\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
OFFNet & & 86.00 \% & 88.86 \% & 81.12 \% & 0.1 s / GPU & \\
TED & & 84.08 \% & 92.46 \% & 78.07 \% & 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.\\
CasA++ & & 83.98 \% & 92.24 \% & 78.05 \% & 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.\\
UPIDet & & 83.78 \% & 89.86 \% & 76.98 \% & 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.\\
LoGoNet & & 83.51 \% & 89.90 \% & 77.41 \% & 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.\\
CasA & & 82.95 \% & 92.71 \% & 76.78 \% & 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.\\
IMLIDAR(base) & & 81.31 \% & 90.67 \% & 74.64 \% & 0.1 s / 1 core & \\
RangeIoUDet & la & 81.24 \% & 90.24 \% & 74.49 \% & 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.\\
HMFI & & 81.13 \% & 89.09 \% & 74.30 \% & 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.\\
VPA & & 81.12 \% & 89.41 \% & 74.43 \% & 0.01 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
MLF-DET & & 81.07 \% & 87.17 \% & 73.92 \% & 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.\\
USVLab BSAODet & & 80.87 \% & 86.64 \% & 73.87 \% & 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.\\
HA-PillarNet & & 80.36 \% & 89.76 \% & 74.40 \% & 0.05 s / 1 core & \\
U\_PV\_V2\_ep100\_80 & & 80.31 \% & 89.14 \% & 73.46 \% & 0... s / 1 core & \\
CAT-Det & & 80.25 \% & 87.79 \% & 73.41 \% & 0.3 s / GPU & Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.\\
BPG3D & & 80.12 \% & 88.63 \% & 73.11 \% & 0.05 s / 1 core & \\
EQ-PVRCNN & & 80.09 \% & 88.92 \% & 73.79 \% & 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.\\
DSA-PV-RCNN & la & 80.05 \% & 88.52 \% & 74.20 \% & 0.08 s / 1 core & P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.\\
PIPC-3Ddet & & 80.02 \% & 89.47 \% & 73.26 \% & 0.05 s / 1 core & \\
Anonymous & & 79.93 \% & 89.44 \% & 72.84 \% & 0.04 s / 1 core & \\
HINTED & & 79.73 \% & 86.59 \% & 73.13 \% & 0.04 s / 1 core & \\
MMLab PV-RCNN & la & 79.70 \% & 86.43 \% & 72.96 \% & 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.\\
PSMS-Net & la & 79.46 \% & 88.39 \% & 72.63 \% & 0.1 s / 1 core & \\
PDV & & 79.34 \% & 88.66 \% & 72.56 \% & 0.1 s / 1 core & J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.\\
U\_PV\_V2\_ep\_100\_100 & & 79.22 \% & 86.74 \% & 72.91 \% & 0.1 s / 1 core & \\
GeVo & & 79.16 \% & 88.89 \% & 72.76 \% & 0.05 s / 1 core & \\
CZY\_PPF\_Net & & 79.14 \% & 87.01 \% & 72.46 \% & 0.1 s / 1 core & \\
KPTr & & 79.10 \% & 87.02 \% & 72.03 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
PASS-PV-RCNN-Plus & & 78.82 \% & 86.15 \% & 72.28 \% & 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.\\
M3DeTR & & 78.80 \% & 87.21 \% & 71.88 \% & 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.\\
HAF-PVP\_test & & 78.66 \% & 89.62 \% & 71.38 \% & 0.09 s / 1 core & \\
DiffCandiDet & & 78.64 \% & 88.43 \% & 72.22 \% & 0.06 s / GPU & \\
PV-RCNN-Plus & & 78.56 \% & 86.39 \% & 72.31 \% & 1 s / 1 core & \\
IA-SSD (single) & & 78.34 \% & 88.78 \% & 71.63 \% & 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.\\
HotSpotNet & & 78.31 \% & 85.79 \% & 71.24 \% & 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.\\
PG-RCNN & & 78.30 \% & 87.89 \% & 71.76 \% & 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.\\
DDF & & 78.02 \% & 88.97 \% & 71.11 \% & 0.1 s / 1 core & \\
LGNet-3classes & & 77.80 \% & 84.31 \% & 71.26 \% & 0.11 s / 1 core & \\
focalnet & & 77.79 \% & 84.68 \% & 72.72 \% & 0.05 s / 1 core & \\
focalnet & & 77.75 \% & 84.80 \% & 72.57 \% & 0.05 s / 1 core & \\
SDGUFusion & & 77.61 \% & 85.79 \% & 71.37 \% & 0.5 s / 1 core & \\
MMLab-PartA^2 & la & 77.52 \% & 88.70 \% & 70.41 \% & 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.\\
DFAF3D & & 77.41 \% & 86.98 \% & 70.42 \% & 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.\\
FIRM-Net & & 77.23 \% & 89.35 \% & 70.31 \% & 0.07 s / 1 core & \\
PVTr & & 77.19 \% & 89.56 \% & 70.28 \% & 0.1 s / 1 core & \\
casx & & 77.18 \% & 88.67 \% & 70.42 \% & 0.01 s / 1 core & \\
mm3d\_PartA2 & & 77.15 \% & 88.70 \% & 70.69 \% & 0.1 s / GPU & \\
PointPainting & la & 76.92 \% & 87.33 \% & 68.21 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
3ONet & & 76.91 \% & 88.98 \% & 69.85 \% & 0.1 s / 1 core & H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.\\
RPF3D & & 76.91 \% & 89.31 \% & 70.07 \% & 0.1 s / 1 core & \\
OGMMDet & & 76.86 \% & 86.40 \% & 71.57 \% & 0.01 s / 1 core & \\
ANM & & 76.86 \% & 88.28 \% & 71.57 \% & ANM / & \\
GraphAlign(ICCV2023) & & 76.81 \% & 84.53 \% & 71.90 \% & 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.\\
RAFDet & & 76.73 \% & 87.01 \% & 70.57 \% & 0.1 s / 1 core & \\
F-ConvNet & la & 76.71 \% & 86.39 \% & 66.92 \% & 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.\\
P2V-RCNN & & 76.52 \% & 88.21 \% & 69.90 \% & 0.1 s / 2 cores & 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.\\
af & & 76.21 \% & 85.40 \% & 71.04 \% & 1 s / GPU & \\
BAPartA2S-4h & & 76.18 \% & 88.80 \% & 70.06 \% & 0.1 s / 1 core & \\
XT-PartA2 & & 76.18 \% & 87.18 \% & 70.88 \% & 0.1 s / GPU & \\
RAFDet & & 76.12 \% & 88.40 \% & 69.51 \% & 0.01 s / 1 core & \\
GF-pointnet & & 75.98 \% & 85.13 \% & 69.35 \% & 0.02 s / 1 core & \\
AAMVFNet & & 75.55 \% & 85.41 \% & 69.15 \% & 0.04 s / GPU & \\
F3D & & 75.42 \% & 88.22 \% & 68.69 \% & 0.01 s / 1 core & \\
ACFNet & & 75.34 \% & 86.11 \% & 70.41 \% & 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.\\
PR-SSD & & 75.28 \% & 85.63 \% & 69.93 \% & 0.02 s / GPU & \\
casxv1 & & 75.23 \% & 88.07 \% & 68.40 \% & 0.01 s / 1 core & \\
AMVFNet & & 75.20 \% & 84.47 \% & 69.12 \% & 0.04 s / GPU & \\
DA-Net & & 75.11 \% & 86.16 \% & 70.49 \% & 0.1 s / 1 core & \\
u\_second\_v4\_epoch\_10 & & 75.04 \% & 87.83 \% & 69.19 \% & 0.1 s / 1 core & \\
TF-PartA2 & & 74.98 \% & 86.86 \% & 68.78 \% & 0.1 s / 1 core & \\
centerpoint\_pcdet & & 74.76 \% & 85.81 \% & 67.86 \% & 0.06 s / 1 core & \\
Fast-CLOCs & & 74.74 \% & 89.54 \% & 67.54 \% & 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.\\
VoxelFSD-S & & 74.66 \% & 87.04 \% & 67.75 \% & 0.05 s / 1 core & \\
bs & & 74.64 \% & 83.68 \% & 68.29 \% & 0.1 s / 1 core & \\
SVGA-Net & & 74.64 \% & 84.62 \% & 67.64 \% & 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.\\
ACDet & & 74.52 \% & 88.21 \% & 68.33 \% & 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.\\
LGSLNet & & 74.48 \% & 83.67 \% & 69.78 \% & 0.1 s / GPU & \\
U\_second\_v4\_ep\_100\_8 & & 74.46 \% & 84.32 \% & 67.43 \% & 0.1 s / 1 core & \\
prcnn\_v18\_80\_100 & & 74.30 \% & 86.21 \% & 66.60 \% & 0.1 s / 1 core & \\
voxelnext\_pcdet & & 74.13 \% & 87.44 \% & 67.34 \% & 0.05 s / 1 core & \\
SC-SSD & & 74.05 \% & 84.20 \% & 67.45 \% & 1 s / 1 core & \\
VPFNet & & 73.62 \% & 82.08 \% & 65.27 \% & 0.2 s / 1 core & C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.\\
DVFENet & & 73.43 \% & 85.32 \% & 66.87 \% & 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.\\
focal & & 73.38 \% & 83.92 \% & 68.69 \% & 100 s / 1 core & \\
PA-Det3D & & 73.25 \% & 83.13 \% & 66.97 \% & 0.06 s / 1 core & \\
SRDL & & 73.21 \% & 85.22 \% & 66.45 \% & 0.05 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
RAFDet & & 73.13 \% & 84.78 \% & 66.98 \% & 0.01 s / 1 core & \\
L-AUG & & 73.07 \% & 83.69 \% & 67.72 \% & 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.\\
PI-SECOND & & 72.97 \% & 86.80 \% & 66.52 \% & 0.05 s / GPU & \\
IIOU & & 72.87 \% & 86.72 \% & 65.65 \% & 0.1 s / GPU & \\
MMLab-PointRCNN & la & 72.81 \% & 85.94 \% & 65.84 \% & 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.\\
MM\_SECOND & & 72.74 \% & 84.69 \% & 65.85 \% & 0.05 s / GPU & \\
SIF & & 72.73 \% & 84.96 \% & 64.94 \% & 0.1 s / 1 core & P. An: SIF. Submitted to CVIU 2021.\\
XView & & 72.70 \% & 87.59 \% & 64.96 \% & 0.1 s / 1 core & L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.\\
FromVoxelToPoint & & 72.62 \% & 86.71 \% & 65.42 \% & 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.\\
EOTL & & 72.37 \% & 82.07 \% & 62.06 \% & 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.\\
H^23D R-CNN & & 72.20 \% & 85.09 \% & 65.25 \% & 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\_dif & & 72.16 \% & 85.41 \% & 67.13 \% & 0.01 s / 1 core & \\
SFA-GCL(80) & & 71.96 \% & 86.93 \% & 64.97 \% & 0.04 s / 1 core & \\
SeSame-point & & 71.88 \% & 83.97 \% & 65.00 \% & N/A s / TITAN RTX & \\
P2P & & 71.78 \% & 82.09 \% & 65.28 \% & 0.1 s / GPU & \\
SFA-GCL(80, k=4) & & 71.75 \% & 86.48 \% & 62.80 \% & 0.04 s / 1 core & \\
SFA-GCL & & 71.73 \% & 86.78 \% & 64.73 \% & 0.04 s / 1 core & \\
MG & & 71.09 \% & 83.49 \% & 64.60 \% & 0.1 s / 1 core & \\
S-AT GCN & & 71.04 \% & 82.31 \% & 65.13 \% & 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.\\
IOUFusion & & 71.00 \% & 85.88 \% & 64.05 \% & 0.1 s / GPU & \\
DGEnhCL & & 70.83 \% & 85.51 \% & 61.95 \% & 0.04 s / 1 core & \\
LVFSD & & 70.52 \% & 85.37 \% & 64.21 \% & 0.06 s / & ERROR: Wrong syntax in BIBTEX file.\\
MGAF-3DSSD & & 70.16 \% & 86.28 \% & 62.99 \% & 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.\\
IA-SSD (multi) & & 70.13 \% & 84.82 \% & 65.13 \% & 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.\\
AB3DMOT & la on & 69.54 \% & 82.18 \% & 62.98 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
fuf & & 69.44 \% & 84.83 \% & 62.66 \% & 10 s / 1 core & \\
SFA-GCL(baseline) & & 69.29 \% & 86.10 \% & 60.34 \% & 0.04 s / 1 core & \\
SeSame-voxel & & 69.21 \% & 86.97 \% & 62.47 \% & N/A s / TITAN RTX & \\
IIOU\_LDR & & 69.15 \% & 82.85 \% & 64.18 \% & 0.03 s / 1 core & \\
VSAC & & 69.14 \% & 88.31 \% & 62.03 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SFA-GCL\_dataaug & & 69.10 \% & 85.79 \% & 62.14 \% & 0.04 s / 1 core & \\
SFA-GCL & & 69.05 \% & 85.82 \% & 62.07 \% & 0.04 s / 1 core & \\
ARPNET & & 68.72 \% & 82.61 \% & 62.00 \% & 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.\\
PointPillars & la & 68.55 \% & 83.79 \% & 61.71 \% & 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.\\
ROT\_S3D & & 68.28 \% & 84.11 \% & 63.43 \% & 0.1 s / GPU & \\
EPNet++ & & 67.26 \% & 79.81 \% & 61.75 \% & 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.\\
TANet & & 66.37 \% & 81.15 \% & 60.10 \% & 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.\\
PointRGBNet & & 65.68 \% & 79.64 \% & 59.48 \% & 0.08 s / 4 cores & P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.\\
HA PillarNet & & 65.49 \% & 77.81 \% & 58.58 \% & 0.05 s / 1 core & \\
PUDet & & 65.35 \% & 79.42 \% & 58.98 \% & 0.3 s / GPU & \\
MMpointpillars & & 64.10 \% & 75.90 \% & 58.55 \% & 0.05 s / 1 core & \\
PFF3D & la & 64.06 \% & 78.02 \% & 58.06 \% & 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.\\
MMpp & & 63.72 \% & 78.33 \% & 57.50 \% & 0.05 s / 1 core & \\
SeSame-pillar & & 63.61 \% & 75.66 \% & 57.48 \% & N/A s / TITAN RTX & \\
SubCNN & & 63.36 \% & 71.97 \% & 55.42 \% & 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.\\
PiFeNet & & 62.62 \% & 77.54 \% & 55.66 \% & 0.03 s / 1 core & D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. IEEE Robotics and Automation Letters 2022.\\
Pose-RCNN & & 62.02 \% & 75.74 \% & 53.99 \% & 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.\\
SCNet & la & 61.11 \% & 77.77 \% & 54.82 \% & 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.\\
DMF & st & 60.85 \% & 71.83 \% & 54.58 \% & 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.\\
SFEBEV & & 60.01 \% & 72.79 \% & 54.59 \% & 0.01 s / 1 core & \\
BirdNet+ & la & 59.44 \% & 67.52 \% & 54.27 \% & 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.\\
AVOD-FPN & la & 58.70 \% & 69.21 \% & 53.47 \% & 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.\\
Deep3DBox & & 58.56 \% & 68.31 \% & 50.30 \% & 1.5 s / GPU & A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.\\
3DOP & st & 58.45 \% & 72.24 \% & 51.91 \% & 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.\\
Complexer-YOLO & la & 58.28 \% & 65.41 \% & 54.27 \% & 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.\\
DFSemONet(Baseline) & & 57.61 \% & 74.31 \% & 51.75 \% & 0.04 s / GPU & \\
DD3D & & 57.42 \% & 73.60 \% & 50.90 \% & 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) .\\
DeepStereoOP & & 56.55 \% & 69.36 \% & 49.37 \% & 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.\\
MonoLiG & & 54.91 \% & 76.10 \% & 47.58 \% & 0.03 s / 1 core & A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.\\
SeSame-voxel w/score & & 54.49 \% & 66.51 \% & 49.51 \% & N/A s / GPU & \\
MonoInsight & & 54.40 \% & 70.86 \% & 46.33 \% & 0.03 s / 1 core & \\
MonoInsight & & 54.40 \% & 70.86 \% & 46.33 \% & 0.03 s / 1 core & \\
Mix-Teaching & & 54.00 \% & 70.90 \% & 46.66 \% & 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.\\
Mono3D & & 53.96 \% & 67.33 \% & 47.91 \% & 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.\\
MVAF-Net(3-classes) & & 51.18 \% & 65.18 \% & 47.26 \% & 0.1 s / 1 core & \\
AVOD & la & 51.05 \% & 64.81 \% & 45.12 \% & 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.\\
BirdNet+ (legacy) & la & 50.94 \% & 69.92 \% & 47.01 \% & 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.\\
MVAF-Net(3-classes) & & 50.64 \% & 61.90 \% & 46.62 \% & 0.1 s / 1 core & \\
FRCNN+Or & & 49.53 \% & 63.45 \% & 43.65 \% & 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.\\
MonoPSR & & 49.32 \% & 58.63 \% & 43.05 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
StereoDistill & & 48.99 \% & 65.65 \% & 43.14 \% & 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.\\
MonoFlex & & 47.91 \% & 65.51 \% & 40.40 \% & 0.03 s / GPU & Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.\\
HomoLoss(monoflex) & & 47.36 \% & 62.89 \% & 40.55 \% & 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.\\
MonoLSS & & 47.09 \% & 65.31 \% & 41.74 \% & 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.\\
QD-3DT & on & 46.24 \% & 64.64 \% & 40.58 \% & 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.\\
DSGN++ & st & 45.94 \% & 57.93 \% & 41.93 \% & 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.\\
MonoDDE & & 45.58 \% & 63.91 \% & 39.29 \% & 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.\\
LPCG-Monoflex & & 45.24 \% & 63.07 \% & 39.28 \% & 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.\\
MonoUNI & & 45.21 \% & 62.21 \% & 38.28 \% & 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.\\
MMLAB LIGA-Stereo & st & 45.13 \% & 63.89 \% & 39.23 \% & 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.\\
monodle & & 45.12 \% & 61.84 \% & 37.95 \% & 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 .\\
BirdNet & la & 45.03 \% & 62.69 \% & 41.88 \% & 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.\\
MonOAPC & & 44.74 \% & 60.40 \% & 38.01 \% & 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.\\
SparsePool & & 43.50 \% & 59.77 \% & 39.36 \% & 0.13 s / 8 cores & Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.\\
MonoDTR & & 42.45 \% & 56.40 \% & 36.32 \% & 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.\\
SH3D & & 41.71 \% & 60.19 \% & 36.43 \% & 0.1 s / 1 core & \\
sensekitti & & 41.14 \% & 47.48 \% & 35.07 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
MonoSIM\_v2 & & 40.72 \% & 58.96 \% & 35.22 \% & 0.03 s / 1 core & \\
CG-Stereo & st & 40.64 \% & 60.24 \% & 35.55 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
MonoRCNN++ & & 39.84 \% & 56.32 \% & 34.82 \% & 0.07 s / GPU & X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.\\
MonoPair & & 39.47 \% & 53.36 \% & 33.95 \% & 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.\\
CMKD & & 38.70 \% & 56.46 \% & 34.00 \% & 0.1 s / 1 core & Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.\\
DEVIANT & & 38.46 \% & 57.64 \% & 32.76 \% & 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.\\
Disp R-CNN (velo) & st & 35.93 \% & 52.35 \% & 31.09 \% & 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.\\
Disp R-CNN & st & 35.92 \% & 52.37 \% & 31.08 \% & 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.\\
MonoAIU & & 35.88 \% & 53.18 \% & 30.98 \% & 0.03 s / GPU & \\
GUPNet & & 35.03 \% & 55.03 \% & 31.18 \% & 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.\\
Shift R-CNN (mono) & & 34.77 \% & 51.95 \% & 31.10 \% & 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.\\
MonoAuxNorm & & 34.71 \% & 52.62 \% & 30.22 \% & 0.02 s / GPU & \\
SparsePool & & 34.56 \% & 43.33 \% & 31.09 \% & 0.13 s / 8 cores & Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.\\
MonoRUn & & 34.36 \% & 49.04 \% & 30.22 \% & 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.\\
SPG\_mini & la & 34.28 \% & 36.23 \% & 32.09 \% & 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.\\
BtcDet & la & 33.94 \% & 35.79 \% & 31.90 \% & 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.\\
Plane-Constraints & & 32.87 \% & 48.36 \% & 28.52 \% & 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.\\
Point-GNN & la & 32.37 \% & 36.29 \% & 29.81 \% & 0.6 s / GPU & W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.\\
MonoEF & & 32.19 \% & 43.70 \% & 27.93 \% & 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.\\
D4LCN & & 31.70 \% & 48.03 \% & 26.99 \% & 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.\\
OPA-3D & & 31.64 \% & 45.97 \% & 27.92 \% & 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.\\
M3D-RPN & & 31.09 \% & 48.11 \% & 26.10 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
Aug3D-RPN & & 30.01 \% & 42.60 \% & 24.74 \% & 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.\\
DDMP-3D & & 29.53 \% & 46.42 \% & 25.91 \% & 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.\\
Anonymous & & 29.16 \% & 44.95 \% & 26.17 \% & 0.1 s / 1 core & \\
PS-fld & & 27.99 \% & 41.21 \% & 24.75 \% & 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.\\
SS3D & & 27.79 \% & 42.95 \% & 24.26 \% & 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.\\
CMAN & & 27.63 \% & 42.58 \% & 23.14 \% & 0.15 s / 1 core & C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.\\
MonoFRD & & 26.87 \% & 39.05 \% & 24.09 \% & 0.01 s / 1 core & \\
DFR-Net & & 24.85 \% & 38.60 \% & 21.86 \% & 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.\\
MonoTRKDv2 & & 24.38 \% & 35.75 \% & 21.06 \% & 40 s / 1 core & \\
MonoTAKD V2 & & 24.18 \% & 37.61 \% & 21.44 \% & 0.1 s / 1 core & \\
MonoLTKD & & 24.18 \% & 37.61 \% & 21.44 \% & 0.04 s / 1 core & \\
MonoTAKD & & 24.18 \% & 37.61 \% & 21.44 \% & 0.1 s / 1 core & \\
MonoLTKD\_V3 & & 24.18 \% & 37.61 \% & 21.44 \% & 0.04 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
Cube R-CNN & & 23.98 \% & 29.00 \% & 21.67 \% & 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.\\
SeSame-pillar w/scor & & 21.79 \% & 19.53 \% & 20.12 \% & N/A s / 1 core & \\
DSGN & st & 20.28 \% & 29.76 \% & 19.13 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
MonoNeRD & & 20.13 \% & 30.64 \% & 18.19 \% & 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.\\
SVDM-VIEW & & 20.06 \% & 30.50 \% & 17.45 \% & 1 s / 1 core & \\
CaDDN & & 19.96 \% & 30.35 \% & 17.38 \% & 0.63 s / GPU & C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.\\
LSVM-MDPM-sv & & 19.15 \% & 26.05 \% & 18.02 \% & 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.\\
PGD-FCOS3D & & 19.10 \% & 31.75 \% & 16.59 \% & 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.\\
OC Stereo & st & 18.99 \% & 29.07 \% & 16.40 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
DPM-VOC+VP & & 18.92 \% & 27.97 \% & 17.43 \% & 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.\\
CIE & & 17.52 \% & 24.39 \% & 15.84 \% & 0.1 s / 1 core & Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.\\
SST [st] & st & 16.72 \% & 27.08 \% & 14.53 \% & 1 s / 1 core & \\
SGM3D & & 16.50 \% & 25.51 \% & 15.09 \% & 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.\\
RT3D-GMP & st & 16.18 \% & 23.91 \% & 14.23 \% & 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.\\
RefinedMPL & & 16.02 \% & 26.54 \% & 13.20 \% & 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.\\
FMF-occlusion-net & & 15.24 \% & 23.82 \% & 13.84 \% & 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.\\
DPM-C8B1 & st & 14.64 \% & 23.93 \% & 13.09 \% & 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.\\
MonoGhost\_Ped\_Cycl & & 14.12 \% & 19.66 \% & 12.40 \% & 0.03 s / 1 core & \\
MonoSIM & & 13.83 \% & 19.81 \% & 11.81 \% & 0.16 s / 1 core & \\
SeSame-point w/score & & 10.17 \% & 12.39 \% & 9.31 \% & N/A s / GPU & \\
mdab & & 9.59 \% & 15.41 \% & 8.62 \% & 0.02 s / 1 core & \\
ESGN & st & 7.73 \% & 12.50 \% & 6.80 \% & 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.\\
RT3DStereo & st & 3.88 \% & 5.46 \% & 3.54 \% & 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.\\
init & & 0.04 \% & 0.04 \% & 0.02 \% & 0.03 s / 1 core & \\
DA3D+KM3D+v2-99 & & 0.00 \% & 0.00 \% & 0.00 \% & 0.120s / GPU & \\
mdab & & 0.00 \% & 0.00 \% & 0.00 \% & 0.02 s / 1 core & \\
DA3D+KM3D & & 0.00 \% & 0.00 \% & 0.00 \% & 0.02 s / GPU & \\
DA3D & & 0.00 \% & 0.00 \% & 0.00 \% & 0.03 s / 1 core &
\end{tabular}