\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
VirConv-S & & 87.20 \% & 92.48 \% & 82.45 \% & 0.09 s / 1 core & H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.\\
UDeerPEP & & 86.72 \% & 91.77 \% & 82.57 \% & 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.\\
VirConv-T & & 86.25 \% & 92.54 \% & 81.24 \% & 0.09 s / 1 core & H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.\\
MPCF & & 85.50 \% & 92.46 \% & 80.69 \% & 0.08 s / 1 core & \\
TSSTDet & & 85.47 \% & 91.84 \% & 80.65 \% & 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.\\
3ONet & & 85.47 \% & 92.03 \% & 78.64 \% & 0.1 s / 1 core & H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.\\
TED & & 85.28 \% & 91.61 \% & 80.68 \% & 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.\\
MB3D & & 85.24 \% & 91.43 \% & 80.28 \% & 0.09 s / 1 core & \\
PVFusion & & 85.07 \% & 90.98 \% & 80.16 \% & 0.01 s / 1 core & \\
LoGoNet & & 85.06 \% & 91.80 \% & 80.74 \% & 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.\\
ANM & & 84.92 \% & 91.46 \% & 81.87 \% & ANM / & \\
LVP(84.92) & & 84.92 \% & 91.37 \% & 80.07 \% & 0.04 s / 1 core & \\
CDF & & 84.87 \% & 91.02 \% & 79.41 \% & 0.08 s / 1 core & \\
MM-UniMODE & & 84.81 \% & 91.23 \% & 81.44 \% & 0.04 s / 1 core & \\
SFD & & 84.76 \% & 91.73 \% & 77.92 \% & 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.\\
ACFNet & & 84.67 \% & 90.80 \% & 80.14 \% & 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.\\
FEIF3D & la & 84.56 \% & 91.27 \% & 80.05 \% & 0.1 s / GPU & \\
OGMMDet & & 84.51 \% & 91.82 \% & 79.80 \% & 0.01 s / 1 core & \\
HAF-PVP\_test & & 84.50 \% & 91.53 \% & 77.85 \% & 0.09 s / 1 core & \\
Anonymous & & 84.40 \% & 91.31 \% & 80.04 \% & 0.1 s / 1 core & \\
SSLFusion & & 84.38 \% & 91.43 \% & 80.04 \% & 0.5 s / 1 core & \\
TED-S Reproduced & & 84.29 \% & 91.62 \% & 80.00 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
3D HANet & & 84.18 \% & 90.79 \% & 77.57 \% & 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.\\
CasA++ & & 84.04 \% & 90.68 \% & 79.69 \% & 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 & & 83.99 \% & 90.75 \% & 79.63 \% & 0.09 s / 1 core & \\
L-AUG & & 83.84 \% & 90.53 \% & 79.10 \% & 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.\\
MLFusion-VS & & 83.71 \% & 91.12 \% & 79.74 \% & 0.06 s / 1 core & \\
HS-fusion & & 83.42 \% & 89.12 \% & 78.60 \% & - s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
URFormer & & 83.40 \% & 89.64 \% & 78.62 \% & 0.1 s / 1 core & \\
SFA-GCL & & 83.32 \% & 92.12 \% & 78.07 \% & 0.04 s / 1 core & \\
LFT & & 83.32 \% & 91.80 \% & 78.29 \% & 0.1s / 1 core & \\
SFA-GCL(80) & & 83.29 \% & 91.96 \% & 78.05 \% & 0.04 s / 1 core & \\
MSIT-Det & & 83.27 \% & 92.11 \% & 73.81 \% & 0.06 s / 1 core & \\
GraR-VoI & & 83.27 \% & 91.89 \% & 77.78 \% & 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.\\
GLENet-VR & & 83.23 \% & 91.67 \% & 78.43 \% & 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.\\
VPFNet & & 83.21 \% & 91.02 \% & 78.20 \% & 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.\\
GraR-Po & & 83.18 \% & 91.79 \% & 77.98 \% & 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.\\
CasA & & 83.06 \% & 91.58 \% & 80.08 \% & 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.\\
SFA-GCL(80, k=4) & & 83.05 \% & 91.91 \% & 77.84 \% & 0.04 s / 1 core & \\
UPIDet & & 82.97 \% & 89.13 \% & 80.05 \% & 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.\\
Anonymous & & 82.93 \% & 91.31 \% & 78.00 \% & 0.04 s / 1 core & \\
MLF-DET & & 82.89 \% & 91.18 \% & 77.89 \% & 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.\\
BtcDet & la & 82.86 \% & 90.64 \% & 78.09 \% & 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.\\
VPA & & 82.78 \% & 91.62 \% & 77.97 \% & 0.01 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
GraR-Vo & & 82.77 \% & 91.29 \% & 77.20 \% & 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.\\
SPG\_mini & la & 82.66 \% & 90.64 \% & 77.91 \% & 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.\\
OcTr & & 82.64 \% & 90.88 \% & 77.77 \% & 0.06 s / GPU & C. Zhou, Y. Zhang, J. Chen and D. Huang: OcTr: Octree-based Transformer for 3D Object Detection. CVPR 2023.\\
MAK\_VOXEL\_RCNN & & 82.62 \% & 91.29 \% & 77.93 \% & 0.03 s / 1 core & \\
DiffCandiDet & & 82.59 \% & 91.18 \% & 77.64 \% & 0.06 s / GPU & \\
PA3DNet & & 82.57 \% & 90.49 \% & 77.88 \% & 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.\\
SE-SSD & la & 82.54 \% & 91.49 \% & 77.15 \% & 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.\\
IIOU & & 82.51 \% & 88.83 \% & 75.82 \% & 0.1 s / GPU & \\
MAK & & 82.50 \% & 88.97 \% & 77.81 \% & 0.03 s / GPU & \\
DVF-V & & 82.45 \% & 89.40 \% & 77.56 \% & 0.1 s / 1 core & A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.\\
GraR-Pi & & 82.42 \% & 90.94 \% & 77.00 \% & 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.\\
SFA-GCL(baseline) & & 82.40 \% & 91.57 \% & 75.45 \% & 0.04 s / 1 core & \\
DVF-PV & & 82.40 \% & 90.99 \% & 77.37 \% & 0.1 s / 1 core & A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.\\
3D Dual-Fusion & & 82.40 \% & 91.01 \% & 79.39 \% & 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.\\
SFA-GCL & & 82.38 \% & 91.56 \% & 75.41 \% & 0.04 s / 1 core & \\
SCNet3D & & 82.35 \% & 89.16 \% & 77.72 \% & 0.08 s / 1 core & \\
HDet3D & & 82.33 \% & 89.93 \% & 77.20 \% & 0.07 s / >8 cores & \\
RDIoU & & 82.30 \% & 90.65 \% & 77.26 \% & 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.\\
PVT-SSD & & 82.29 \% & 90.65 \% & 76.85 \% & 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.\\
Focals Conv & & 82.28 \% & 90.55 \% & 77.59 \% & 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.\\
SFA-GCL\_dataaug & & 82.28 \% & 89.57 \% & 75.35 \% & 0.04 s / 1 core & \\
CLOCs & & 82.28 \% & 89.16 \% & 77.23 \% & 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.\\
GraphAlign(ICCV2023) & & 82.23 \% & 90.90 \% & 79.67 \% & 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.\\
DEF-Model & & 82.19 \% & 88.49 \% & 77.40 \% & 0.03 s / 1 core & \\
spark & & 82.18 \% & 90.66 \% & 77.44 \% & 0.1 s / 1 core & \\
DGEnhCL & & 82.18 \% & 91.12 \% & 75.29 \% & 0.04 s / 1 core & \\
SASA & la & 82.16 \% & 88.76 \% & 77.16 \% & 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.\\
spark\_voxel\_rcnn & & 82.15 \% & 90.62 \% & 77.40 \% & 0.04 s / 1 core & \\
PG-RCNN & & 82.13 \% & 89.38 \% & 77.33 \% & 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.\\
SPG & la & 82.13 \% & 90.50 \% & 78.90 \% & 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.\\
SDGUFusion & & 82.12 \% & 91.03 \% & 77.67 \% & 0.5 s / 1 core & \\
voxel\_spark & & 82.10 \% & 90.47 \% & 79.01 \% & 0.04 s / GPU & \\
VoTr-TSD & & 82.09 \% & 89.90 \% & 79.14 \% & 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.\\
Voxel\_Spark\_focal\_we & & 82.08 \% & 90.65 \% & 77.36 \% & 0.08 s / 1 core & \\
Pyramid R-CNN & & 82.08 \% & 88.39 \% & 77.49 \% & 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.\\
VoxSeT & & 82.06 \% & 88.53 \% & 77.46 \% & 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.\\
c2f & & 82.05 \% & 89.69 \% & 79.05 \% & 1 s / 1 core & \\
DDF & & 82.03 \% & 89.69 \% & 79.47 \% & 0.1 s / 1 core & \\
LGNet-Car & & 82.02 \% & 90.65 \% & 77.34 \% & 0.11 s / 1 core & \\
PSMS-Net & la & 82.02 \% & 90.88 \% & 77.37 \% & 0.1 s / 1 core & \\
EQ-PVRCNN & & 82.01 \% & 90.13 \% & 77.53 \% & 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.\\
BPG3D & & 81.98 \% & 90.52 \% & 78.97 \% & 0.05 s / 1 core & \\
voxel-rcnn+++ & & 81.97 \% & 90.59 \% & 77.13 \% & 0.08 s / GPU & \\
EPNet++ & & 81.96 \% & 91.37 \% & 76.71 \% & 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.\\
USVLab BSAODet & & 81.95 \% & 88.66 \% & 77.40 \% & 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.\\
NIV-SSD & & 81.95 \% & 90.98 \% & 76.83 \% & 0.03 s / 1 core & \\
Spark\_partA22 & & 81.94 \% & 90.24 \% & 76.95 \% & 10 s / 1 core & \\
HMFI & & 81.93 \% & 88.90 \% & 77.30 \% & 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.\\
focalnet & & 81.92 \% & 90.59 \% & 79.25 \% & 0.05 s / 1 core & \\
focalnet & & 81.92 \% & 90.57 \% & 79.24 \% & 0.05 s / 1 core & \\
RagNet3D & & 81.91 \% & 88.74 \% & 77.45 \% & 0.05 s / 1 core & \\
AMVFNet & & 81.90 \% & 90.52 \% & 77.42 \% & 0.04 s / GPU & \\
spark2 & & 81.88 \% & 88.61 \% & 77.19 \% & 0.1 s / 1 core & \\
PDV & & 81.86 \% & 90.43 \% & 77.36 \% & 0.1 s / 1 core & J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.\\
SQD & & 81.82 \% & 91.58 \% & 79.07 \% & 0.06 s / 1 core & \\
Spark\_PartA2\_Soft\_fo & & 81.82 \% & 90.10 \% & 78.35 \% & 0.1 s / 1 core & \\
af & & 81.78 \% & 90.46 \% & 77.37 \% & 1 s / GPU & \\
CityBrainLab-CT3D & & 81.77 \% & 87.83 \% & 77.16 \% & 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.\\
M3DeTR & & 81.73 \% & 90.28 \% & 76.96 \% & 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.\\
SIENet & & 81.71 \% & 88.22 \% & 77.22 \% & 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.\\
FIRM-Net & & 81.65 \% & 88.25 \% & 76.98 \% & 0.07 s / 1 core & \\
Voxel R-CNN & & 81.62 \% & 90.90 \% & 77.06 \% & 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.\\
BADet & & 81.61 \% & 89.28 \% & 76.58 \% & 0.14 s / 1 core & R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.\\
FromVoxelToPoint & & 81.58 \% & 88.53 \% & 77.37 \% & 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.\\
LGNet-3classes & & 81.57 \% & 90.84 \% & 76.98 \% & 0.11 s / 1 core & \\
H^23D R-CNN & & 81.55 \% & 90.43 \% & 77.22 \% & 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.\\
test & & 81.55 \% & 88.47 \% & 76.51 \% & 0.1 s / 1 core & \\
FARP-Net & & 81.53 \% & 88.36 \% & 78.98 \% & 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.\\
VoxelFSD & & 81.50 \% & 89.89 \% & 76.82 \% & 0.08 s / 1 core & \\
PR-SSD & & 81.49 \% & 89.69 \% & 76.71 \% & 0.02 s / GPU & \\
spark-part2 & & 81.49 \% & 89.82 \% & 76.76 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
DSA-PV-RCNN & la & 81.46 \% & 88.25 \% & 76.96 \% & 0.08 s / 1 core & P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.\\
focal & & 81.45 \% & 90.73 \% & 77.13 \% & 100 s / 1 core & \\
P2V-RCNN & & 81.45 \% & 88.34 \% & 77.20 \% & 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.\\
MMLab PV-RCNN & la & 81.43 \% & 90.25 \% & 76.82 \% & 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.\\
CZY\_PPF\_Net & & 81.39 \% & 90.44 \% & 77.02 \% & 0.1 s / 1 core & \\
XView & & 81.35 \% & 89.21 \% & 76.87 \% & 0.1 s / 1 core & L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.\\
GeVo & & 81.35 \% & 89.66 \% & 76.82 \% & 0.05 s / 1 core & \\
ECA & & 81.34 \% & 88.88 \% & 78.68 \% & 0.08 s / GPU & \\
RangeRCNN & la & 81.33 \% & 88.47 \% & 77.09 \% & 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.\\
CAT-Det & & 81.32 \% & 89.87 \% & 76.68 \% & 0.3 s / GPU & Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.\\
PV-RCNN-Plus & & 81.29 \% & 87.72 \% & 76.78 \% & 1 s / 1 core & \\
PASS-PV-RCNN-Plus & & 81.28 \% & 87.65 \% & 76.79 \% & 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.\\
SP\_SECOND\_IOU & & 81.25 \% & 89.50 \% & 76.69 \% & 0.04 s / 1 core & \\
GF-pointnet & & 81.23 \% & 88.23 \% & 76.53 \% & 0.02 s / 1 core & \\
MFB3D & & 81.11 \% & 90.57 \% & 76.62 \% & 0.14 s / 1 core & \\
HA-PillarNet & & 81.06 \% & 89.65 \% & 76.67 \% & 0.05 s / 1 core & \\
U\_PV\_V2\_ep\_100\_100 & & 80.97 \% & 87.41 \% & 76.58 \% & 0.1 s / 1 core & \\
VPFNet & & 80.97 \% & 88.51 \% & 76.74 \% & 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.\\
CG-SSD & & 80.97 \% & 87.87 \% & 76.54 \% & 0.01 s / 1 core & \\
RBEV-Voxel & & 80.87 \% & 87.15 \% & 76.28 \% & 0.08 s / GPU & \\
Sem-Aug & la & 80.77 \% & 89.41 \% & 75.90 \% & 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.\\
U\_PV\_V2\_ep100\_80 & & 80.75 \% & 87.16 \% & 76.43 \% & 0... s / 1 core & \\
StructuralIF & & 80.69 \% & 87.15 \% & 76.26 \% & 0.02 s / 8 cores & J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.\\
CLOCs\_PVCas & & 80.67 \% & 88.94 \% & 77.15 \% & 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.\\
SVGA-Net & & 80.47 \% & 87.33 \% & 75.91 \% & 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.\\
KPTr & & 80.40 \% & 88.52 \% & 75.28 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SRDL & & 80.38 \% & 87.73 \% & 76.27 \% & 0.05 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
Fast-CLOCs & & 80.35 \% & 89.10 \% & 76.99 \% & 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.\\
SPANet & & 80.34 \% & 91.05 \% & 74.89 \% & 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.\\
IA-SSD (single) & & 80.32 \% & 88.87 \% & 75.10 \% & 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.\\
GSG-FPS & & 80.29 \% & 88.56 \% & 75.16 \% & 0.01 s / 1 core & \\
CIA-SSD & la & 80.28 \% & 89.59 \% & 72.87 \% & 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.\\
Test\_dif & & 80.17 \% & 88.68 \% & 75.12 \% & 0.01 s / 1 core & \\
IA-SSD (multi) & & 80.13 \% & 88.34 \% & 75.04 \% & 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.\\
EBM3DOD & & 80.12 \% & 91.05 \% & 72.78 \% & 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.\\
3D-CVF at SPA & la & 80.05 \% & 89.20 \% & 73.11 \% & 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.\\
IOUFusion & & 80.00 \% & 87.72 \% & 75.00 \% & 0.1 s / GPU & \\
bs & & 79.95 \% & 90.52 \% & 76.86 \% & 0.1 s / 1 core & \\
spark\_second & & 79.93 \% & 86.66 \% & 74.93 \% & . s / 1 core & \\
SIF & & 79.88 \% & 86.84 \% & 75.89 \% & 0.1 s / 1 core & P. An: SIF. Submitted to CVIU 2021.\\
spark\_second\_focal\_w & & 79.81 \% & 86.41 \% & 75.03 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
RAFDet & & 79.81 \% & 88.24 \% & 75.06 \% & 0.01 s / 1 core & \\
RangeIoUDet & la & 79.80 \% & 88.60 \% & 76.76 \% & 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.\\
SA-SSD & & 79.79 \% & 88.75 \% & 74.16 \% & 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.\\
MMAE & la & 79.75 \% & 88.07 \% & 74.41 \% & 0.07 s / 1 core & \\
STD & & 79.71 \% & 87.95 \% & 75.09 \% & 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.\\
OFFNet & & 79.68 \% & 85.81 \% & 75.41 \% & 0.1 s / GPU & \\
MGAF-3DSSD & & 79.68 \% & 88.16 \% & 72.39 \% & 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.\\
RAFDet & & 79.66 \% & 88.17 \% & 74.75 \% & 0.1 s / 1 core & \\
Struc info fusion II & & 79.59 \% & 88.97 \% & 72.51 \% & 0.05 s / GPU & P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.\\
3DSSD & & 79.57 \% & 88.36 \% & 74.55 \% & 0.04 s / GPU & Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.\\
EBM3DOD baseline & & 79.52 \% & 88.80 \% & 72.30 \% & 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.\\
Struc info fusion I & & 79.49 \% & 88.70 \% & 74.25 \% & 0.05 s / 1 core & P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.\\
PartA2\_basline & & 79.48 \% & 88.66 \% & 76.67 \% & 0.09 s / 1 core & \\
Point-GNN & la & 79.47 \% & 88.33 \% & 72.29 \% & 0.6 s / GPU & W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.\\
spark\_second2 & & 79.45 \% & 86.28 \% & 74.71 \% & 10 s / 1 core & \\
sec\_spark & & 79.44 \% & 86.08 \% & 74.70 \% & 0.03 s / GPU & \\
RAFDet & & 79.41 \% & 87.40 \% & 74.61 \% & 0.01 s / 1 core & \\
DFAF3D & & 79.37 \% & 88.59 \% & 72.21 \% & 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.\\
SSL-PointGNN & & 79.36 \% & 87.78 \% & 74.15 \% & 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.\\
PUDet & & 79.34 \% & 87.85 \% & 74.58 \% & 0.3 s / GPU & \\
EPNet & & 79.28 \% & 89.81 \% & 74.59 \% & 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.\\
second\_iou\_baseline & & 79.20 \% & 88.08 \% & 75.91 \% & 0.05 s / 1 core & \\
DVFENet & & 79.18 \% & 86.20 \% & 74.58 \% & 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.\\
AAMVFNet & & 79.09 \% & 88.01 \% & 76.43 \% & 0.04 s / GPU & \\
Faraway-Frustum & la & 79.05 \% & 87.45 \% & 76.14 \% & 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.\\
GD-MAE & & 79.03 \% & 88.14 \% & 73.55 \% & 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.\\
3D IoU-Net & & 79.03 \% & 87.96 \% & 72.78 \% & 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.\\
SERCNN & la & 78.96 \% & 87.74 \% & 74.30 \% & 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 & & 78.94 \% & 85.85 \% & 74.28 \% & 0.03 s / 1 core & \\
ACDet & & 78.85 \% & 88.47 \% & 73.86 \% & 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.\\
MG & & 78.72 \% & 87.68 \% & 72.00 \% & 0.1 s / 1 core & \\
MVAF-Net & & 78.71 \% & 87.87 \% & 75.48 \% & 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.\\
Res3DNet & & 78.54 \% & 87.22 \% & 74.36 \% & 0.05 s / GPU & \\
MMLab-PartA^2 & la & 78.49 \% & 87.81 \% & 73.51 \% & 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.\\
CLOCs\_SecCas & & 78.45 \% & 86.38 \% & 72.45 \% & 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.\\
Patches - EMP & la & 78.41 \% & 89.84 \% & 73.15 \% & 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.\\
HotSpotNet & & 78.31 \% & 87.60 \% & 73.34 \% & 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.\\
Sem-Aug-PointRCNN++ & & 78.06 \% & 86.69 \% & 73.85 \% & 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.\\
CenterNet3D & & 77.90 \% & 86.20 \% & 73.03 \% & 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.\\
spark\_pointpillar & & 77.82 \% & 87.59 \% & 73.94 \% & 0.02 s / GPU & \\
pointpillars\_spark & & 77.75 \% & 87.55 \% & 73.63 \% & 0.02 s / GPU & \\
VoxelFSD-S & & 77.67 \% & 86.29 \% & 72.18 \% & 0.05 s / 1 core & \\
pointpillar\_spark\_fo & & 77.66 \% & 85.99 \% & 72.51 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
spark\_pointpillar2 & & 77.57 \% & 85.96 \% & 72.29 \% & 10 s / 1 core & \\
SC-SSD & & 77.52 \% & 85.51 \% & 74.02 \% & 1 s / 1 core & \\
U\_second\_v4\_ep\_100\_8 & & 77.45 \% & 85.20 \% & 73.84 \% & 0.1 s / 1 core & \\
UberATG-MMF & la & 77.43 \% & 88.40 \% & 70.22 \% & 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.\\
Associate-3Ddet & & 77.40 \% & 85.99 \% & 70.53 \% & 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.\\
Fast Point R-CNN & la & 77.40 \% & 85.29 \% & 70.24 \% & 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.\\
RangeDet (Official) & & 77.36 \% & 85.41 \% & 72.60 \% & 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.\\
Patches & la & 77.20 \% & 88.67 \% & 71.82 \% & 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.\\
u\_second\_v4\_epoch\_10 & & 76.99 \% & 84.23 \% & 73.75 \% & 0.1 s / 1 core & \\
SeSame-point & & 76.83 \% & 85.25 \% & 71.60 \% & N/A s / TITAN RTX & \\
HRI-VoxelFPN & & 76.70 \% & 85.64 \% & 69.44 \% & 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.\\
SARPNET & & 76.64 \% & 85.63 \% & 71.31 \% & 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.\\
IIOU\_LDR & & 76.51 \% & 86.95 \% & 71.53 \% & 0.03 s / 1 core & \\
3D IoU Loss & la & 76.50 \% & 86.16 \% & 71.39 \% & 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.\\
TF-PartA2 & & 76.39 \% & 86.65 \% & 71.67 \% & 0.1 s / 1 core & \\
F-ConvNet & la & 76.39 \% & 87.36 \% & 66.69 \% & 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.\\
pointpillar\_baseline & & 76.37 \% & 85.29 \% & 71.03 \% & 0.01 s / 1 core & \\
ROT\_S3D & & 76.35 \% & 86.56 \% & 71.51 \% & 0.1 s / GPU & \\
BAPartA2S-4h & & 76.31 \% & 86.97 \% & 73.03 \% & 0.1 s / 1 core & \\
VSAC & & 76.29 \% & 85.06 \% & 71.65 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
LVFSD & & 76.14 \% & 84.18 \% & 71.55 \% & 0.06 s / & ERROR: Wrong syntax in BIBTEX file.\\
SegVoxelNet & & 76.13 \% & 86.04 \% & 70.76 \% & 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.\\
centerpoint\_pcdet & & 76.12 \% & 83.47 \% & 71.17 \% & 0.06 s / 1 core & \\
mm3d\_PartA2 & & 76.09 \% & 86.82 \% & 72.74 \% & 0.1 s / GPU & \\
S-AT GCN & & 76.04 \% & 83.20 \% & 71.17 \% & 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.\\
prcnn\_v18\_80\_100 & & 76.03 \% & 84.37 \% & 71.44 \% & 0.1 s / 1 core & \\
TANet & & 75.94 \% & 84.39 \% & 68.82 \% & 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.\\
MMpointpillars & & 75.75 \% & 85.86 \% & 70.65 \% & 0.05 s / 1 core & \\
SFEBEV & & 75.74 \% & 86.08 \% & 70.59 \% & 0.01 s / 1 core & \\
PointRGCN & & 75.73 \% & 85.97 \% & 70.60 \% & 0.26 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
MMLab-PointRCNN & la & 75.64 \% & 86.96 \% & 70.70 \% & 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.\\
voxelnext\_pcdet & & 75.58 \% & 83.88 \% & 70.77 \% & 0.05 s / 1 core & \\
MEDL-U & & 75.56 \% & 85.43 \% & 68.79 \% & 1 s / GPU & \\
XT-PartA2 & & 75.56 \% & 85.54 \% & 71.02 \% & 0.1 s / GPU & \\
AB3DMOT & la on & 75.43 \% & 86.10 \% & 68.88 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
CAT2 & & 75.33 \% & 84.84 \% & 70.07 \% & 1 s / 1 core & \\
R-GCN & & 75.26 \% & 83.42 \% & 68.73 \% & 0.16 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
epBRM & la & 75.15 \% & 85.00 \% & 69.84 \% & 0.1 s / GPU & K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.\\
SeSame-voxel & & 75.05 \% & 81.51 \% & 70.53 \% & N/A s / TITAN RTX & \\
MAFF-Net(DAF-Pillar) & & 75.04 \% & 85.52 \% & 67.61 \% & 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.\\
HA PillarNet & & 74.89 \% & 83.83 \% & 70.11 \% & 0.05 s / 1 core & \\
PASS-PointPillar & & 74.85 \% & 84.72 \% & 69.05 \% & 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.\\
PI-RCNN & & 74.82 \% & 84.37 \% & 70.03 \% & 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.\\
P2P & & 74.76 \% & 85.47 \% & 67.96 \% & 0.1 s / GPU & \\
WA & & 74.59 \% & 84.80 \% & 67.27 \% & 0.3 s / GPU & \\
mmFUSION & & 74.38 \% & 85.24 \% & 69.43 \% & 1s / 1 core & J. Ahmad and A. Del Bue: mmFUSION: Multimodal Fusion for 3D Objects Detection. arXiv preprint arXiv:2311.04058 2023.\\
MMpp & & 74.34 \% & 83.64 \% & 68.02 \% & 0.05 s / 1 core & \\
PointPillars & la & 74.31 \% & 82.58 \% & 68.99 \% & 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.\\
HINTED & & 74.13 \% & 84.00 \% & 67.03 \% & 0.04 s / 1 core & \\
ARPNET & & 74.04 \% & 84.69 \% & 68.64 \% & 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.\\
Harmonic PointPillar & & 73.96 \% & 82.26 \% & 69.21 \% & 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.\\
SeSame-pillar & & 73.85 \% & 83.88 \% & 68.65 \% & N/A s / TITAN RTX & \\
PC-CNN-V2 & la & 73.79 \% & 85.57 \% & 65.65 \% & 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.\\
C-GCN & & 73.62 \% & 83.49 \% & 67.01 \% & 0.147 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
3DBN & la & 73.53 \% & 83.77 \% & 66.23 \% & 0.13s / & X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.\\
PointRGBNet & & 73.49 \% & 83.99 \% & 68.56 \% & 0.08 s / 4 cores & P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.\\
SCNet & la & 73.17 \% & 83.34 \% & 67.93 \% & 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.\\
SeSame-pillar w/scor & & 73.15 \% & 82.32 \% & 66.64 \% & N/A s / 1 core & \\
PFF3D & la & 72.93 \% & 81.11 \% & 67.24 \% & 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.\\
MM\_SECOND & & 72.68 \% & 82.02 \% & 66.27 \% & 0.05 s / GPU & \\
DASS & & 72.31 \% & 81.85 \% & 65.99 \% & 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.\\
AVOD-FPN & la & 71.76 \% & 83.07 \% & 65.73 \% & 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.\\
PointPainting & la & 71.70 \% & 82.11 \% & 67.08 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
PI-SECOND & & 71.46 \% & 81.62 \% & 66.26 \% & 0.05 s / GPU & \\
ODGS & & 70.85 \% & 78.39 \% & 64.81 \% & 0.1 s / 1 core & \\
WS3D & la & 70.59 \% & 80.99 \% & 64.23 \% & 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.\\
F-PointNet & la & 69.79 \% & 82.19 \% & 60.59 \% & 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.\\
EOTL & & 69.13 \% & 79.97 \% & 58.57 \% & 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.\\
UberATG-ContFuse & la & 68.78 \% & 83.68 \% & 61.67 \% & 0.06 s / GPU & M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.\\
MLOD & la & 67.76 \% & 77.24 \% & 62.05 \% & 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.\\
DSGN++ & st & 67.37 \% & 83.21 \% & 59.91 \% & 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.\\
DMF & st & 67.33 \% & 77.55 \% & 62.44 \% & 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.\\
AVOD & la & 66.47 \% & 76.39 \% & 60.23 \% & 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.\\
StereoDistill & & 66.39 \% & 81.66 \% & 57.39 \% & 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.\\
MMLAB LIGA-Stereo & st & 64.66 \% & 81.39 \% & 57.22 \% & 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.\\
BirdNet+ & la & 64.04 \% & 76.15 \% & 59.79 \% & 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.\\
MV3D & la & 63.63 \% & 74.97 \% & 54.00 \% & 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.\\
SNVC & st & 61.34 \% & 78.54 \% & 54.23 \% & 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.\\
RCD & & 60.56 \% & 70.54 \% & 55.58 \% & 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.\\
SeSame-point w/score & & 56.92 \% & 74.30 \% & 48.14 \% & N/A s / GPU & \\
A3DODWTDA & la & 56.82 \% & 62.84 \% & 48.12 \% & 0.08 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
PL++ (SDN+GDC) & st la & 54.88 \% & 68.38 \% & 49.16 \% & 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.\\
MV3D (LIDAR) & la & 54.54 \% & 68.35 \% & 49.16 \% & 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.\\
CDN & st & 54.22 \% & 74.52 \% & 46.36 \% & 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.\\
CG-Stereo & st & 53.58 \% & 74.39 \% & 46.50 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
DSGN & st & 52.18 \% & 73.50 \% & 45.14 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
BirdNet+ (legacy) & la & 51.85 \% & 70.14 \% & 50.03 \% & 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.\\
Complexer-YOLO & la & 47.34 \% & 55.93 \% & 42.60 \% & 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.\\
SeSame-voxel w/score & & 47.14 \% & 61.57 \% & 41.06 \% & N/A s / GPU & \\
ESGN & st & 46.39 \% & 65.80 \% & 38.42 \% & 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.\\
Disp R-CNN (velo) & st & 45.78 \% & 68.21 \% & 37.73 \% & 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.\\
CDN-PL++ & st & 44.86 \% & 64.31 \% & 38.11 \% & 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.\\
Disp R-CNN & st & 43.27 \% & 67.02 \% & 36.43 \% & 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.\\
Pseudo-LiDAR++ & st & 42.43 \% & 61.11 \% & 36.99 \% & 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.\\
YOLOStereo3D & st & 41.25 \% & 65.68 \% & 30.42 \% & 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.\\
RT3D-GMP & st & 38.76 \% & 45.79 \% & 30.00 \% & 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.\\
ZoomNet & st & 38.64 \% & 55.98 \% & 30.97 \% & 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.\\
OC Stereo & st & 37.60 \% & 55.15 \% & 30.25 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
SST [st] & st & 35.49 \% & 57.02 \% & 31.03 \% & 1 s / 1 core & \\
BKDStereo3D & & 35.23 \% & 59.38 \% & 25.24 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
Pseudo-Lidar & st & 34.05 \% & 54.53 \% & 28.25 \% & 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.\\
BKDStereo3D w/o KD & & 32.08 \% & 56.72 \% & 23.74 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
Stereo CenterNet & st & 31.30 \% & 49.94 \% & 25.62 \% & 0.04 s / GPU & Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.\\
Stereo R-CNN & st & 30.23 \% & 47.58 \% & 23.72 \% & 0.3 s / GPU & P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.\\
BirdNet & la & 27.26 \% & 40.99 \% & 25.32 \% & 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.\\
DA3D+KM3D+v2-99 & & 26.80 \% & 34.72 \% & 23.05 \% & 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.\\
CIE + DM3D & & 25.02 \% & 35.96 \% & 21.47 \% & 0.1 s / 1 core & Ananimities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.\\
monodetrnext-a & & 24.14 \% & 29.94 \% & 23.79 \% & 0.04 s / 1 core & \\
RT3DStereo & st & 23.28 \% & 29.90 \% & 18.96 \% & 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.\\
error & & 23.07 \% & 39.40 \% & 19.52 \% & 1 s / 1 core & \\
DA3D+KM3D & & 22.08 \% & 30.83 \% & 19.20 \% & 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.\\
MonoTRKDv2 & & 21.87 \% & 30.26 \% & 18.87 \% & 40 s / 1 core & \\
monodetrnext-f & & 21.69 \% & 27.21 \% & 21.16 \% & 0.03 s / GPU & \\
MonoTAKD V2 & & 21.26 \% & 29.86 \% & 18.27 \% & 0.1 s / 1 core & \\
CIE & & 20.95 \% & 31.55 \% & 17.83 \% & 0.1 s / 1 core & Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.\\
DA3D & & 20.47 \% & 27.76 \% & 17.89 \% & 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.\\
Sample & & 19.49 \% & 25.75 \% & 15.70 \% & 0.01 s / 1 core & \\
MonoLTKD & & 19.43 \% & 27.91 \% & 16.51 \% & 0.04 s / 1 core & \\
MonoLTKD\_V3 & & 19.42 \% & 27.91 \% & 16.51 \% & 0.04 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
MonoTAKD & & 19.42 \% & 27.91 \% & 16.51 \% & 0.1 s / 1 core & \\
MonoLSS & & 19.15 \% & 26.11 \% & 16.94 \% & 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.\\
RT3D & la & 19.14 \% & 23.74 \% & 18.86 \% & 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.\\
NeurOCS & & 18.94 \% & 29.89 \% & 15.90 \% & 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.\\
MonoLiG & & 18.86 \% & 24.90 \% & 16.79 \% & 0.03 s / 1 core & A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.\\
CMKD & & 18.69 \% & 28.55 \% & 16.77 \% & 0.1 s / 1 core & Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.\\
MonoAux-v2 & & 18.55 \% & 26.00 \% & 15.79 \% & 0.04 s / GPU & \\
Mix-Teaching & & 18.54 \% & 26.89 \% & 15.79 \% & 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.\\
StereoFENet & st & 18.41 \% & 29.14 \% & 14.20 \% & 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.\\
Occlude3D & & 18.20 \% & 23.71 \% & 15.18 \% & 0.01 s / 1 core & \\
Anonymous & & 18.19 \% & 25.48 \% & 14.27 \% & 0.1 s / 1 core & \\
SHUD & & 18.18 \% & 28.41 \% & 15.11 \% & 0.04 s / 1 core & \\
PS-SVDM & & 18.13 \% & 29.22 \% & 15.35 \% & 1 s / 1 core & Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.\\
MonoSample (DID-M3D) & & 18.05 \% & 28.63 \% & 15.19 \% & 0.2 s / 1 core & \\
TBD & & 17.97 \% & 28.50 \% & 15.03 \% & 0.04 s / 1 core & \\
LPCG-Monoflex & & 17.80 \% & 25.56 \% & 15.38 \% & 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.\\
PS-fld & & 17.74 \% & 23.74 \% & 15.14 \% & 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.\\
MonoSKD & & 17.35 \% & 28.43 \% & 15.01 \% & 0.04 s / 1 core & S. Wang and J. Zheng: MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation Coefficient. ECAI 2023.\\
MonoSTL & & 17.14 \% & 24.54 \% & 14.59 \% & na s / 1 core & \\
MonoDDE & & 17.14 \% & 24.93 \% & 15.10 \% & 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.\\
MonoCDiT & & 17.13 \% & 23.52 \% & 14.37 \% & 0.05 s / GPU & \\
MonoNeRD & & 17.13 \% & 22.75 \% & 15.63 \% & 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.\\
OPA-3D & & 17.05 \% & 24.60 \% & 14.25 \% & 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.\\
Mobile Stereo R-CNN & st & 17.04 \% & 26.97 \% & 13.26 \% & 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.\\
DD3D & & 16.87 \% & 23.19 \% & 14.36 \% & 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) .\\
ADD & & 16.81 \% & 25.61 \% & 13.79 \% & 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 .\\
MonoSGC & & 16.77 \% & 27.01 \% & 14.61 \% & 0.04 s / 1 core & \\
MonoUNI & & 16.73 \% & 24.75 \% & 13.49 \% & 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.\\
MOPNet & & 16.67 \% & 26.95 \% & 14.33 \% & 0.1 s / 1 core & \\
MonoCD & & 16.59 \% & 25.53 \% & 14.53 \% & 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.\\
FDGNet & & 16.53 \% & 27.22 \% & 13.52 \% & 0.1 s / 1 core & \\
MSFENet & & 16.49 \% & 26.30 \% & 13.55 \% & 0.1 s / 1 core & \\
BA2-Det+MonoFlex & & 16.30 \% & 23.45 \% & 13.50 \% & 0.03 s / 1 core & \\
DID-M3D & & 16.29 \% & 24.40 \% & 13.75 \% & 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.\\
MonoDETR & & 16.26 \% & 24.52 \% & 13.93 \% & 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.\\
MonoFRD & & 16.24 \% & 21.11 \% & 14.97 \% & 0.01 s / 1 core & \\
DCD & & 15.90 \% & 23.81 \% & 13.21 \% & 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.\\
MonoDTR & & 15.39 \% & 21.99 \% & 12.73 \% & 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.\\
GUPNet & & 15.02 \% & 22.26 \% & 13.12 \% & 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.\\
Cube R-CNN & & 15.01 \% & 23.59 \% & 12.56 \% & 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.\\
HomoLoss(monoflex) & & 14.94 \% & 21.75 \% & 13.07 \% & 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.\\
MonoSIM\_v2 & & 14.74 \% & 21.69 \% & 13.08 \% & 0.03 s / 1 core & \\
SGM3D & & 14.65 \% & 22.46 \% & 12.97 \% & 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.\\
MDSNet & & 14.46 \% & 24.30 \% & 11.12 \% & 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.\\
DEVIANT & & 14.46 \% & 21.88 \% & 11.89 \% & 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.\\
DLE & & 14.33 \% & 24.23 \% & 10.30 \% & 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.\\
AutoShape & & 14.17 \% & 22.47 \% & 11.36 \% & 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.\\
MonoFlex & & 13.89 \% & 19.94 \% & 12.07 \% & 0.03 s / GPU & Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.\\
MonoEF & & 13.87 \% & 21.29 \% & 11.71 \% & 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.\\
MonoRCNN++ & & 13.72 \% & 20.08 \% & 11.34 \% & 0.07 s / GPU & X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.\\
MonoSIM & & 13.72 \% & 20.24 \% & 12.29 \% & 0.16 s / 1 core & \\
DFR-Net & & 13.63 \% & 19.40 \% & 10.35 \% & 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.\\
PS-SVDM & & 13.49 \% & 20.83 \% & 11.18 \% & 1 s / 1 core & Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.\\
CaDDN & & 13.41 \% & 19.17 \% & 11.46 \% & 0.63 s / GPU & C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.\\
PCT & & 13.37 \% & 21.00 \% & 11.31 \% & 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.\\
Ground-Aware & & 13.25 \% & 21.65 \% & 9.91 \% & 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.\\
FMF-occlusion-net & & 13.12 \% & 20.28 \% & 9.56 \% & 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.\\
Aug3D-RPN & & 12.99 \% & 17.82 \% & 9.78 \% & 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.\\
HomoLoss(imvoxelnet) & & 12.99 \% & 20.10 \% & 10.50 \% & 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.\\
DDMP-3D & & 12.78 \% & 19.71 \% & 9.80 \% & 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.\\
mdab & & 12.74 \% & 18.62 \% & 11.10 \% & 22 s / 1 core & \\
Kinematic3D & & 12.72 \% & 19.07 \% & 9.17 \% & 0.12 s / 1 core & G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .\\
MonoRCNN & & 12.65 \% & 18.36 \% & 10.03 \% & 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.\\
GrooMeD-NMS & & 12.32 \% & 18.10 \% & 9.65 \% & 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.\\
MonoRUn & & 12.30 \% & 19.65 \% & 10.58 \% & 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.\\
monodle & & 12.26 \% & 17.23 \% & 10.29 \% & 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 .\\
YoloMono3D & & 12.06 \% & 18.28 \% & 8.42 \% & 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.\\
IAFA & & 12.01 \% & 17.81 \% & 10.61 \% & 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.\\
MonOAPC & & 12.00 \% & 18.77 \% & 9.75 \% & 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.\\
GAC3D & & 12.00 \% & 17.75 \% & 9.15 \% & 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.\\
CMAN & & 11.87 \% & 17.77 \% & 9.16 \% & 0.15 s / 1 core & C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.\\
PGD-FCOS3D & & 11.76 \% & 19.05 \% & 9.39 \% & 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.\\
D4LCN & & 11.72 \% & 16.65 \% & 9.51 \% & 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.\\
KM3D & & 11.45 \% & 16.73 \% & 9.92 \% & 0.03 s / 1 core & P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.\\
RefinedMPL & & 11.14 \% & 18.09 \% & 8.94 \% & 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.\\
PatchNet & & 11.12 \% & 15.68 \% & 10.17 \% & 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.\\
MonoAIU & & 11.02 \% & 15.73 \% & 8.82 \% & 0.03 s / GPU & \\
ImVoxelNet & & 10.97 \% & 17.15 \% & 9.15 \% & 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.\\
AM3D & & 10.74 \% & 16.50 \% & 9.52 \% & 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.\\
RTM3D & & 10.34 \% & 14.41 \% & 8.77 \% & 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.\\
MonoPair & & 9.99 \% & 13.04 \% & 8.65 \% & 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.\\
mdab & & 9.99 \% & 14.70 \% & 8.65 \% & 22 s / 1 core & \\
Neighbor-Vote & & 9.90 \% & 15.57 \% & 8.89 \% & 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.\\
SMOKE & & 9.76 \% & 14.03 \% & 7.84 \% & 0.03 s / GPU & Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.\\
M3D-RPN & & 9.71 \% & 14.76 \% & 7.42 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
QD-3DT & on & 9.33 \% & 12.81 \% & 7.86 \% & 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.\\
TopNet-HighRes & la & 9.28 \% & 12.67 \% & 7.95 \% & 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.\\
MonoCInIS & & 7.94 \% & 15.82 \% & 6.68 \% & 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.\\
Plane-Constraints & & 7.88 \% & 11.29 \% & 6.48 \% & 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.\\
SS3D & & 7.68 \% & 10.78 \% & 6.51 \% & 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.\\
MonoCInIS & & 7.66 \% & 15.21 \% & 6.24 \% & 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.\\
Mono3D\_PLiDAR & & 7.50 \% & 10.76 \% & 6.10 \% & 0.1 s / & X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.\\
mdab & & 7.47 \% & 11.55 \% & 6.27 \% & 0.02 s / 1 core & \\
MonoPSR & & 7.25 \% & 10.76 \% & 5.85 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
Decoupled-3D & & 7.02 \% & 11.08 \% & 5.63 \% & 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.\\
mdab & & 6.94 \% & 10.52 \% & 5.18 \% & 0.02 s / 1 core & \\
VoxelJones & & 6.35 \% & 7.39 \% & 5.80 \% & .18 s / 1 core & M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.\\
MonoGRNet & & 5.74 \% & 9.61 \% & 4.25 \% & 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.\\
A3DODWTDA (image) & & 5.27 \% & 6.88 \% & 4.45 \% & 0.8 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
MonoFENet & & 5.14 \% & 8.35 \% & 4.10 \% & 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.\\
TLNet (Stereo) & st & 4.37 \% & 7.64 \% & 3.74 \% & 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.\\
CSoR & la & 4.06 \% & 5.61 \% & 3.17 \% & 3.5 s / 4 cores & L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.\\
Shift R-CNN (mono) & & 3.87 \% & 6.88 \% & 2.83 \% & 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.\\
MVRA + I-FRCNN+ & & 3.27 \% & 5.19 \% & 2.49 \% & 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.\\
SparVox3D & & 3.20 \% & 5.27 \% & 2.56 \% & 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.\\
TopNet-UncEst & la & 3.02 \% & 3.24 \% & 2.26 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
GS3D & & 2.90 \% & 4.47 \% & 2.47 \% & 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.\\
3D-GCK & & 2.52 \% & 3.27 \% & 2.11 \% & 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.\\
WeakM3D & & 2.26 \% & 5.03 \% & 1.63 \% & 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.\\
ROI-10D & & 2.02 \% & 4.32 \% & 1.46 \% & 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.\\
FQNet & & 1.51 \% & 2.77 \% & 1.01 \% & 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.\\
3D-SSMFCNN & & 1.41 \% & 1.88 \% & 1.11 \% & 0.1 s / GPU & L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.\\
f3sd & & 0.01 \% & 0.01 \% & 0.01 \% & 1.67 s / 1 core & \\
mBoW & la & 0.00 \% & 0.00 \% & 0.00 \% & 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.
\end{tabular}