\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
VMVS & la & 68.19 \% & 79.98 \% & 63.18 \% & 0.25 s / GPU & J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.\\
SubCNN & & 66.70 \% & 79.65 \% & 61.35 \% & 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.\\
DD3D & & 63.92 \% & 77.09 \% & 59.41 \% & 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) .\\
F-ConvNet & la & 63.87 \% & 75.19 \% & 58.57 \% & 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.\\
UPIDet & & 61.92 \% & 72.38 \% & 59.31 \% & 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.\\
CasA++ & & 61.59 \% & 71.78 \% & 58.71 \% & 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.\\
3DOP & st & 61.48 \% & 74.22 \% & 55.89 \% & 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.\\
TED & & 61.44 \% & 71.72 \% & 58.59 \% & 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.\\
LoGoNet & & 60.70 \% & 69.16 \% & 58.13 \% & 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.\\
HotSpotNet & & 60.65 \% & 70.36 \% & 57.42 \% & 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.\\
MonoLSS & & 60.28 \% & 75.13 \% & 53.85 \% & 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.\\
DeepStereoOP & & 60.15 \% & 73.76 \% & 55.30 \% & 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.\\
Pose-RCNN & & 59.84 \% & 76.24 \% & 53.59 \% & 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.\\
USVLab BSAODet & & 59.73 \% & 69.95 \% & 55.85 \% & 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.\\
CasA & & 59.69 \% & 70.33 \% & 56.89 \% & 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.\\
SDGUFusion & & 58.93 \% & 68.49 \% & 56.15 \% & 0.5 s / 1 core & \\
FFNet & & 58.87 \% & 69.24 \% & 53.75 \% & 1.07 s / GPU & C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.\\
Mono3D & & 58.66 \% & 71.19 \% & 53.94 \% & 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.\\
VPFNet & & 58.63 \% & 67.96 \% & 54.77 \% & 0.2 s / 1 core & C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.\\
P2V-RCNN & & 57.94 \% & 68.67 \% & 55.07 \% & 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.\\
OGMMDet & & 57.91 \% & 69.64 \% & 55.24 \% & 0.01 s / 1 core & \\
ANM & & 57.91 \% & 69.64 \% & 55.24 \% & ANM / & \\
Fast-CLOCs & & 57.35 \% & 70.93 \% & 54.48 \% & 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.\\
EOTL & & 57.17 \% & 68.99 \% & 51.48 \% & 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.\\
MLF-DET & & 56.89 \% & 64.49 \% & 53.17 \% & 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.\\
R^2 R-CNN & & 56.78 \% & 66.45 \% & 53.26 \% & 0.1 s / 1 core & \\
KPTr & & 56.50 \% & 66.74 \% & 52.72 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
af & & 56.43 \% & 65.85 \% & 52.71 \% & 1 s / GPU & \\
BPG3D & & 56.42 \% & 66.21 \% & 52.80 \% & 0.05 s / 1 core & \\
FIRM-Net & & 56.33 \% & 66.69 \% & 53.53 \% & 0.07 s / 1 core & \\
RPF3D & & 56.19 \% & 66.61 \% & 53.42 \% & 0.1 s / 1 core & \\
OFFNet & & 55.58 \% & 63.92 \% & 53.01 \% & 0.1 s / GPU & \\
PIPC-3Ddet & & 55.54 \% & 63.09 \% & 51.84 \% & 0.05 s / 1 core & \\
focalnet & & 55.17 \% & 63.53 \% & 52.79 \% & 0.05 s / 1 core & \\
focalnet & & 55.11 \% & 63.52 \% & 52.81 \% & 0.05 s / 1 core & \\
DFAF3D & & 54.99 \% & 65.42 \% & 51.21 \% & 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.\\
PV-RCNN-Plus & & 54.97 \% & 64.32 \% & 51.85 \% & 1 s / 1 core & \\
LGNet-3classes & & 54.90 \% & 65.18 \% & 52.21 \% & 0.11 s / 1 core & \\
3ONet & & 54.88 \% & 66.35 \% & 50.82 \% & 0.1 s / 1 core & H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.\\
CZY\_PPF\_Net & & 54.84 \% & 63.95 \% & 52.36 \% & 0.1 s / 1 core & \\
FromVoxelToPoint & & 54.80 \% & 66.21 \% & 52.03 \% & 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.\\
RAFDet & & 54.68 \% & 65.13 \% & 52.20 \% & 0.01 s / 1 core & \\
MonoPSR & & 54.65 \% & 68.98 \% & 50.07 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
F3D & & 54.53 \% & 64.68 \% & 51.90 \% & 0.01 s / 1 core & \\
centerpoint\_pcdet & & 54.48 \% & 63.77 \% & 52.20 \% & 0.06 s / 1 core & \\
voxelnext\_pcdet & & 54.41 \% & 64.12 \% & 51.79 \% & 0.05 s / 1 core & \\
DSA-PV-RCNN & la & 54.38 \% & 63.12 \% & 51.98 \% & 0.08 s / 1 core & P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.\\
PDV & & 54.08 \% & 63.43 \% & 50.75 \% & 0.1 s / 1 core & J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.\\
PSMS-Net & la & 54.07 \% & 66.04 \% & 51.24 \% & 0.1 s / 1 core & \\
ACFNet & & 53.97 \% & 65.55 \% & 49.97 \% & 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.\\
casx & & 53.85 \% & 66.81 \% & 49.83 \% & 0.01 s / 1 core & \\
PASS-PV-RCNN-Plus & & 53.82 \% & 63.49 \% & 51.30 \% & 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.\\
u\_second\_v4\_epoch\_10 & & 53.81 \% & 64.38 \% & 51.29 \% & 0.1 s / 1 core & \\
monodle & & 53.78 \% & 69.94 \% & 48.98 \% & 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 .\\
HA-PillarNet & & 53.77 \% & 63.18 \% & 50.98 \% & 0.05 s / 1 core & \\
VPA & & 53.76 \% & 66.61 \% & 51.11 \% & 0.01 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
MGAF-3DSSD & & 53.73 \% & 64.69 \% & 49.84 \% & 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.\\
IOUFusion & & 53.71 \% & 64.03 \% & 49.66 \% & 0.1 s / GPU & \\
SFA-GCL(80, k=4) & & 53.43 \% & 66.21 \% & 50.74 \% & 0.04 s / 1 core & \\
LGSLNet & & 53.38 \% & 62.69 \% & 50.86 \% & 0.1 s / GPU & \\
SRDL & & 53.36 \% & 63.39 \% & 50.43 \% & 0.05 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SFA-GCL(80) & & 53.26 \% & 64.46 \% & 48.99 \% & 0.04 s / 1 core & \\
IIOU & & 53.22 \% & 63.25 \% & 49.25 \% & 0.1 s / GPU & \\
PG-RCNN & & 53.12 \% & 63.73 \% & 50.46 \% & 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.\\
Anonymous & & 53.12 \% & 66.41 \% & 48.96 \% & 0.04 s / 1 core & \\
SFA-GCL\_dataaug & & 53.10 \% & 65.98 \% & 50.38 \% & 0.04 s / 1 core & \\
RAFDet & & 53.10 \% & 63.36 \% & 49.53 \% & 0.01 s / 1 core & \\
casxv1 & & 53.06 \% & 65.78 \% & 50.67 \% & 0.01 s / 1 core & \\
PA-Det3D & & 53.03 \% & 62.44 \% & 50.51 \% & 0.06 s / 1 core & \\
SFA-GCL & & 52.98 \% & 65.70 \% & 50.30 \% & 0.04 s / 1 core & \\
SFA-GCL(baseline) & & 52.83 \% & 65.53 \% & 50.14 \% & 0.04 s / 1 core & \\
U\_PV\_V2\_ep100\_80 & & 52.77 \% & 61.91 \% & 50.29 \% & 0... s / 1 core & \\
U\_second\_v4\_ep\_100\_8 & & 52.73 \% & 63.41 \% & 50.32 \% & 0.1 s / 1 core & \\
LVFSD & & 52.72 \% & 63.39 \% & 49.93 \% & 0.06 s / & ERROR: Wrong syntax in BIBTEX file.\\
IA-SSD (single) & & 52.69 \% & 62.90 \% & 50.27 \% & 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.\\
MonoUNI & & 52.62 \% & 69.15 \% & 47.89 \% & 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.\\
HMFI & & 52.47 \% & 63.10 \% & 49.57 \% & 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.\\
MMLab PV-RCNN & la & 52.42 \% & 63.45 \% & 49.23 \% & 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.\\
RAFDet & & 52.34 \% & 62.42 \% & 49.92 \% & 0.1 s / 1 core & \\
SVGA-Net & & 52.27 \% & 62.33 \% & 49.44 \% & 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.\\
MMLab-PartA^2 & la & 52.20 \% & 63.51 \% & 48.27 \% & 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.\\
focal & & 52.19 \% & 60.54 \% & 49.90 \% & 100 s / 1 core & \\
FRCNN+Or & & 52.15 \% & 67.03 \% & 47.14 \% & 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.\\
SIF & & 52.10 \% & 62.72 \% & 49.19 \% & 0.1 s / 1 core & P. An: SIF. Submitted to CVIU 2021.\\
U\_PV\_V2\_ep\_100\_100 & & 51.89 \% & 60.70 \% & 49.60 \% & 0.1 s / 1 core & \\
DGEnhCL & & 51.54 \% & 64.48 \% & 48.87 \% & 0.04 s / 1 core & \\
QD-3DT & on & 51.46 \% & 68.64 \% & 47.00 \% & 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.\\
DDF & & 51.45 \% & 64.44 \% & 47.26 \% & 0.1 s / 1 core & \\
PR-SSD & & 51.15 \% & 60.89 \% & 47.65 \% & 0.02 s / GPU & \\
GF-pointnet & & 51.05 \% & 61.33 \% & 48.28 \% & 0.02 s / 1 core & \\
SFA-GCL & & 51.00 \% & 63.63 \% & 46.80 \% & 0.04 s / 1 core & \\
GeVo & & 50.97 \% & 60.31 \% & 48.62 \% & 0.05 s / 1 core & \\
ACDet & & 50.90 \% & 62.39 \% & 48.34 \% & 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.\\
GUPNet & & 50.74 \% & 68.93 \% & 44.01 \% & 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.\\
SC-SSD & & 50.74 \% & 60.57 \% & 48.22 \% & 1 s / 1 core & \\
DEVIANT & & 50.66 \% & 68.78 \% & 45.89 \% & 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.\\
DVFENet & & 50.52 \% & 60.32 \% & 47.92 \% & 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.\\
TF-PartA2 & & 50.50 \% & 60.42 \% & 46.90 \% & 0.1 s / 1 core & \\
OPA-3D & & 50.42 \% & 68.35 \% & 43.91 \% & 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.\\
PointPainting & la & 50.22 \% & 59.25 \% & 46.95 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
MonoInsight & & 50.22 \% & 63.94 \% & 44.33 \% & 0.03 s / 1 core & \\
MonoInsight & & 50.22 \% & 63.94 \% & 44.33 \% & 0.03 s / 1 core & \\
BAPartA2S-4h & & 50.21 \% & 60.15 \% & 47.41 \% & 0.1 s / 1 core & \\
Mix-Teaching & & 50.19 \% & 64.04 \% & 44.37 \% & 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.\\
M3DeTR & & 50.09 \% & 58.90 \% & 47.66 \% & 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.\\
IA-SSD (multi) & & 49.58 \% & 62.51 \% & 47.17 \% & 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.\\
HAF-PVP\_test & & 49.33 \% & 58.87 \% & 46.53 \% & 0.09 s / 1 core & \\
XView & & 49.30 \% & 58.39 \% & 46.81 \% & 0.1 s / 1 core & L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.\\
Test\_dif & & 48.72 \% & 58.85 \% & 46.44 \% & 0.01 s / 1 core & \\
ARPNET & & 48.49 \% & 60.47 \% & 45.02 \% & 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.\\
PVTr & & 48.45 \% & 57.80 \% & 45.96 \% & 0.1 s / 1 core & \\
DPPFA-Net & & 48.38 \% & 56.13 \% & 45.93 \% & 0.1 s / 1 core & J. Wang, X. Kong, H. Nishikawa, Q. Lian and H. Tomiyama: Dynamic Point-Pixel Feature Alignment for Multi-modal 3D Object Detection. IEEE Internet of Things Journal 2023.\\
PI-SECOND & & 48.05 \% & 57.38 \% & 44.39 \% & 0.05 s / GPU & \\
PointPillars & la & 48.05 \% & 57.47 \% & 45.40 \% & 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.\\
AMVFNet & & 47.99 \% & 58.15 \% & 44.39 \% & 0.04 s / GPU & \\
HINTED & & 47.84 \% & 62.13 \% & 43.71 \% & 0.04 s / 1 core & \\
MonoRUn & & 47.82 \% & 63.28 \% & 43.23 \% & 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.\\
XT-PartA2 & & 47.80 \% & 57.11 \% & 45.03 \% & 0.1 s / GPU & \\
SeSame-voxel & & 47.60 \% & 58.80 \% & 43.53 \% & N/A s / TITAN RTX & \\
L-AUG & & 47.59 \% & 58.42 \% & 44.64 \% & 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.\\
AAMVFNet & & 47.39 \% & 58.19 \% & 44.80 \% & 0.04 s / GPU & \\
MMLab-PointRCNN & la & 47.33 \% & 57.19 \% & 44.31 \% & 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.\\
PUDet & & 47.30 \% & 58.44 \% & 44.83 \% & 0.3 s / GPU & \\
MG & & 47.28 \% & 56.02 \% & 43.97 \% & 0.1 s / 1 core & \\
SeSame-point & & 47.09 \% & 56.55 \% & 44.58 \% & N/A s / TITAN RTX & \\
VoxelFSD-S & & 47.06 \% & 56.88 \% & 44.58 \% & 0.05 s / 1 core & \\
prcnn\_v18\_80\_100 & & 46.82 \% & 58.60 \% & 43.91 \% & 0.1 s / 1 core & \\
S-AT GCN & & 46.64 \% & 56.55 \% & 44.23 \% & 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.\\
PiFeNet & & 46.59 \% & 55.11 \% & 44.14 \% & 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.\\
Shift R-CNN (mono) & & 46.56 \% & 64.73 \% & 41.86 \% & 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.\\
DiffCandiDet & & 46.32 \% & 56.30 \% & 44.10 \% & 0.06 s / GPU & \\
MonoAIU & & 46.23 \% & 63.48 \% & 39.81 \% & 0.03 s / GPU & \\
IMLIDAR(base) & & 46.00 \% & 54.49 \% & 43.58 \% & 0.1 s / 1 core & \\
VSAC & & 45.97 \% & 58.47 \% & 43.24 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
Disp R-CNN & st & 45.80 \% & 63.23 \% & 41.32 \% & 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 (velo) & st & 45.66 \% & 63.16 \% & 41.14 \% & 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.\\
bs & & 45.58 \% & 54.34 \% & 43.16 \% & 0.1 s / 1 core & \\
HomoLoss(monoflex) & & 45.44 \% & 59.94 \% & 41.15 \% & 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.\\
mm3d\_PartA2 & & 45.39 \% & 54.51 \% & 42.50 \% & 0.1 s / GPU & \\
ROT\_S3D & & 45.27 \% & 56.72 \% & 43.12 \% & 0.1 s / GPU & \\
GraphAlign(ICCV2023) & & 45.18 \% & 52.14 \% & 43.18 \% & 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.\\
LSFM & & 44.92 \% & 48.58 \% & 42.74 \% & 0.05 s / 4 cores & \\
Plane-Constraints & & 44.76 \% & 57.28 \% & 40.56 \% & 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.\\
MM\_SECOND & & 44.69 \% & 54.97 \% & 41.93 \% & 0.05 s / GPU & \\
IIOU\_LDR & & 44.65 \% & 56.31 \% & 42.43 \% & 0.03 s / 1 core & \\
MonoFlex & & 44.20 \% & 58.96 \% & 39.89 \% & 0.03 s / GPU & Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.\\
AVOD-FPN & la & 43.99 \% & 53.48 \% & 41.56 \% & 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.\\
CAT-Det & & 43.86 \% & 52.75 \% & 41.15 \% & 0.3 s / GPU & Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.\\
DSGN++ & st & 43.35 \% & 54.16 \% & 40.10 \% & 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.\\
EPNet++ & & 43.29 \% & 51.89 \% & 40.98 \% & 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.\\
MonoSIM\_v2 & & 43.09 \% & 56.78 \% & 37.54 \% & 0.03 s / 1 core & \\
Frustum-PointPillars & & 42.97 \% & 49.04 \% & 40.69 \% & 0.06 s / 4 cores & A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.\\
MonoRCNN++ & & 42.54 \% & 56.59 \% & 36.64 \% & 0.07 s / GPU & X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.\\
MonOAPC & & 42.52 \% & 56.84 \% & 38.43 \% & 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.\\
MonoPair & & 42.38 \% & 55.26 \% & 38.53 \% & 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.\\
DFSemONet(Baseline) & & 41.33 \% & 51.00 \% & 39.35 \% & 0.04 s / GPU & \\
MonoDDE & & 41.09 \% & 55.28 \% & 36.85 \% & 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.\\
PFF3D & la & 40.99 \% & 48.75 \% & 38.99 \% & 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.\\
MMLAB LIGA-Stereo & st & 40.98 \% & 53.16 \% & 38.12 \% & 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.\\
Anonymous & & 40.52 \% & 54.94 \% & 34.87 \% & 0.1 s / 1 core & \\
MMpointpillars & & 40.32 \% & 50.36 \% & 37.58 \% & 0.05 s / 1 core & \\
SH3D & & 40.07 \% & 52.95 \% & 36.09 \% & 0.1 s / 1 core & \\
LPCG-Monoflex & & 39.79 \% & 56.60 \% & 35.42 \% & 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.\\
AB3DMOT & la on & 39.76 \% & 50.30 \% & 36.90 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
P2P & & 39.75 \% & 50.63 \% & 37.77 \% & 0.1 s / GPU & \\
SS3D & & 39.60 \% & 53.72 \% & 35.40 \% & 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.\\
SemanticVoxels & & 38.95 \% & 45.59 \% & 37.21 \% & 0.04 s / GPU & J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.\\
MonoLiG & & 38.92 \% & 52.66 \% & 35.05 \% & 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 & & 38.87 \% & 46.62 \% & 36.58 \% & N/A s / GPU & \\
HA PillarNet & & 38.19 \% & 47.57 \% & 35.73 \% & 0.05 s / 1 core & \\
DPM-VOC+VP & & 37.79 \% & 52.91 \% & 33.27 \% & 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.\\
StereoDistill & & 37.58 \% & 48.49 \% & 34.41 \% & 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.\\
fuf & & 37.16 \% & 48.56 \% & 33.51 \% & 10 s / 1 core & \\
MonoAuxNorm & & 37.02 \% & 50.48 \% & 32.96 \% & 0.02 s / GPU & \\
EQ-PVRCNN & & 36.49 \% & 43.67 \% & 34.67 \% & 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.\\
CG-Stereo & st & 36.47 \% & 48.23 \% & 32.77 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
TANet & & 36.21 \% & 42.54 \% & 34.39 \% & 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.\\
ODGS & & 35.75 \% & 44.76 \% & 33.14 \% & 0.1 s / 1 core & \\
YOLOStereo3D & st & 35.62 \% & 48.99 \% & 31.58 \% & 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.\\
SCNet & la & 35.49 \% & 44.50 \% & 33.38 \% & 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.\\
MonoDTR & & 35.11 \% & 49.41 \% & 31.41 \% & 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.\\
BirdNet+ & la & 35.01 \% & 41.84 \% & 33.03 \% & 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.\\
MonoEF & & 34.63 \% & 47.45 \% & 31.01 \% & 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.\\
sensekitti & & 34.26 \% & 41.03 \% & 31.51 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
DA-Net & & 34.06 \% & 43.83 \% & 31.06 \% & 0.1 s / 1 core & \\
MMpp & & 33.67 \% & 40.93 \% & 31.51 \% & 0.05 s / 1 core & \\
D4LCN & & 33.62 \% & 46.73 \% & 28.71 \% & 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.\\
DDMP-3D & & 33.35 \% & 46.12 \% & 28.45 \% & 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.\\
SparsePool & & 33.35 \% & 43.86 \% & 29.99 \% & 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.\\
SparsePool & & 33.29 \% & 43.52 \% & 30.01 \% & 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.\\
LSVM-MDPM-sv & & 33.01 \% & 45.60 \% & 29.27 \% & 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.\\
SeSame-pillar & & 32.73 \% & 40.30 \% & 30.56 \% & N/A s / TITAN RTX & \\
PointRGBNet & & 32.57 \% & 43.08 \% & 29.17 \% & 0.08 s / 4 cores & P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.\\
MVAF-Net(3-classes) & & 32.33 \% & 39.62 \% & 30.09 \% & 0.1 s / 1 core & \\
AVOD & la & 32.19 \% & 42.54 \% & 29.09 \% & 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.\\
Complexer-YOLO & la & 32.13 \% & 37.32 \% & 28.94 \% & 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.\\
RPN+BF & & 32.12 \% & 41.19 \% & 28.83 \% & 0.6 s / GPU & L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.\\
DMF & st & 32.00 \% & 39.86 \% & 30.12 \% & 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.\\
CMKD & & 31.97 \% & 42.60 \% & 29.13 \% & 0.1 s / 1 core & Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.\\
M3D-RPN & & 31.88 \% & 44.33 \% & 28.55 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
Point-GNN & la & 31.86 \% & 39.16 \% & 29.65 \% & 0.6 s / GPU & W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.\\
SubCat & & 31.26 \% & 42.31 \% & 27.39 \% & 1.2 s / 6 cores & E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.\\
SeSame-pillar w/scor & & 30.83 \% & 38.16 \% & 28.98 \% & N/A s / 1 core & \\
Aug3D-RPN & & 29.75 \% & 40.50 \% & 25.96 \% & 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.\\
TS3D & st & 29.65 \% & 42.36 \% & 25.91 \% & 0.09 s / GPU & \\
BirdNet+ (legacy) & la & 29.56 \% & 36.76 \% & 28.10 \% & 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) & & 29.31 \% & 36.05 \% & 27.41 \% & 0.1 s / 1 core & \\
SeSame-point w/score & & 28.86 \% & 39.33 \% & 26.47 \% & N/A s / GPU & \\
RT3D-GMP & st & 28.75 \% & 40.81 \% & 25.13 \% & 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.\\
CMAN & & 28.16 \% & 40.27 \% & 24.82 \% & 0.15 s / 1 core & C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.\\
Cube R-CNN & & 28.07 \% & 34.26 \% & 25.14 \% & 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.\\
CIE & & 27.84 \% & 37.65 \% & 25.24 \% & 0.1 s / 1 core & Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.\\
BKDStereo3D w/o KD & & 27.81 \% & 38.59 \% & 24.48 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
mdab & & 27.66 \% & 39.50 \% & 25.05 \% & 0.02 s / 1 core & \\
BKDStereo3D & & 27.64 \% & 38.65 \% & 23.62 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
PGD-FCOS3D & & 27.61 \% & 40.20 \% & 24.29 \% & 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.\\
FMF-occlusion-net & & 26.28 \% & 38.13 \% & 22.91 \% & 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.\\
MonoTRKDv2 & & 25.80 \% & 35.60 \% & 23.11 \% & 40 s / 1 core & \\
MonoSIM & & 25.25 \% & 35.75 \% & 22.62 \% & 0.16 s / 1 core & \\
SFEBEV & & 25.09 \% & 32.10 \% & 23.42 \% & 0.01 s / 1 core & \\
SVDM-VIEW & & 25.09 \% & 34.33 \% & 22.42 \% & 1 s / 1 core & \\
MonoFRD & & 24.92 \% & 33.47 \% & 22.38 \% & 0.01 s / 1 core & \\
DFR-Net & & 24.88 \% & 35.75 \% & 21.72 \% & 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.\\
MonoTAKD V2 & & 24.37 \% & 35.52 \% & 21.37 \% & 0.1 s / 1 core & \\
MonoLTKD & & 24.37 \% & 35.52 \% & 21.37 \% & 0.04 s / 1 core & \\
MonoTAKD & & 24.37 \% & 35.52 \% & 21.37 \% & 0.1 s / 1 core & \\
MonoLTKD\_V3 & & 24.37 \% & 35.52 \% & 21.37 \% & 0.04 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
DSGN & st & 24.32 \% & 31.21 \% & 23.09 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
ACF & & 24.31 \% & 32.23 \% & 21.70 \% & 1 s / 1 core & P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.\\
PS-fld & & 23.67 \% & 32.84 \% & 21.40 \% & 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.\\
SGM3D & & 23.54 \% & 33.73 \% & 20.50 \% & 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.\\
multi-task CNN & & 22.80 \% & 30.30 \% & 20.47 \% & 25.1 ms / GPU & M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.\\
ACF-MR & & 22.61 \% & 29.23 \% & 20.08 \% & 0.6 s / 1 core & R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.\\
OC Stereo & st & 22.02 \% & 31.36 \% & 20.20 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
BirdNet & la & 21.83 \% & 27.12 \% & 20.56 \% & 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.\\
MonoNeRD & & 20.54 \% & 28.43 \% & 18.36 \% & 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.\\
DPM-C8B1 & st & 19.17 \% & 27.79 \% & 16.48 \% & 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.\\
ESGN & st & 19.17 \% & 26.02 \% & 16.90 \% & 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.\\
RefinedMPL & & 17.26 \% & 25.83 \% & 15.41 \% & 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.\\
CaDDN & & 17.13 \% & 24.45 \% & 15.79 \% & 0.63 s / GPU & C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.\\
RT3DStereo & st & 15.34 \% & 21.41 \% & 13.23 \% & 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.\\
SST [st] & st & 13.91 \% & 20.31 \% & 12.77 \% & 1 s / 1 core & \\
MonoGhost\_Ped\_Cycl & & 8.22 \% & 11.25 \% & 8.21 \% & 0.03 s / 1 core & \\
init & & 0.02 \% & 0.02 \% & 0.02 \% & 0.03 s / 1 core & \\
mdab & & 0.02 \% & 0.02 \% & 0.02 \% & 0.02 s / 1 core & \\
DA3D+KM3D+v2-99 & & 0.00 \% & 0.00 \% & 0.00 \% & 0.120s / GPU & \\
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}