 \% & 0.147 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
ITVD & & 91.73 \% & 95.85 \% & 79.31 \% & 0.3 s / GPU & Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.\\
MM\_SECOND & & 91.71 \% & 95.14 \% & 86.75 \% & 0.05 s / GPU & \\
SINet+ & & 91.67 \% & 94.17 \% & 78.60 \% & 0.3 s / & X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.\\
Cascade MS-CNN & & 91.60 \% & 94.26 \% & 78.84 \% & 0.25 s / GPU & Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019.Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.\\
SeSame-pillar & & 91.57 \% & 95.13 \% & 88.41 \% & N/A s / TITAN RTX & \\
PointRGBNet & & 91.48 \% & 95.40 \% & 86.50 \% & 0.08 s / 4 cores & P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.\\
MAFF-Net(DAF-Pillar) & & 91.46 \% & 94.38 \% & 83.89 \% & 0.04 s / 1 core & Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.\\
HRI-VoxelFPN & & 91.44 \% & 96.65 \% & 86.18 \% & 0.02 s / GPU & H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.\\
MonoAux-v2 & & 91.43 \% & 94.42 \% & 79.20 \% & 0.04 s / GPU & \\
TBD & & 91.39 \% & 96.76 \% & 81.51 \% & 0.04 s / 1 core & \\
EgoNet & & 91.39 \% & 96.18 \% & 81.33 \% & 0.1 s / GPU & S. Li, Z. Yan, H. Li and K. Cheng: Exploring intermediate representation for monocular vehicle pose estimation. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.\\
SeSame-pillar w/scor & & 91.34 \% & 94.89 \% & 88.13 \% & N/A s / 1 core & \\
FDGNet & & 91.31 \% & 96.44 \% & 83.51 \% & 0.1 s / 1 core & \\
SHUD & & 91.28 \% & 96.57 \% & 81.36 \% & 0.04 s / 1 core & \\
Stereo CenterNet & st & 91.27 \% & 96.61 \% & 83.50 \% & 0.04 s / GPU & Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.\\
PointPillars & la & 91.19 \% & 94.00 \% & 88.17 \% & 16 ms / & A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.\\
MEDL-U & & 91.19 \% & 96.70 \% & 86.06 \% & 1 s / GPU & \\
LTN & & 91.18 \% & 94.68 \% & 81.51 \% & 0.4 s / GPU & T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.\\
EOTL & & 91.17 \% & 96.31 \% & 81.20 \% & TBD s / 1 core & R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.\\
WS3D & la & 91.15 \% & 95.13 \% & 86.52 \% & 0.1 s / GPU & Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.\\
BA2-Det+MonoFlex & & 91.12 \% & 96.45 \% & 81.30 \% & 0.03 s / 1 core & \\
LWLANet & & 91.12 \% & 94.22 \% & 81.22 \% & 0.1 s / 1 core & \\
MonoSGC & & 91.10 \% & 94.21 \% & 83.45 \% & 0.04 s / 1 core & \\
NeurOCS & & 91.08 \% & 96.39 \% & 81.20 \% & 0.1 s / GPU & Z. Min, B. Zhuang, S. Schulter, B. Liu, E. Dunn and M. Chandraker: NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization. CVPR 2023.\\
MSFENet & & 91.08 \% & 96.47 \% & 83.43 \% & 0.1 s / 1 core & \\
KM3D & & 91.07 \% & 96.44 \% & 81.19 \% & 0.03 s / 1 core & P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.\\
DID-M3D & & 91.04 \% & 94.29 \% & 81.31 \% & 0.04 s / 1 core & L. Peng, X. Wu, Z. Yang, H. Liu and D. Cai: DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection. ECCV 2022.\\
FII-CenterNet & & 91.03 \% & 94.48 \% & 83.00 \% & 0.09 s / GPU & S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.\\
Aston-EAS & & 91.02 \% & 93.91 \% & 77.93 \% & 0.24 s / GPU & J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.\\
MonoFlex & & 91.02 \% & 96.01 \% & 83.38 \% & 0.03 s / GPU & Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.\\
Mix-Teaching & & 91.02 \% & 96.35 \% & 83.41 \% & 30 s / 1 core & L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.\\
ARPNET & & 90.99 \% & 94.00 \% & 83.49 \% & 0.08 s / GPU & Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.\\
CIE & & 90.98 \% & 96.31 \% & 83.43 \% & 0.1 s / 1 core & Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.\\
HINTED & & 90.97 \% & 95.16 \% & 85.55 \% & 0.04 s / 1 core & \\
DCD & & 90.93 \% & 96.44 \% & 83.36 \% & 0.03 s / 1 core & Y. Li, Y. Chen, J. He and Z. Zhang: Densely Constrained Depth Estimator for Monocular 3D Object Detection. European Conference on Computer Vision 2022.\\
prcnn\_v18\_80\_100 & & 90.88 \% & 96.21 \% & 85.85 \% & 0.1 s / 1 core & \\
MonoEF & & 90.88 \% & 96.32 \% & 83.27 \% & 0.03 s / 1 core & Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.\\
PatchNet & & 90.87 \% & 93.82 \% & 79.62 \% & 0.4 s / 1 core & X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.\\
MV3D & la & 90.83 \% & 96.47 \% & 78.63 \% & 0.36 s / GPU & X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.\\
monodle & & 90.81 \% & 93.83 \% & 80.93 \% & 0.04 s / GPU & X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .\\
3D IoU Loss & la & 90.79 \% & 95.92 \% & 85.65 \% & 0.08 s / GPU & D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.\\
SINet\_VGG & & 90.79 \% & 93.59 \% & 77.53 \% & 0.2 s / & X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.\\
HomoLoss(monoflex) & & 90.69 \% & 95.92 \% & 80.91 \% & 0.04 s / 1 core & J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.\\
VSAC & & 90.68 \% & 96.18 \% & 87.93 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
TANet & & 90.67 \% & 93.67 \% & 85.31 \% & 0.035s / GPU & Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.\\
MonoCInIS & & 90.60 \% & 96.05 \% & 82.43 \% & 0,13 s / GPU & J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.\\
SeSame-voxel & & 90.55 \% & 95.78 \% & 87.62 \% & N/A s / TITAN RTX & \\
MonoCDiT & & 90.53 \% & 96.01 \% & 80.69 \% & 0.05 s / GPU & \\
DFSemONet(Baseline) & & 90.51 \% & 95.58 \% & 87.58 \% & 0.04 s / GPU & \\
CG-Stereo & st & 90.38 \% & 96.31 \% & 82.80 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
SCNet & la & 90.30 \% & 95.59 \% & 85.09 \% & 0.04 s / GPU & Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.\\
CMKD & & 90.28 \% & 95.14 \% & 83.91 \% & 0.1 s / 1 core & Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.\\
PS-fld & & 90.27 \% & 95.75 \% & 82.32 \% & 0.25 s / 1 core & Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.\\
ODGS & & 90.20 \% & 95.79 \% & 85.23 \% & 0.1 s / 1 core & \\
MonoSTL & & 90.19 \% & 95.32 \% & 80.53 \% & na s / 1 core & \\
Deep3DBox & & 90.19 \% & 94.71 \% & 76.82 \% & 1.5 s / GPU & A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.\\
FQNet & & 90.17 \% & 94.72 \% & 76.78 \% & 0.5 s / 1 core & L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.\\
MonoSIM\_v2 & & 90.12 \% & 95.91 \% & 80.67 \% & 0.03 s / 1 core & \\
DeepStereoOP & & 90.06 \% & 95.15 \% & 79.91 \% & 3.4 s / GPU & C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.\\
PI-SECOND & & 89.99 \% & 95.31 \% & 86.86 \% & 0.05 s / GPU & \\
SubCNN & & 89.98 \% & 94.26 \% & 79.78 \% & 2 s / GPU & Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.\\
MLOD & la & 89.97 \% & 94.88 \% & 84.98 \% & 0.12 s / GPU & J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.\\
GPP & & 89.96 \% & 94.02 \% & 81.13 \% & 0.23 s / GPU & A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. IEEE Transactions on Intelligent Vehicles 2020.\\
AVOD & la & 89.88 \% & 95.17 \% & 82.83 \% & 0.08 s / & J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.\\
SINet\_PVA & & 89.86 \% & 92.72 \% & 76.47 \% & 0.11 s / & X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.\\
3DOP & st & 89.55 \% & 92.96 \% & 79.38 \% & 3s / GPU & X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.\\
ADD & & 89.53 \% & 94.82 \% & 81.60 \% & 0.1 s / 1 core & Z. Wu, Y. Wu, J. Pu, X. Li and X. Wang: Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection. AAAI2023 .\\
IAFA & & 89.46 \% & 93.08 \% & 79.83 \% & 0.04 s / 1 core & D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.\\
Mono3D & & 89.37 \% & 94.52 \% & 79.15 \% & 4.2 s / GPU & X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.\\
4d-MSCNN & st & 89.37 \% & 92.40 \% & 77.00 \% & 0.3 min / GPU & P. Ferraz, B. Oliveira, F. Ferreira, C. Silva Martins and others: Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision. IET Intelligent Transport Systems 2020.\\
MonoDDE & & 89.19 \% & 96.76 \% & 81.60 \% & 0.04 s / 1 core & Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.\\
MonoUNI & & 88.96 \% & 94.30 \% & 78.95 \% & 0.04 s / 1 core & J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.\\
AVOD-FPN & la & 88.92 \% & 94.70 \% & 84.13 \% & 0.1 s / & J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.\\
PCT & & 88.78 \% & 96.45 \% & 78.85 \% & 0.045 s / 1 core & L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.\\
OPA-3D & & 88.77 \% & 96.50 \% & 76.55 \% & 0.04 s / 1 core & Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.\\
AM3D & & 88.71 \% & 92.55 \% & 77.78 \% & 0.4 s / GPU & X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.\\
MS-CNN & & 88.68 \% & 93.87 \% & 76.11 \% & 0.4 s / GPU & Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.\\
MonoPSR & & 88.50 \% & 93.63 \% & 73.36 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
Shift R-CNN (mono) & & 88.48 \% & 94.07 \% & 78.34 \% & 0.25 s / GPU & A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.\\
RCD & & 88.46 \% & 92.52 \% & 83.73 \% & 0.1 s / GPU & A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.\\
MM-MRFC & fl la & 88.46 \% & 95.54 \% & 78.14 \% & 0.05 s / GPU & A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.\\
MonoDTR & & 88.41 \% & 93.90 \% & 76.20 \% & 0.04 s / 1 core & K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.\\
3DBN & la & 88.29 \% & 93.74 \% & 80.74 \% & 0.13s / & X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.\\
MonoCInIS & & 88.16 \% & 96.22 \% & 75.72 \% & 0,14 s / GPU & J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.\\
error & & 88.15 \% & 94.60 \% & 79.97 \% & 1 s / 1 core & \\
MonoRUn & & 87.91 \% & 95.48 \% & 78.10 \% & 0.07 s / GPU & H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.\\
PS-SVDM & & 87.55 \% & 94.49 \% & 78.21 \% & 1 s / 1 core & Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.\\
SMOKE & & 87.51 \% & 93.21 \% & 77.66 \% & 0.03 s / GPU & Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.\\
SH3D & & 87.33 \% & 95.79 \% & 77.76 \% & 0.1 s / 1 core & \\
MonoFRD & & 87.31 \% & 95.25 \% & 77.66 \% & 0.01 s / 1 core & \\
CDN & st & 87.19 \% & 95.85 \% & 79.43 \% & 0.6 s / GPU & D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.\\
RTM3D & & 86.93 \% & 91.82 \% & 77.41 \% & 0.05 s / GPU & P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.\\
MonoNeRD & & 86.89 \% & 94.60 \% & 77.23 \% & na s / 1 core & J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.\\
MonoRCNN & & 86.78 \% & 91.98 \% & 66.97 \% & 0.07 s / GPU & X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.\\
BirdNet+ & la & 86.73 \% & 92.61 \% & 81.80 \% & 0.11 s / & A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.\\
MonoRCNN++ & & 86.69 \% & 94.31 \% & 71.87 \% & 0.07 s / GPU & X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.\\
DEVIANT & & 86.64 \% & 94.42 \% & 76.69 \% & 0.04 s / & A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.\\
MonoAuxNorm & & 86.62 \% & 92.56 \% & 78.73 \% & 0.02 s / GPU & \\
GUPNet & & 86.45 \% & 94.15 \% & 74.18 \% & NA s / 1 core & Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.\\
DSGN & st & 86.43 \% & 95.53 \% & 78.75 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
MonoAIU & & 86.30 \% & 94.06 \% & 71.53 \% & 0.03 s / GPU & \\
MonoDETR & & 86.17 \% & 93.99 \% & 76.19 \% & 0.04 s / 1 core & R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection. arXiv preprint arXiv:2203.13310 2022.\\
mdab & & 86.15 \% & 94.14 \% & 76.25 \% & 22 s / 1 core & \\
PS-SVDM & & 86.15 \% & 94.45 \% & 77.86 \% & 1 s / 1 core & Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.\\
Stereo R-CNN & st & 85.98 \% & 93.98 \% & 71.25 \% & 0.3 s / GPU & P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.\\
Anonymous & & 85.82 \% & 94.15 \% & 71.22 \% & 0.1 s / 1 core & \\
StereoFENet & st & 85.70 \% & 91.48 \% & 77.62 \% & 0.15 s / 1 core & W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.\\
DE\_Fusion & & 85.69 \% & 93.85 \% & 75.81 \% & 0.06 s / 1 core & \\
MonoSIM & & 85.65 \% & 93.99 \% & 78.58 \% & 0.16 s / 1 core & \\
DMF & st & 85.49 \% & 89.50 \% & 82.52 \% & 0.2 s / 1 core & X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.\\
ResNet-RRC\_Car & & 85.33 \% & 91.45 \% & 74.27 \% & 0.06 s / GPU & H. Jeon and others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.\\
PL++ (SDN+GDC) & st la & 85.15 \% & 94.95 \% & 77.78 \% & 0.6 s / GPU & Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.\\
M3D-RPN & & 85.08 \% & 89.04 \% & 69.26 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
CDN-PL++ & st & 85.01 \% & 94.66 \% & 77.60 \% & 0.4 s / GPU & D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.\\
SDP+CRC (ft) & & 85.00 \% & 92.06 \% & 71.71 \% & 0.6 s / GPU & F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.\\
Occlude3D & & 84.98 \% & 93.32 \% & 75.25 \% & 0.01 s / 1 core & \\
SS3D & & 84.92 \% & 92.72 \% & 70.35 \% & 48 ms / & E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.\\
Sample & & 84.79 \% & 93.17 \% & 75.11 \% & 0.01 s / 1 core & \\
MonoFENet & & 84.63 \% & 91.68 \% & 76.71 \% & 0.15 s / 1 core & W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.\\
DLE & & 84.45 \% & 94.66 \% & 62.10 \% & 0.06 s / & C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.\\
MV3D (LIDAR) & la & 84.39 \% & 93.08 \% & 79.27 \% & 0.24 s / GPU & X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.\\
Complexer-YOLO & la & 84.16 \% & 91.92 \% & 79.62 \% & 0.06 s / GPU & M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.\\
MonOAPC & & 84.13 \% & 92.39 \% & 74.62 \% & 0035 s / 1 core & H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2023.\\
BKDStereo3D & & 84.10 \% & 94.61 \% & 61.85 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
ZoomNet & st & 83.92 \% & 94.22 \% & 69.00 \% & 0.3 s / 1 core & L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.\\
CMAN & & 83.74 \% & 89.74 \% & 65.35 \% & 0.15 s / 1 core & C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.\\
D4LCN & & 83.67 \% & 90.34 \% & 65.33 \% & 0.2 s / GPU & M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.\\
mdab & & 83.37 \% & 93.72 \% & 73.56 \% & 22 s / 1 core & \\
MonoTAKD V2 & & 83.31 \% & 93.84 \% & 77.95 \% & 0.1 s / 1 core & \\
MonoLTKD & & 83.31 \% & 93.84 \% & 77.95 \% & 0.04 s / 1 core & \\
MonoTAKD & & 83.31 \% & 93.84 \% & 77.95 \% & 0.1 s / 1 core & \\
MonoLTKD\_V3 & & 83.31 \% & 93.84 \% & 77.95 \% & 0.04 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
Faster R-CNN & & 83.16 \% & 88.97 \% & 72.62 \% & 2 s / GPU & S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.\\
SGM3D & & 83.05 \% & 93.66 \% & 73.35 \% & 0.03 s / 1 core & Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.\\
Pseudo-LiDAR++ & st & 82.90 \% & 94.46 \% & 75.45 \% & 0.4 s / GPU & Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.\\
Disp R-CNN & st & 82.86 \% & 93.64 \% & 68.33 \% & 0.387 s / GPU & J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.\\
BS3D & & 82.72 \% & 95.35 \% & 70.01 \% & 22 ms / & N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.\\
Disp R-CNN (velo) & st & 82.64 \% & 93.45 \% & 70.45 \% & 0.387 s / GPU & J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.\\
HomoLoss(imvoxelnet) & & 82.54 \% & 92.81 \% & 72.80 \% & 0.20 s / 1 core & J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.\\
YOLOStereo3D & st & 82.15 \% & 94.81 \% & 62.17 \% & 0.1 s / & Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.\\
Ground-Aware & & 82.05 \% & 92.33 \% & 62.08 \% & 0.05 s / 1 core & Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.\\
FRCNN+Or & & 82.00 \% & 92.91 \% & 68.79 \% & 0.09 s / & C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.\\
DDMP-3D & & 81.70 \% & 91.15 \% & 63.12 \% & 0.18 s / 1 core & L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.\\
BKDStereo3D w/o KD & & 81.50 \% & 94.56 \% & 61.64 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
A3DODWTDA (image) & & 81.25 \% & 78.96 \% & 70.56 \% & 0.8 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
RefineNet & & 81.01 \% & 91.91 \% & 65.67 \% & 0.20 s / GPU & R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016.R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.\\
MonoTRKDv2 & & 80.76 \% & 93.78 \% & 75.36 \% & 40 s / 1 core & \\
CaDDN & & 80.73 \% & 93.61 \% & 71.09 \% & 0.63 s / GPU & C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.\\
ESGN & st & 80.58 \% & 93.07 \% & 70.68 \% & 0.06 s / GPU & A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.\\
PGD-FCOS3D & & 80.58 \% & 92.04 \% & 69.67 \% & 0.03 s / 1 core & T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.\\
GrooMeD-NMS & & 80.28 \% & 90.14 \% & 63.78 \% & 0.12 s / 1 core & A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.\\
3D-GCK & & 80.19 \% & 89.55 \% & 68.08 \% & 24 ms / & N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.\\
YoloMono3D & & 79.63 \% & 92.37 \% & 59.69 \% & 0.05 s / GPU & Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.\\
A3DODWTDA & la & 79.15 \% & 82.98 \% & 68.30 \% & 0.08 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
ImVoxelNet & & 79.09 \% & 89.80 \% & 69.45 \% & 0.2 s / GPU & D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.\\
DFR-Net & & 78.81 \% & 90.13 \% & 60.40 \% & 0.18 s / & Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.\\
spLBP & & 78.66 \% & 81.66 \% & 61.69 \% & 1.5 s / 8 cores & Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.\\
FMF-occlusion-net & & 78.21 \% & 92.33 \% & 61.58 \% & 0.16 s / 1 core & H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.\\
3D-SSMFCNN & & 78.19 \% & 77.92 \% & 69.19 \% & 0.1 s / GPU & L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.\\
SST [st] & st & 78.01 \% & 90.78 \% & 70.97 \% & 1 s / 1 core & \\
MonoGRNet & & 77.94 \% & 88.65 \% & 63.31 \% & 0.04s / & Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.\\
Aug3D-RPN & & 77.88 \% & 85.57 \% & 61.16 \% & 0.08 s / 1 core & C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.\\
AutoShape & & 77.66 \% & 86.51 \% & 64.40 \% & 0.04 s / 1 core & Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.\\
Reinspect & & 77.48 \% & 90.27 \% & 66.73 \% & 2s / 1 core & R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.\\
multi-task CNN & & 77.18 \% & 86.12 \% & 68.09 \% & 25.1 ms / GPU & M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.\\
Regionlets & & 76.99 \% & 88.75 \% & 60.49 \% & 1 s / >8 cores & X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.\\
3DVP & & 76.98 \% & 84.95 \% & 65.78 \% & 40 s / 8 cores & Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.\\
Mobile Stereo R-CNN & st & 76.73 \% & 90.08 \% & 62.23 \% & 1.8 s / & M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.\\
monodetrnext-f & & 76.64 \% & 89.19 \% & 69.75 \% & 0.03 s / GPU & \\
SubCat & & 76.36 \% & 84.10 \% & 60.56 \% & 0.7 s / 6 cores & E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.\\
GS3D & & 76.35 \% & 86.23 \% & 62.67 \% & 2 s / 1 core & B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
AOG & & 76.24 \% & 86.08 \% & 61.51 \% & 3 s / 4 cores & T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016.B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.\\
monodetrnext-a & & 76.08 \% & 88.93 \% & 69.50 \% & 0.04 s / 1 core & \\
Pose-RCNN & & 75.83 \% & 89.59 \% & 64.06 \% & 2 s / >8 cores & M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.\\
Plane-Constraints & & 75.43 \% & 82.54 \% & 66.82 \% & 0.05 s / 4 cores & H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.\\
3D FCN & la & 74.65 \% & 86.74 \% & 67.85 \% & >5 s / 1 core & B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.\\
OC Stereo & st & 74.60 \% & 87.39 \% & 62.56 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
mdab & & 73.55 \% & 91.06 \% & 63.82 \% & 0.02 s / 1 core & \\
Kinematic3D & & 71.73 \% & 89.67 \% & 54.97 \% & 0.12 s / 1 core & G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .\\
SeSame-point w/score & & 71.56 \% & 88.90 \% & 61.60 \% & N/A s / GPU & \\
AOG-View & & 71.26 \% & 85.01 \% & 55.73 \% & 3 s / 1 core & B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.\\
GAC3D & & 70.73 \% & 83.30 \% & 52.23 \% & 0.25 s / 1 core & M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.\\
MV-RGBD-RF & la & 70.70 \% & 77.89 \% & 57.41 \% & 4 s / 4 cores & A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.\\
Vote3Deep & la & 70.30 \% & 78.95 \% & 63.12 \% & 1.5 s / 4 cores & M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.\\
ROI-10D & & 70.16 \% & 76.56 \% & 61.15 \% & 0.2 s / GPU & F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.\\
BirdNet+ (legacy) & la & 68.05 \% & 92.10 \% & 65.61 \% & 0.1 s / & A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.\\
Decoupled-3D & & 67.92 \% & 87.78 \% & 54.53 \% & 0.08 s / GPU & Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.\\
SparVox3D & & 67.88 \% & 83.76 \% & 52.56 \% & 0.05 s / GPU & E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.\\
Pseudo-Lidar & st & 67.79 \% & 85.40 \% & 58.50 \% & 0.4 s / GPU & Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
OC-DPM & & 67.06 \% & 79.07 \% & 52.61 \% & 10 s / 8 cores & B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.\\
DPM-VOC+VP & & 66.72 \% & 82.15 \% & 49.01 \% & 8 s / 1 core & B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.\\
BdCost48LDCF & & 66.63 \% & 81.38 \% & 52.20 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
RefinedMPL & & 65.24 \% & 88.29 \% & 53.20 \% & 0.15 s / GPU & J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.\\
MDPM-un-BB & & 64.06 \% & 79.74 \% & 49.07 \% & 60 s / 4 core & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.\\
SeSame-voxel w/score & & 63.79 \% & 73.57 \% & 58.02 \% & N/A s / GPU & \\
TLNet (Stereo) & st & 63.53 \% & 76.92 \% & 54.58 \% & 0.1 s / 1 core & Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
PDV-Subcat & & 63.24 \% & 78.27 \% & 47.67 \% & 7 s / 1 core & J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.\\
MDSNet & & 62.74 \% & 85.94 \% & 50.27 \% & 0.05 s / 1 core & Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.\\
MODet & la & 62.54 \% & 66.06 \% & 60.04 \% & 0.05 s / & Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.\\
CIE + DM3D & & 61.54 \% & 79.36 \% & 53.56 \% & 0.1 s / 1 core & Ananimities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.\\
SubCat48LDCF & & 61.16 \% & 78.86 \% & 44.69 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
DPM-C8B1 & st & 60.21 \% & 75.24 \% & 44.73 \% & 15 s / 4 cores & J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.\\
SAMME48LDCF & & 58.38 \% & 77.47 \% & 44.43 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
LSVM-MDPM-sv & & 58.36 \% & 71.11 \% & 43.22 \% & 10 s / 4 cores & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.\\
BirdNet & la & 57.12 \% & 79.30 \% & 55.16 \% & 0.11 s / & J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
ACF-SC & & 56.60 \% & 69.90 \% & 43.61 \% & & C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.\\
LSVM-MDPM-us & & 55.95 \% & 68.94 \% & 41.45 \% & 10 s / 4 cores & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.\\
ACF & & 54.09 \% & 63.05 \% & 41.81 \% & 0.2 s / 1 core & P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .\\
Mono3D\_PLiDAR & & 53.36 \% & 80.85 \% & 44.80 \% & 0.1 s / & X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.\\
RT3D-GMP & st & 51.95 \% & 62.41 \% & 39.14 \% & 0.06 s / GPU & H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.\\
VeloFCN & la & 51.82 \% & 70.53 \% & 45.70 \% & 1 s / GPU & B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .\\
Vote3D & la & 45.94 \% & 54.38 \% & 40.48 \% & 0.5 s / 4 cores & D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.\\
TopNet-HighRes & la & 45.85 \% & 58.04 \% & 41.11 \% & 101ms / & S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
RT3DStereo & st & 45.81 \% & 56.53 \% & 37.63 \% & 0.08 s / GPU & H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.\\
Multimodal Detection & la & 45.46 \% & 63.91 \% & 37.25 \% & 0.06 s / GPU & A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.\\
RT3D & la & 39.69 \% & 50.33 \% & 40.04 \% & 0.09 s / GPU & Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.\\
VoxelJones & & 36.31 \% & 43.89 \% & 34.16 \% & .18 s / 1 core & M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.\\
CSoR & la & 21.66 \% & 31.52 \% & 17.99 \% & 3.5 s / 4 cores & L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.\\
mBoW & la & 21.59 \% & 35.22 \% & 16.89 \% & 10 s / 1 core & J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.\\
DepthCN & la & 21.18 \% & 37.45 \% & 16.08 \% & 2.3 s / GPU & A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.\\
YOLOv2 & & 14.31 \% & 26.74 \% & 10.94 \% & 0.02 s / GPU & J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.\\
TopNet-UncEst & la & 6.24 \% & 7.24 \% & 5.42 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
TopNet-Retina & la & 5.00 \% & 6.82 \% & 4.52 \% & 52ms / & S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
f3sd & & 0.01 \% & 0.01 \% & 0.02 \% & 1.67 s / 1 core & \\
init & & 0.01 \% & 0.01 \% & 0.01 \% & 0.03 s / 1 core & \\
TopNet-DecayRate & la & 0.01 \% & 0.00 \% & 0.01 \% & 92 ms / & S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
LaserNet & & 0.00 \% & 0.00 \% & 0.00 \% & 12 ms / GPU & G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
DA3D+KM3D+v2-99 & & 0.00 \% & 0.00 \% & 0.00 \% & 0.120s / GPU & Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.\\
Neighbor-Vote & & 0.00 \% & 0.00 \% & 0.00 \% & 0.1 s / GPU & X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.\\
mdab & & 0.00 \% & 0.00 \% & 0.00 \% & 0.02 s / 1 core & \\
DA3D+KM3D & & 0.00 \% & 0.00 \% & 0.00 \% & 0.02 s / GPU & Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.\\
DA3D & & 0.00 \% & 0.00 \% & 0.00 \% & 0.03 s / 1 core & Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
\end{tabular}