\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
PiFeNet & & 53.92 \% & 63.25 \% & 50.53 \% & 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.\\
CasA++ & & 53.84 \% & 60.14 \% & 51.35 \% & 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 & & 53.48 \% & 60.13 \% & 50.89 \% & 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.\\
IMLIDAR(base) & & 53.36 \% & 62.73 \% & 49.90 \% & 0.1 s / 1 core & \\
EQ-PVRCNN & & 52.81 \% & 61.73 \% & 49.87 \% & 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.\\
VPFNet & & 52.41 \% & 60.07 \% & 50.28 \% & 0.2 s / 1 core & C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.\\
Frustum-PointPillars & & 52.23 \% & 60.98 \% & 48.30 \% & 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.\\
LGSLNet & & 52.09 \% & 60.15 \% & 48.71 \% & 0.1 s / GPU & \\
LoGoNet & & 52.06 \% & 58.24 \% & 49.87 \% & 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.\\
TANet & & 51.38 \% & 60.85 \% & 47.54 \% & 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.\\
focalnet & & 51.38 \% & 58.82 \% & 49.23 \% & 0.05 s / 1 core & \\
CasA & & 51.37 \% & 57.95 \% & 49.08 \% & 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.\\
focalnet & & 51.34 \% & 58.94 \% & 49.21 \% & 0.05 s / 1 core & \\
SDGUFusion & & 51.00 \% & 58.58 \% & 48.72 \% & 0.5 s / 1 core & \\
MLF-DET & & 50.88 \% & 56.45 \% & 47.60 \% & 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.\\
MMLab PV-RCNN & la & 50.57 \% & 59.86 \% & 46.74 \% & 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.\\
DPPFA-Net & & 50.55 \% & 57.02 \% & 47.25 \% & 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.\\
HotSpotNet & & 50.53 \% & 57.39 \% & 46.65 \% & 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.\\
VMVS & la & 50.34 \% & 60.34 \% & 46.45 \% & 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.\\
AVOD-FPN & la & 50.32 \% & 58.49 \% & 46.98 \% & 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.\\
3DSSD & & 49.94 \% & 60.54 \% & 45.73 \% & 0.04 s / GPU & Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.\\
PointPainting & la & 49.93 \% & 58.70 \% & 46.29 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
SemanticVoxels & & 49.93 \% & 58.91 \% & 47.31 \% & 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.\\
ACDet & & 49.82 \% & 58.35 \% & 47.17 \% & 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.\\
MMLab-PartA^2 & la & 49.81 \% & 59.04 \% & 45.92 \% & 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.\\
USVLab BSAODet & & 49.75 \% & 56.05 \% & 47.59 \% & 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.\\
ACFNet & & 49.74 \% & 58.07 \% & 47.27 \% & 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.\\
F-PointNet & la & 49.57 \% & 57.13 \% & 45.48 \% & 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.\\
IOUFusion & & 49.45 \% & 57.53 \% & 45.42 \% & 0.1 s / GPU & \\
af & & 49.12 \% & 55.95 \% & 46.90 \% & 1 s / GPU & \\
F-ConvNet & la & 48.96 \% & 57.04 \% & 44.33 \% & 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.\\
HVNet & & 48.86 \% & 54.84 \% & 46.33 \% & 0.03 s / GPU & M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.\\
CAT-Det & & 48.78 \% & 57.13 \% & 45.56 \% & 0.3 s / GPU & Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.\\
STD & & 48.72 \% & 60.02 \% & 44.55 \% & 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.\\
PSMS-Net & la & 48.66 \% & 55.15 \% & 45.14 \% & 0.1 s / 1 core & \\
PointPillars & la & 48.64 \% & 57.60 \% & 45.78 \% & 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.\\
focal & & 48.56 \% & 55.56 \% & 46.42 \% & 100 s / 1 core & \\
EPNet++ & & 48.47 \% & 56.24 \% & 45.73 \% & 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.\\
MGAF-3DSSD & & 48.46 \% & 56.09 \% & 44.90 \% & 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.\\
Fast-CLOCs & & 48.27 \% & 57.19 \% & 44.55 \% & 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.\\
RPF3D & & 48.24 \% & 55.50 \% & 45.80 \% & 0.1 s / 1 core & \\
FromVoxelToPoint & & 48.15 \% & 56.54 \% & 45.63 \% & 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.\\
R^2 R-CNN & & 48.10 \% & 56.04 \% & 45.65 \% & 0.1 s / 1 core & \\
EOTL & & 47.80 \% & 56.52 \% & 43.36 \% & 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.\\
RAFDet & & 47.80 \% & 55.20 \% & 45.34 \% & 0.01 s / 1 core & \\
HMFI & & 47.77 \% & 55.61 \% & 45.17 \% & 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.\\
casxv1 & & 47.75 \% & 55.52 \% & 44.19 \% & 0.01 s / 1 core & \\
IIOU & & 47.41 \% & 54.83 \% & 43.70 \% & 0.1 s / GPU & \\
LVFSD & & 47.41 \% & 55.85 \% & 44.77 \% & 0.06 s / & ERROR: Wrong syntax in BIBTEX file.\\
P2V-RCNN & & 47.36 \% & 54.15 \% & 45.10 \% & 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.\\
CZY\_PPF\_Net & & 47.22 \% & 51.95 \% & 45.46 \% & 0.1 s / 1 core & \\
PIPC-3Ddet & & 47.22 \% & 52.71 \% & 44.02 \% & 0.05 s / 1 core & \\
Point-GNN & la & 47.07 \% & 55.36 \% & 44.61 \% & 0.6 s / GPU & W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.\\
3ONet & & 47.05 \% & 56.76 \% & 44.62 \% & 0.1 s / 1 core & H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.\\
OFFNet & & 47.05 \% & 53.85 \% & 44.75 \% & 0.1 s / GPU & \\
LGNet-3classes & & 46.89 \% & 52.46 \% & 44.77 \% & 0.11 s / 1 core & \\
F3D & & 46.77 \% & 53.69 \% & 43.89 \% & 0.01 s / 1 core & \\
SCNet & la & 46.73 \% & 56.87 \% & 42.74 \% & 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.\\
PA-Det3D & & 46.55 \% & 53.88 \% & 44.13 \% & 0.06 s / 1 core & \\
DGEnhCL & & 46.53 \% & 56.39 \% & 42.65 \% & 0.04 s / 1 core & \\
casx & & 46.44 \% & 54.61 \% & 42.83 \% & 0.01 s / 1 core & \\
PASS-PV-RCNN-Plus & & 46.36 \% & 51.47 \% & 44.10 \% & 1 s / 1 core & Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.\\
RAFDet & & 46.32 \% & 53.65 \% & 42.98 \% & 0.01 s / 1 core & \\
VPA & & 46.23 \% & 52.37 \% & 42.84 \% & 0.01 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
MMLab-PointRCNN & la & 46.13 \% & 54.77 \% & 42.84 \% & 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.\\
DA-Net & & 45.96 \% & 56.48 \% & 41.73 \% & 0.1 s / 1 core & \\
ARPNET & & 45.92 \% & 55.48 \% & 42.54 \% & 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.\\
DSA-PV-RCNN & la & 45.82 \% & 52.03 \% & 43.81 \% & 0.08 s / 1 core & P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.\\
DFSemONet(Baseline) & & 45.68 \% & 55.82 \% & 42.13 \% & 0.04 s / GPU & \\
SVGA-Net & & 45.68 \% & 53.09 \% & 43.30 \% & 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.\\
epBRM & la & 45.49 \% & 52.48 \% & 42.75 \% & 0.10 s / 1 core & K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.\\
PG-RCNN & & 45.48 \% & 51.63 \% & 43.30 \% & 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.\\
MG & & 45.46 \% & 51.71 \% & 42.09 \% & 0.1 s / 1 core & \\
PDV & & 45.45 \% & 51.95 \% & 43.33 \% & 0.1 s / 1 core & J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.\\
MLOD & la & 45.40 \% & 55.09 \% & 41.42 \% & 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.\\
HA-PillarNet & & 45.26 \% & 50.50 \% & 43.21 \% & 0.05 s / 1 core & \\
IA-SSD (single) & & 45.07 \% & 52.73 \% & 42.75 \% & 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.\\
DiffCandiDet & & 45.02 \% & 52.45 \% & 41.24 \% & 0.06 s / GPU & \\
DFAF3D & & 45.01 \% & 52.86 \% & 42.73 \% & 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.\\
SRDL & & 44.84 \% & 52.42 \% & 42.56 \% & 0.05 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
M3DeTR & & 44.78 \% & 50.63 \% & 42.57 \% & 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.\\
U\_PV\_V2\_ep100\_80 & & 44.67 \% & 49.71 \% & 42.64 \% & 0... s / 1 core & \\
Anonymous & & 44.66 \% & 53.73 \% & 42.58 \% & 0.04 s / 1 core & \\
PI-SECOND & & 44.64 \% & 54.33 \% & 40.68 \% & 0.05 s / GPU & \\
PV-RCNN-Plus & & 44.61 \% & 51.20 \% & 42.55 \% & 1 s / 1 core & \\
TF-PartA2 & & 44.47 \% & 53.22 \% & 41.88 \% & 0.1 s / 1 core & \\
U\_second\_v4\_ep\_100\_8 & & 44.46 \% & 51.36 \% & 42.47 \% & 0.1 s / 1 core & \\
u\_second\_v4\_epoch\_10 & & 44.32 \% & 50.97 \% & 42.55 \% & 0.1 s / 1 core & \\
MSAW & & 44.28 \% & 54.36 \% & 40.72 \% & 0.42 s / 2 cores & \\
SIF & & 44.28 \% & 52.05 \% & 42.03 \% & 0.1 s / 1 core & P. An: SIF. Submitted to CVIU 2021.\\
BAPartA2S-4h & & 44.18 \% & 53.05 \% & 41.54 \% & 0.1 s / 1 core & \\
bs & & 44.18 \% & 50.59 \% & 41.92 \% & 0.1 s / 1 core & \\
U\_PV\_V2\_ep\_100\_100 & & 44.18 \% & 49.62 \% & 42.44 \% & 0.1 s / 1 core & \\
DVFENet & & 44.12 \% & 50.98 \% & 41.62 \% & 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.\\
Faraway-Frustum & la & 43.85 \% & 52.15 \% & 41.68 \% & 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.\\
RAFDet & & 43.81 \% & 51.37 \% & 41.62 \% & 0.1 s / 1 core & \\
HAF-PVP\_test & & 43.70 \% & 50.24 \% & 40.12 \% & 0.09 s / 1 core & \\
MLAFF & & 43.67 \% & 53.43 \% & 41.41 \% & 0.39 s / 2 cores & \\
SC-SSD & & 43.64 \% & 50.05 \% & 41.56 \% & 1 s / 1 core & \\
DDF & & 43.64 \% & 52.64 \% & 41.42 \% & 0.1 s / 1 core & \\
PR-SSD & & 43.58 \% & 50.38 \% & 41.36 \% & 0.02 s / GPU & \\
GF-pointnet & & 43.47 \% & 50.12 \% & 41.30 \% & 0.02 s / 1 core & \\
GeVo & & 43.46 \% & 47.87 \% & 41.53 \% & 0.05 s / 1 core & \\
S-AT GCN & & 43.43 \% & 50.63 \% & 41.58 \% & 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.\\
AAMVFNet & & 43.29 \% & 49.38 \% & 40.33 \% & 0.04 s / GPU & \\
BirdNet+ & la & 42.87 \% & 48.90 \% & 40.59 \% & 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.\\
L-AUG & & 42.84 \% & 50.32 \% & 40.29 \% & 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.\\
XT-PartA2 & & 42.68 \% & 50.62 \% & 40.25 \% & 0.1 s / GPU & \\
IA-SSD (multi) & & 42.61 \% & 51.76 \% & 40.51 \% & 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.\\
AMVFNet & & 42.61 \% & 50.04 \% & 39.35 \% & 0.04 s / GPU & \\
prcnn\_v18\_80\_100 & & 42.48 \% & 50.92 \% & 38.81 \% & 0.1 s / 1 core & \\
XView & & 42.42 \% & 47.24 \% & 39.96 \% & 0.1 s / 1 core & L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.\\
PVTr & & 42.14 \% & 48.76 \% & 40.08 \% & 0.1 s / 1 core & \\
GraphAlign & & 41.95 \% & 46.61 \% & 40.05 \% & 0.03 s / GPU & \\
APDM & & 41.66 \% & 50.19 \% & 39.04 \% & 0.7 s / 1 core & \\
SeSame-voxel & & 41.59 \% & 50.12 \% & 37.79 \% & N/A s / TITAN RTX & \\
HINTED & & 41.55 \% & 53.09 \% & 39.18 \% & 0.04 s / 1 core & \\
MVAF-Net(3-classes) & & 41.47 \% & 50.56 \% & 38.90 \% & 0.1 s / 1 core & \\
mm3d\_PartA2 & & 41.24 \% & 48.45 \% & 38.92 \% & 0.1 s / GPU & \\
SeSame-point & & 41.22 \% & 48.25 \% & 39.18 \% & N/A s / TITAN RTX & \\
PFF3D & la & 40.94 \% & 48.74 \% & 38.54 \% & 0.05 s / GPU & L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.\\
HA PillarNet & & 40.73 \% & 49.38 \% & 38.11 \% & 0.05 s / 1 core & \\
MMpointpillars & & 40.43 \% & 47.85 \% & 37.68 \% & 0.05 s / 1 core & \\
VSAC & & 40.37 \% & 49.91 \% & 36.64 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
IIOU\_LDR & & 40.24 \% & 48.51 \% & 37.03 \% & 0.03 s / 1 core & \\
MM\_SECOND & & 40.22 \% & 49.46 \% & 37.46 \% & 0.05 s / GPU & \\
ROT\_S3D & & 40.08 \% & 46.62 \% & 38.33 \% & 0.1 s / GPU & \\
DSGN++ & st & 38.92 \% & 50.26 \% & 35.12 \% & 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.\\
MVAF-Net(3-classes) & & 38.87 \% & 46.92 \% & 36.56 \% & 0.1 s / 1 core & \\
AB3DMOT & la on & 38.79 \% & 47.51 \% & 35.85 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
BirdNet+ (legacy) & la & 38.28 \% & 45.53 \% & 35.37 \% & 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.\\
MMpp & & 38.07 \% & 45.74 \% & 35.75 \% & 0.05 s / 1 core & \\
CSW3D & la & 37.96 \% & 49.27 \% & 33.83 \% & 0.03 s / 4 cores & J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.\\
StereoDistill & & 37.75 \% & 50.79 \% & 34.28 \% & 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.\\
SeSame-pillar & & 37.31 \% & 44.21 \% & 35.17 \% & N/A s / TITAN RTX & \\
P2P & & 36.71 \% & 44.67 \% & 34.86 \% & 0.1 s / GPU & \\
DMF & st & 34.92 \% & 42.08 \% & 32.69 \% & 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.\\
SparsePool & & 34.15 \% & 43.33 \% & 31.78 \% & 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.\\
MMLAB LIGA-Stereo & st & 34.13 \% & 44.71 \% & 30.42 \% & 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.\\
SeSame-voxel w/score & & 33.76 \% & 39.42 \% & 31.31 \% & N/A s / GPU & \\
AVOD & la & 33.57 \% & 42.58 \% & 30.14 \% & 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.\\
SparsePool & & 33.22 \% & 41.55 \% & 29.66 \% & 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.\\
fuf & & 33.16 \% & 41.95 \% & 29.75 \% & 10 s / 1 core & \\
SeSame-pillar w/scor & & 32.78 \% & 39.11 \% & 30.87 \% & N/A s / 1 core & \\
ODGS & & 31.23 \% & 37.54 \% & 28.97 \% & 0.1 s / 1 core & \\
FastDet & & 31.04 \% & 38.14 \% & 29.09 \% & 0.01 s / 1 core & \\
CG-Stereo & st & 29.56 \% & 39.24 \% & 25.87 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
PointRGBNet & & 29.32 \% & 38.07 \% & 26.94 \% & 0.08 s / 4 cores & P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.\\
Disp R-CNN & st & 29.12 \% & 42.72 \% & 25.09 \% & 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 (velo) & st & 28.34 \% & 40.21 \% & 24.46 \% & 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.\\
SeSame-point w/score & & 25.79 \% & 33.98 \% & 22.50 \% & N/A s / GPU & \\
BirdNet & la & 23.06 \% & 28.20 \% & 21.65 \% & 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.\\
TS3D & st & 21.69 \% & 32.90 \% & 19.27 \% & 0.09 s / GPU & \\
OC Stereo & st & 20.80 \% & 29.79 \% & 18.62 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
YOLOStereo3D & st & 20.76 \% & 31.01 \% & 18.41 \% & 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.\\
DSGN & st & 20.75 \% & 26.61 \% & 18.86 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
Complexer-YOLO & la & 18.26 \% & 21.42 \% & 17.06 \% & 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.\\
BKDStereo3D & & 17.44 \% & 25.47 \% & 14.44 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
BKDStereo3D w/o KD & & 16.87 \% & 23.82 \% & 14.85 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
TopNet-Retina & la & 14.57 \% & 18.04 \% & 12.48 \% & 52ms / & S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
RT3D-GMP & st & 14.22 \% & 19.92 \% & 12.83 \% & 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.\\
MonoLTKD\_V3 & & 13.62 \% & 19.79 \% & 11.92 \% & 0.04 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
TopNet-HighRes & la & 13.50 \% & 19.43 \% & 11.93 \% & 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.\\
MonoTAKD V2 & & 13.47 \% & 19.67 \% & 11.75 \% & 0.1 s / 1 core & \\
ESGN & st & 13.03 \% & 17.94 \% & 11.54 \% & 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.\\
SST [st] & st & 12.66 \% & 19.18 \% & 11.07 \% & 1 s / 1 core & \\
DD3D & & 12.51 \% & 18.58 \% & 10.65 \% & 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) .\\
MonoLSS & & 12.34 \% & 18.40 \% & 10.54 \% & 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.\\
PS-fld & & 12.23 \% & 19.03 \% & 10.53 \% & 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.\\
MonoTAKD & & 12.15 \% & 18.23 \% & 10.50 \% & 0.1 s / 1 core & \\
CIE & & 11.94 \% & 17.90 \% & 10.34 \% & 0.1 s / 1 core & Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.\\
MonoLTKD & & 11.85 \% & 17.74 \% & 10.26 \% & 0.04 s / 1 core & \\
SVDM-VIEW & & 11.11 \% & 16.66 \% & 9.46 \% & 1 s / 1 core & \\
OPA-3D & & 11.01 \% & 17.14 \% & 9.94 \% & 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.\\
MonoUNI & & 10.90 \% & 16.54 \% & 9.17 \% & 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.\\
MonoDTR & & 10.59 \% & 16.66 \% & 9.00 \% & 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.\\
MonoFRD & & 10.38 \% & 15.68 \% & 8.79 \% & 0.01 s / 1 core & \\
GUPNet & & 10.37 \% & 15.62 \% & 8.79 \% & 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.\\
CMKD & & 10.28 \% & 16.03 \% & 8.85 \% & 0.1 s / 1 core & Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.\\
MonoSIM & & 10.08 \% & 15.80 \% & 8.64 \% & 0.16 s / 1 core & \\
MonoInsight & & 9.98 \% & 15.20 \% & 8.90 \% & 0.03 s / 1 core & \\
MonoInsight & & 9.98 \% & 15.20 \% & 8.90 \% & 0.03 s / 1 core & \\
DEVIANT & & 9.77 \% & 14.49 \% & 8.28 \% & 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.\\
MonoNeRD & & 9.66 \% & 15.27 \% & 8.28 \% & 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.\\
CaDDN & & 9.41 \% & 14.72 \% & 8.17 \% & 0.63 s / GPU & C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.\\
SGM3D & & 9.39 \% & 15.39 \% & 8.61 \% & 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.\\
MonoRCNN++ & & 9.04 \% & 13.45 \% & 7.74 \% & 0.07 s / GPU & X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.\\
HomoLoss(monoflex) & & 8.81 \% & 13.26 \% & 7.41 \% & 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 & & 8.62 \% & 13.13 \% & 7.27 \% & 0.03 s / 1 core & \\
MonoDDE & & 8.41 \% & 12.38 \% & 7.16 \% & 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.\\
Mix-Teaching & & 8.40 \% & 12.34 \% & 7.06 \% & 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.\\
MDSNet & & 8.18 \% & 12.05 \% & 7.03 \% & 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.\\
LPCG-Monoflex & & 7.92 \% & 12.11 \% & 6.61 \% & 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.\\
RefinedMPL & & 7.92 \% & 13.09 \% & 7.25 \% & 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.\\
Cube R-CNN & & 7.65 \% & 11.67 \% & 6.60 \% & 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.\\
MonoRUn & & 7.59 \% & 11.70 \% & 6.34 \% & 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.\\
MonoFlex & & 7.36 \% & 10.36 \% & 6.29 \% & 0.03 s / GPU & Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.\\
MonoTRKDv2 & & 7.22 \% & 11.05 \% & 6.11 \% & 40 s / 1 core & \\
DA3D+KM3D+v2-99 & & 7.06 \% & 10.32 \% & 6.10 \% & 0.120s / GPU & \\
MonoPair & & 7.04 \% & 10.99 \% & 6.29 \% & 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.\\
monodle & & 6.96 \% & 10.73 \% & 6.20 \% & 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 .\\
MonOAPC & & 6.82 \% & 9.62 \% & 5.78 \% & 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.\\
SH3D & & 6.75 \% & 10.26 \% & 5.61 \% & 0.1 s / 1 core & \\
TopNet-DecayRate & la & 6.59 \% & 8.78 \% & 6.25 \% & 92 ms / & S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
MonoAIU & & 6.43 \% & 9.55 \% & 5.39 \% & 0.03 s / GPU & \\
Anonymous & & 6.25 \% & 9.22 \% & 5.32 \% & 0.1 s / 1 core & \\
Shift R-CNN (mono) & & 5.66 \% & 8.58 \% & 4.49 \% & 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.\\
FMF-occlusion-net & & 5.62 \% & 8.69 \% & 5.25 \% & 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 & & 5.22 \% & 7.14 \% & 4.21 \% & 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.\\
TopNet-UncEst & la & 4.60 \% & 6.88 \% & 3.79 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
MonoPSR & & 4.56 \% & 7.24 \% & 4.11 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
DFR-Net & & 4.52 \% & 6.66 \% & 3.71 \% & 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.\\
MonoGhost\_Ped\_Cycl & & 4.49 \% & 7.14 \% & 4.39 \% & 0.03 s / 1 core & \\
QD-3DT & on & 4.23 \% & 6.62 \% & 3.39 \% & 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.\\
DA3D+KM3D & & 4.05 \% & 5.94 \% & 3.55 \% & 0.02 s / GPU & \\
M3D-RPN & & 4.05 \% & 5.65 \% & 3.29 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
DDMP-3D & & 4.02 \% & 5.53 \% & 3.36 \% & 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.\\
CMAN & & 3.96 \% & 5.24 \% & 3.18 \% & 0.15 s / 1 core & C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.\\
D4LCN & & 3.86 \% & 5.06 \% & 3.59 \% & 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.\\
RT3DStereo & st & 3.65 \% & 4.72 \% & 3.00 \% & 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.\\
DA3D & & 3.27 \% & 4.93 \% & 2.74 \% & 0.03 s / 1 core & \\
MonoEF & & 3.05 \% & 4.61 \% & 2.85 \% & 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.\\
MonoLiG & & 2.72 \% & 3.74 \% & 2.55 \% & 0.03 s / 1 core & A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.\\
mdab & & 2.43 \% & 3.68 \% & 2.26 \% & 0.02 s / 1 core & \\
SS3D & & 2.09 \% & 2.48 \% & 1.61 \% & 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.\\
SparVox3D & & 2.05 \% & 2.90 \% & 1.69 \% & 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.\\
PGD-FCOS3D & & 1.88 \% & 2.82 \% & 1.54 \% & 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.\\
MonoAuxNorm & & 1.24 \% & 1.58 \% & 0.96 \% & 0.02 s / GPU & \\
Plane-Constraints & & 1.16 \% & 1.87 \% & 1.13 \% & 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.\\
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}