\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
CasA++ & & 49.29 \% & 56.33 \% & 46.70 \% & 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 & & 49.21 \% & 55.85 \% & 46.52 \% & 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.\\
3D HA Net & & 48.90 \% & 56.27 \% & 46.25 \% & 0.1 s / 1 core & Q. Xia, Y. Chen, G. Cai, G. Chen, D. Xie, J. Su and Z. Wang: 3D HANet: A Flexible 3D Heatmap Auxiliary Network for Object Detection. IEEE Transactions on Geoscience and Remote Sensing 2023.\\
BiProDet & & 48.77 \% & 55.59 \% & 46.12 \% & 0.1 s / GPU & \\
VPFNet & & 48.36 \% & 54.65 \% & 44.98 \% & 0.2 s / 1 core & C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.\\
LoGoNet & & 47.43 \% & 53.07 \% & 45.22 \% & 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.\\
DCAN-Second & & 47.38 \% & 55.12 \% & 44.59 \% & 0.05 s / 1 core & \\
CasA & & 47.09 \% & 54.04 \% & 44.56 \% & 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.\\
EQ-PVRCNN & & 47.02 \% & 55.84 \% & 42.94 \% & 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.\\
PiFeNet & & 46.71 \% & 56.39 \% & 42.71 \% & 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.\\
USVLab BSAODet & & 46.50 \% & 52.69 \% & 43.10 \% & 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.\\
ACF-Net & & 46.36 \% & 54.62 \% & 42.57 \% & n/a s / 1 core & \\
IMLIDAR(base) & & 46.25 \% & 55.55 \% & 42.33 \% & 0.1 s / 1 core & \\
RPPF-Net & & 46.14 \% & 53.58 \% & 42.59 \% & 0.1 s / 1 core & \\
MPFusion & & 45.45 \% & 53.19 \% & 42.66 \% & 0.1 s / 1 core & \\
CAT-Det & & 45.44 \% & 54.26 \% & 41.94 \% & 0.3 s / GPU & Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.\\
HotSpotNet & & 45.37 \% & 53.10 \% & 41.47 \% & 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.\\
VPNetv2 & & 45.33 \% & 52.73 \% & 42.75 \% & 0.1 s / 1 core & \\
ACDet & & 44.79 \% & 53.41 \% & 41.96 \% & 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.\\
SPT & & 44.72 \% & 51.35 \% & 41.38 \% & 0.1 s / GPU & \\
EPNet++ & & 44.38 \% & 52.79 \% & 41.29 \% & 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 & & 44.34 \% & 53.72 \% & 40.49 \% & 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.\\
3DSSD & & 44.27 \% & 54.64 \% & 40.23 \% & 0.04 s / GPU & Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.\\
LGSL & & 44.11 \% & 51.97 \% & 41.91 \% & 0.1 s / GPU & \\
RFA & & 43.93 \% & 52.28 \% & 40.81 \% & 0.1s / GPU & \\
HPV-RCNN & & 43.86 \% & 52.54 \% & 41.56 \% & 0.08 s / 1 core & \\
Point-GNN & la & 43.77 \% & 51.92 \% & 40.14 \% & 0.6 s / GPU & W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.\\
PA-RCNN & & 43.57 \% & 51.25 \% & 40.35 \% & 0.05 s / 1 core & \\
Anomynous & & 43.57 \% & 53.02 \% & 39.86 \% & 0.09 s / 1 core & \\
3ONet & & 43.57 \% & 53.02 \% & 39.86 \% & 0.09 s / 1 core & \\
F-ConvNet & la & 43.38 \% & 52.16 \% & 38.80 \% & 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.\\
MMLab-PartA^2 & la & 43.35 \% & 53.10 \% & 40.06 \% & 0.08 s / GPU & S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.\\
MMF & & 43.30 \% & 51.39 \% & 39.67 \% & 1 s / 1 core & \\
MMLab PV-RCNN & la & 43.29 \% & 52.17 \% & 40.29 \% & 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.\\
FromVoxelToPoint & & 43.28 \% & 51.80 \% & 40.71 \% & 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.\\
VMVS & la & 43.27 \% & 53.44 \% & 39.51 \% & 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.\\
RPF3D & & 43.27 \% & 51.60 \% & 40.84 \% & 0.1 s / 1 core & \\
P2V-RCNN & & 43.19 \% & 50.91 \% & 40.81 \% & 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.\\
MGAF-3DSSD & & 43.09 \% & 50.65 \% & 39.65 \% & 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.\\
Frustum-PointPillars & & 42.89 \% & 51.22 \% & 39.28 \% & 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.\\
PIPC-3Ddet & & 42.83 \% & 48.51 \% & 40.73 \% & 0.05 s / 1 core & \\
PSA-Det3D & & 42.81 \% & 49.72 \% & 39.58 \% & 0.1 s / GPU & \\
IA-SSDx & & 42.79 \% & 50.86 \% & 39.23 \% & 0.01 s / 1 core & \\
casx & & 42.79 \% & 50.86 \% & 39.23 \% & 0.01 s / 1 core & \\
DA-Net & & 42.78 \% & 53.20 \% & 38.67 \% & 0.1 s / 1 core & \\
Fast-CLOCs & & 42.72 \% & 52.10 \% & 39.08 \% & 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.\\
Anonymous & & 42.72 \% & 51.16 \% & 39.06 \% & 0.1 s / 1 core & \\
HMFI & & 42.65 \% & 50.88 \% & 39.78 \% & 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.\\
STD & & 42.47 \% & 53.29 \% & 38.35 \% & 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.\\
POP-RCNN & & 42.45 \% & 50.22 \% & 39.18 \% & 0.1 s / 1 core & \\
AVOD-FPN & la & 42.27 \% & 50.46 \% & 39.04 \% & 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.\\
Anonymous & la & 42.25 \% & 49.06 \% & 40.02 \% & 0.05 s / GPU & \\
F3D & & 42.21 \% & 49.08 \% & 39.19 \% & 0.01 s / 1 core & \\
SemanticVoxels & & 42.19 \% & 50.90 \% & 39.52 \% & 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.\\
F-PointNet & la & 42.15 \% & 50.53 \% & 38.08 \% & 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.\\
Anonymous & & 42.09 \% & 49.06 \% & 38.73 \% & 0.1 s / 1 core & \\
DTE3D & & 41.97 \% & 49.91 \% & 39.27 \% & 0.15s / 1 core & \\
CZY\_PPF\_Net2 & & 41.93 \% & 47.18 \% & 40.08 \% & 0.1 s / 1 core & \\
PointPillars & la & 41.92 \% & 51.45 \% & 38.89 \% & 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.\\
FEMV-RCNN & & 41.89 \% & 48.18 \% & 38.81 \% & 0.03 s / 1 core & \\
PV-PMRTNet & & 41.68 \% & 46.32 \% & 38.98 \% & 0.1 s / 1 core & \\
MVENet & & 41.55 \% & 48.66 \% & 39.29 \% & 0.02 s / 1 core & \\
epBRM & la & 41.52 \% & 49.17 \% & 39.08 \% & 0.10 s / 1 core & K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.\\
SGDA3D & & 41.39 \% & 47.59 \% & 38.37 \% & 0.07 s / 1 core & \\
CZY\_3917 & & 41.38 \% & 46.09 \% & 38.64 \% & 0.1 s / 1 core & \\
PA-Det3D & & 41.36 \% & 48.48 \% & 38.92 \% & 0.06 s / 1 core & \\
DGT-Det3D & & 41.07 \% & 48.79 \% & 38.09 \% & 0.02 s / 1 core & \\
IA-SSD (single) & & 41.03 \% & 47.90 \% & 37.98 \% & 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.\\
DFAF3D & & 40.99 \% & 47.58 \% & 37.65 \% & 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.\\
PointPainting & la & 40.97 \% & 50.32 \% & 37.87 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
DSA-PV-RCNN & la & 40.89 \% & 46.97 \% & 38.80 \% & 0.08 s / 1 core & P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.\\
Rnet & & 40.88 \% & 46.85 \% & 38.75 \% & 0.1 s / 1 core & \\
B2PE & & 40.72 \% & 48.02 \% & 37.67 \% & 0.02 s / 1 core & \\
PDV & & 40.56 \% & 47.80 \% & 38.46 \% & 0.1 s / 1 core & J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.\\
MVMM & & 40.49 \% & 47.54 \% & 38.36 \% & 0.04 s / GPU & \\
Under Blind Review#2 & & 40.47 \% & 46.61 \% & 38.60 \% & 0.1 s / 1 core & \\
U\_SECOND\_V4 & & 40.40 \% & 48.46 \% & 37.40 \% & 0.1 s / 1 core & \\
SVGA-Net & & 40.39 \% & 48.48 \% & 37.92 \% & 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.\\
CZY\_PPF\_Net & & 40.28 \% & 46.03 \% & 38.19 \% & 0.1 s / 1 core & \\
RealSynthesis-SECOND & & 40.27 \% & 47.23 \% & 37.15 \% & 0.05 s / 1 core & \\
U\_PVRCNN\_V2 & & 40.26 \% & 47.10 \% & 37.42 \% & 0.1 s / 1 core & \\
Semantical PVRCNN & & 40.18 \% & 45.94 \% & 37.28 \% & 0.07 s / 1 core & \\
EOTL & & 40.11 \% & 48.65 \% & 35.99 \% & TBD s / 1 core & \\
M3DeTR & & 39.94 \% & 45.70 \% & 37.66 \% & 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.\\
VPNet & & 39.67 \% & 47.55 \% & 36.66 \% & 0.1 s / 1 core & \\
IKT3D & la & 39.53 \% & 45.34 \% & 37.14 \% & 0.05 s / 1 core & \\
WGVRF & & 39.52 \% & 45.98 \% & 37.56 \% & 0.1 s / 1 core & \\
U\_RVRCNN\_V2\_1 & & 39.50 \% & 46.42 \% & 37.29 \% & 0.1 s / 1 core & \\
VGA-RCNN & & 39.48 \% & 47.80 \% & 36.99 \% & 0.07 s / 1 core & \\
SRDL & & 39.43 \% & 47.30 \% & 36.99 \% & 0.05 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
PVRCNN\_8369 & & 39.41 \% & 47.30 \% & 36.96 \% & 0.1 s / 1 core & \\
MMLab-PointRCNN & la & 39.37 \% & 47.98 \% & 36.01 \% & 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.\\
ARPNET & & 39.31 \% & 48.32 \% & 35.93 \% & 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.\\
CZY & & 39.26 \% & 45.08 \% & 36.38 \% & 0.1 s / 1 core & \\
IA-SSD (multi) & & 39.03 \% & 46.51 \% & 35.61 \% & 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.\\
GS & & 38.90 \% & 45.25 \% & 35.87 \% & TBD s / 1 core & \\
BASA & & 38.90 \% & 46.74 \% & 36.24 \% & 1s / 1 core & \\
PSA-SSD & & 38.87 \% & 46.21 \% & 36.85 \% & 0.01 s / 1 core & \\
IPS & & 38.82 \% & 46.37 \% & 36.63 \% & TBD s / 1 core & \\
DTSSD & & 38.75 \% & 45.03 \% & 36.70 \% & 0.1 s / 1 core & \\
SIF & & 38.74 \% & 46.23 \% & 36.06 \% & 0.1 s / 1 core & P. An: SIF. Submitted to CVIU 2021.\\
HybridPillars & & 38.67 \% & 44.11 \% & 36.47 \% & 0.05 s / 1 core & \\
SCNet & la & 38.66 \% & 47.83 \% & 35.70 \% & 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.\\
GS-FPS & & 38.61 \% & 45.43 \% & 35.63 \% & TBD s / 1 core & \\
Faraway-Frustum & la & 38.58 \% & 46.33 \% & 35.71 \% & 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.\\
AGS-SSD[la] & & 38.53 \% & 46.10 \% & 35.40 \% & 0.04 s / 1 core & \\
MSAW & & 38.42 \% & 48.12 \% & 35.11 \% & 0.42 s / 2 cores & \\
MLAFF & & 38.41 \% & 47.81 \% & 36.15 \% & 0.39 s / 2 cores & \\
STNet & & 38.41 \% & 46.19 \% & 36.27 \% & 0.60 s / 1 core & \\
GEO\_LOC & & 38.31 \% & 45.87 \% & 35.34 \% & TBD s / 1 core & \\
HybridPillars (SSD) & & 38.16 \% & 44.81 \% & 36.06 \% & 0.02 s / 1 core & \\
GS-FPS-LT & & 38.10 \% & 44.05 \% & 35.75 \% & TBD s / 1 core & \\
OA-TSSD & & 37.97 \% & 46.12 \% & 35.05 \% & 20 s / 8 cores & \\
ACCF & & 37.91 \% & 44.97 \% & 35.63 \% & 0.02 s / 1 core & \\
NV-RCNN & & 37.82 \% & 44.38 \% & 35.55 \% & 0.1 s / 1 core & \\
PEF & & 37.78 \% & 45.16 \% & 34.34 \% & N/A s / 1 core & \\
DTSSD & & 37.77 \% & 44.55 \% & 34.97 \% & 0.1 s / 1 core & \\
SWA & & 37.76 \% & 44.59 \% & 34.82 \% & 0.18 s / 1 core & \\
PVTr & & 37.75 \% & 44.36 \% & 35.53 \% & 0.1 s / 1 core & \\
DVFENet & & 37.50 \% & 43.55 \% & 35.33 \% & 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.\\
MLOD & la & 37.47 \% & 47.58 \% & 35.07 \% & 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.\\
S-AT GCN & & 37.37 \% & 44.63 \% & 34.92 \% & 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.\\
T\_PVRCNN & & 37.12 \% & 45.20 \% & 34.04 \% & 0.1 s / 1 core & \\
ATT\_SSD & & 37.03 \% & 44.14 \% & 34.94 \% & 0.01 s / 1 core & \\
GraphAlign & & 36.89 \% & 41.38 \% & 34.95 \% & 0.03 s / GPU & \\
T\_PVRCNN\_V2 & & 36.79 \% & 44.81 \% & 33.83 \% & 0.1 s / 1 core & \\
XView & & 36.79 \% & 42.44 \% & 34.96 \% & 0.1 s / 1 core & L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.\\
TTT\_SSD & & 36.26 \% & 43.22 \% & 34.31 \% & TBD s / 1 core & \\
PFF3D & la & 36.07 \% & 43.93 \% & 32.86 \% & 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.\\
SECOND\_7862 & & 35.92 \% & 43.04 \% & 33.56 \% & 1 s / 1 core & \\
MVAF-Net(3-classes) & & 35.87 \% & 44.50 \% & 32.86 \% & 0.1 s / 1 core & \\
BirdNet+ & la & 35.06 \% & 41.55 \% & 32.93 \% & 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.\\
APDM & & 34.94 \% & 43.05 \% & 32.22 \% & 0.7 s / 1 core & \\
AB3DMOT & la on & 34.59 \% & 42.27 \% & 31.37 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
LightCPC & & 34.10 \% & 39.59 \% & 31.47 \% & 0.02 s / 1 core & \\
CAD & st la & 33.67 \% & 41.35 \% & 31.28 \% & 0.1 s / 1 core & \\
HA-PillarNet & & 32.75 \% & 40.82 \% & 30.19 \% & 0.05 s / 1 core & \\
DSGN++ & st & 32.74 \% & 43.05 \% & 29.54 \% & 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) & & 32.60 \% & 39.62 \% & 30.05 \% & 0.1 s / 1 core & \\
Voxel-MAE+SECOND & & 32.60 \% & 39.18 \% & 30.62 \% & 0.05 s / 1 core & \\
ZMMPP & & 32.38 \% & 39.54 \% & 30.25 \% & 0.1 s / 1 core & \\
StereoDistill & & 32.23 \% & 44.12 \% & 28.95 \% & 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.\\
BirdNet+ (legacy) & la & 31.46 \% & 37.99 \% & 29.46 \% & 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.\\
SparsePool & & 30.38 \% & 37.84 \% & 26.94 \% & 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 & 30.00 \% & 40.46 \% & 27.07 \% & 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.\\
fuf & & 29.82 \% & 37.84 \% & 26.53 \% & 10 s / 1 core & \\
DMF & st & 29.77 \% & 37.21 \% & 27.62 \% & 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 & & 27.92 \% & 35.52 \% & 25.87 \% & 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.\\
AVOD & la & 27.86 \% & 36.10 \% & 25.76 \% & 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.\\
PS++ & & 26.71 \% & 36.00 \% & 23.47 \% & 0.4 s / 1 core & \\
CSW3D & la & 26.64 \% & 33.75 \% & 23.34 \% & 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.\\
PointRGBNet & & 26.40 \% & 34.77 \% & 24.03 \% & 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 (velo) & st & 25.80 \% & 37.12 \% & 22.04 \% & 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 & 25.40 \% & 35.75 \% & 21.79 \% & 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.\\
CG-Stereo & st & 24.31 \% & 33.22 \% & 20.95 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
DSC3D & st & 20.35 \% & 29.54 \% & 18.03 \% & 0.31 s / GPU & \\
YOLOStereo3D & st & 19.75 \% & 28.49 \% & 16.48 \% & 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.\\
TS3D & st & 19.56 \% & 29.17 \% & 17.20 \% & 0.09 s / GPU & \\
OC Stereo & st & 17.58 \% & 24.48 \% & 15.60 \% & 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 & 17.08 \% & 22.04 \% & 15.82 \% & 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.\\
DSGN & st & 15.55 \% & 20.53 \% & 14.15 \% & 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 & 13.96 \% & 17.60 \% & 12.70 \% & 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.\\
RT3D-GMP & st & 11.41 \% & 16.23 \% & 10.12 \% & 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.\\
MonoLSS & & 11.27 \% & 17.09 \% & 10.00 \% & 0.04 s / 1 core & \\
DD3D & & 11.04 \% & 16.64 \% & 9.38 \% & 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) .\\
DD3Dv2 & & 10.82 \% & 16.25 \% & 9.24 \% & 0.1 s / 1 core & \\
PS-fld & & 10.82 \% & 16.95 \% & 9.26 \% & 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.\\
CIE & & 10.53 \% & 16.19 \% & 8.97 \% & 0.1 s / 1 core & Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.\\
OPA-3D & & 10.49 \% & 15.65 \% & 8.80 \% & 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.\\
BAIR & & 10.34 \% & 15.71 \% & 8.60 \% & 0.04 s / 1 core & \\
MonoUNI & & 10.34 \% & 15.78 \% & 8.74 \% & 0.04 s / 1 core & \\
ESGN & st & 10.27 \% & 14.05 \% & 9.02 \% & 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.\\
MonoDTR & & 10.18 \% & 15.33 \% & 8.61 \% & 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.\\
MonoInsight & & 10.01 \% & 15.17 \% & 9.06 \% & 0.03 s / 1 core & \\
GUPNet & & 9.76 \% & 14.95 \% & 8.41 \% & 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.\\
DD3D-dequity & & 9.57 \% & 14.28 \% & 8.20 \% & 0.1 s / 1 core & \\
MonoInsight & & 9.42 \% & 14.41 \% & 7.96 \% & 0.03 s / 1 core & \\
MonoInsight & & 9.42 \% & 14.41 \% & 7.96 \% & 0.03 s / 1 core & \\
BSM3D & & 9.37 \% & 14.05 \% & 7.92 \% & 0.03 s / 1 core & \\
OccupancyM3D & & 9.15 \% & 14.68 \% & 7.80 \% & 0.11 s / 1 core & \\
BCA & & 8.85 \% & 13.60 \% & 8.05 \% & 0.17 s / GPU & \\
MonoAD & & 8.84 \% & 13.85 \% & 7.38 \% & 0.03 s / GPU & \\
SGM3D & & 8.81 \% & 13.99 \% & 7.26 \% & 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.\\
CMKD & & 8.79 \% & 13.94 \% & 7.42 \% & 0.1 s / 1 core & Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.\\
AMNet & & 8.67 \% & 13.18 \% & 7.43 \% & 0.03 s / GPU & \\
DEVIANT & & 8.65 \% & 13.43 \% & 7.69 \% & 0.04 s / & A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.\\
MonoPCNS & & 8.63 \% & 14.16 \% & 7.30 \% & 0.14 s / GPU & \\
MonoA^2 & & 8.51 \% & 12.95 \% & 7.56 \% & na s / 1 core & \\
MM3D & & 8.47 \% & 13.65 \% & 7.05 \% & NA s / 1 core & \\
Mono3DMethod & & 8.37 \% & 13.38 \% & 6.91 \% & 0.1 s / 1 core & \\
MonoXiver & & 8.32 \% & 12.70 \% & 7.04 \% & 0.03s / GPU & \\
MM3DV2 & & 8.26 \% & 13.51 \% & 7.16 \% & NA s / 1 core & \\
MonoNeRD & & 8.26 \% & 13.20 \% & 7.02 \% & na s / 1 core & \\
CaDDN & & 8.14 \% & 12.87 \% & 6.76 \% & 0.63 s / GPU & C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.\\
Anonymous & & 8.04 \% & 12.18 \% & 6.58 \% & 0.03 s / 1 core & \\
MonoRCNN++ & & 7.90 \% & 12.26 \% & 6.62 \% & 0.07 s / GPU & X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.\\
SparseLiDAR\_fusion & & 7.71 \% & 11.41 \% & 6.38 \% & 0.08 s / 1 core & \\
HomoLoss(monoflex) & & 7.66 \% & 11.87 \% & 6.82 \% & 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.\\
Mix-Teaching & & 7.47 \% & 11.67 \% & 6.61 \% & 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.\\
LPCG-Monoflex & & 7.33 \% & 10.82 \% & 6.18 \% & 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.\\
MonoDDE & & 7.32 \% & 11.13 \% & 6.67 \% & 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.\\
3DSeMoDLE & & 7.26 \% & 10.78 \% & 6.05 \% & 0.1 s / 1 core & \\
RefinedMPL & & 7.18 \% & 11.14 \% & 5.84 \% & 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.\\
UNM3D & & 7.13 \% & 11.25 \% & 6.00 \% & na s / 1 core & \\
MDSNet & & 7.09 \% & 10.68 \% & 6.06 \% & 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.\\
Anonymous & & 6.95 \% & 10.98 \% & 5.85 \% & 0.1 s / 1 core & \\
Cube R-CNN & & 6.95 \% & 11.17 \% & 5.87 \% & 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.\\
TopNet-HighRes & la & 6.92 \% & 10.40 \% & 6.63 \% & 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.\\
MonoRUn & & 6.78 \% & 10.88 \% & 5.83 \% & 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.\\
DCD & & 6.73 \% & 10.37 \% & 6.28 \% & 1 s / 1 core & \\
MonoPair & & 6.68 \% & 10.02 \% & 5.53 \% & 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.\\
MonoATT\_V2 & & 6.66 \% & 10.55 \% & 5.43 \% & 0.03 s / 1 core & \\
monodle & & 6.55 \% & 9.64 \% & 5.44 \% & 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 .\\
MonoFlex & & 6.31 \% & 9.43 \% & 5.26 \% & 0.03 s / GPU & Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.\\
FMF-occlusion-net & & 5.23 \% & 7.62 \% & 4.28 \% & 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 & & 4.71 \% & 6.01 \% & 3.87 \% & 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.\\
Shift R-CNN (mono) & & 4.66 \% & 7.95 \% & 4.16 \% & 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.\\
MonoPSR & & 4.00 \% & 6.12 \% & 3.30 \% & 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 & & 3.62 \% & 6.09 \% & 3.39 \% & 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.\\
DDMP-3D & & 3.55 \% & 4.93 \% & 3.01 \% & 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.\\
M3D-RPN & & 3.48 \% & 4.92 \% & 2.94 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
D4LCN & & 3.42 \% & 4.55 \% & 2.83 \% & 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.\\
CMAN & & 3.41 \% & 4.62 \% & 2.87 \% & 0.15 s / 1 core & C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.\\
QD-3DT & on & 3.37 \% & 5.53 \% & 3.02 \% & 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.\\
MonoEF & & 2.79 \% & 4.27 \% & 2.21 \% & 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.\\
RT3DStereo & st & 2.45 \% & 3.28 \% & 2.35 \% & 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.\\
MonoLiG & & 1.94 \% & 2.89 \% & 1.91 \% & 0.03 s / 1 core & \\
TopNet-UncEst & la & 1.87 \% & 3.42 \% & 1.73 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
SS3D & & 1.78 \% & 2.31 \% & 1.48 \% & 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.\\
PGD-FCOS3D & & 1.49 \% & 2.28 \% & 1.38 \% & 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.\\
SparVox3D & & 1.35 \% & 1.93 \% & 1.04 \% & 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.\\
Plane-Constraints & & 1.09 \% & 1.73 \% & 1.04 \% & 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}