\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
MB3D & & 97.72 \% & 98.75 \% & 92.81 \% & 0.09 s / 1 core & \\
LVP(84.92) & & 97.66 \% & 98.68 \% & 92.81 \% & 0.04 s / 1 core & \\
MuTOD & & 97.44 \% & 98.63 \% & 94.30 \% & 0.04 s / 1 core & \\
UDeerPEP & & 97.39 \% & 98.40 \% & 94.80 \% & 0.1 s / 1 core & Z. Dong, H. Ji, X. Huang, W. Zhang, X. Zhan and J. Chen: PeP: a Point enhanced Painting method for unified point cloud tasks. 2023.\\
VirConv-S & & 96.46 \% & 96.99 \% & 93.74 \% & 0.09 s / 1 core & H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.\\
GraR-VoI & & 96.29 \% & 96.81 \% & 91.06 \% & 0.07 s / 1 core & H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.\\
MLF-DET & & 96.09 \% & 96.87 \% & 88.78 \% & 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.\\
GraR-Po & & 96.09 \% & 96.83 \% & 90.99 \% & 0.06 s / 1 core & H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.\\
SFD & & 96.05 \% & 98.95 \% & 90.96 \% & 0.1 s / 1 core & X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.\\
VPFNet & & 96.04 \% & 96.63 \% & 90.99 \% & 0.06 s / 2 cores & H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. IEEE Transactions on Multimedia 2022.\\
VirConv-T & & 96.01 \% & 98.64 \% & 93.12 \% & 0.09 s / 1 core & H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.\\
HDet3D & & 96.00 \% & 96.69 \% & 90.84 \% & 0.07 s / >8 cores & \\
TED & & 95.96 \% & 96.63 \% & 93.24 \% & 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.\\
RDIoU & & 95.95 \% & 98.77 \% & 90.90 \% & 0.03 s / 1 core & H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X. Hua, M. Zhao and G. Lee: Rethinking IoU-based Optimization for Single- stage 3D Object Detection. ECCV 2022.\\
ACFNet & & 95.95 \% & 96.64 \% & 93.17 \% & 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.\\
PVFusion & & 95.94 \% & 96.76 \% & 90.90 \% & 0.01 s / 1 core & \\
CLOCs & & 95.93 \% & 96.77 \% & 90.93 \% & 0.1 s / 1 core & S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.\\
GraR-Vo & & 95.92 \% & 96.66 \% & 92.78 \% & 0.04 s / 1 core & H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.\\
LFT & & 95.87 \% & 99.15 \% & 88.47 \% & 0.1s / 1 core & \\
PVT-SSD & & 95.83 \% & 96.74 \% & 90.58 \% & 0.05 s / 1 core & H. Yang, W. Wang, M. Chen, B. Lin, T. He, H. Chen, X. He and W. Ouyang: PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer. CVPR 2023.\\
CLOCs\_PVCas & & 95.79 \% & 96.74 \% & 90.81 \% & 0.1 s / 1 core & S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.\\
3D Dual-Fusion & & 95.76 \% & 96.53 \% & 93.01 \% & 0.1 s / 1 core & Y. Kim, K. Park, M. Kim, D. Kum and J. Choi: 3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection. arXiv preprint arXiv:2211.13529 2022.\\
PIPC-3Ddet & & 95.75 \% & 96.79 \% & 90.79 \% & 0.05 s / 1 core & \\
GLENet-VR & & 95.73 \% & 96.84 \% & 90.80 \% & 0.04 s / 1 core & Y. Zhang, Q. Zhang, Z. Zhu, J. Hou and Y. Yuan: GLENet: Boosting 3D object detectors with generative label uncertainty estimation. International Journal of Computer Vision 2023.\\
GraR-Pi & & 95.72 \% & 98.57 \% & 92.55 \% & 0.03 s / 1 core & H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.\\
VPA & & 95.71 \% & 96.70 \% & 90.81 \% & 0.01 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
NIV-SSD & & 95.71 \% & 98.66 \% & 90.64 \% & 0.03 s / 1 core & \\
DiffCandiDet & & 95.70 \% & 96.57 \% & 92.75 \% & 0.06 s / GPU & \\
OcTr & & 95.69 \% & 96.44 \% & 90.78 \% & 0.06 s / GPU & C. Zhou, Y. Zhang, J. Chen and D. Huang: OcTr: Octree-based Transformer for 3D Object Detection. CVPR 2023.\\
CDF & & 95.66 \% & 96.21 \% & 90.48 \% & 0.08 s / 1 core & \\
DVF-V & & 95.63 \% & 96.59 \% & 90.71 \% & 0.1 s / 1 core & A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.\\
test & & 95.61 \% & 98.37 \% & 92.55 \% & 0.1 s / 1 core & \\
MAK & & 95.60 \% & 96.67 \% & 90.72 \% & 0.03 s / GPU & \\
URFormer & & 95.59 \% & 98.45 \% & 92.69 \% & 0.1 s / 1 core & \\
DSGN++ & st & 95.58 \% & 98.04 \% & 88.09 \% & 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.\\
HAF-PVP\_test & & 95.57 \% & 98.85 \% & 92.64 \% & 0.09 s / 1 core & \\
Fast-CLOCs & & 95.57 \% & 96.66 \% & 90.70 \% & 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.\\
MAK\_VOXEL\_RCNN & & 95.56 \% & 98.62 \% & 92.82 \% & 0.03 s / 1 core & \\
TSSTDet & & 95.56 \% & 96.54 \% & 92.71 \% & 0.08 s / 1 core & H. Hoang, D. Bui and M. Yoo: TSSTDet: Transformation-Based 3-D Object Detection via a Spatial Shape Transformer. IEEE Sensors Journal 2024.\\
3D HANet & & 95.54 \% & 98.59 \% & 92.66 \% & 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.\\
PA-Det3D & & 95.53 \% & 96.36 \% & 90.87 \% & 0.06 s / 1 core & \\
FARP-Net & & 95.53 \% & 96.10 \% & 92.98 \% & 0.06 s / GPU & T. Xie, L. Wang, K. Wang, R. Li, X. Zhang, H. Zhang, L. Yang, H. Liu and J. Li: FARP-Net: Local-Global Feature Aggregation and Relation-Aware Proposals for 3D Object Detection. IEEE Transactions on Multimedia 2023.\\
CasA & & 95.53 \% & 96.51 \% & 92.71 \% & 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.\\
GENet & & 95.50 \% & 96.51 \% & 90.66 \% & 0.02 s / 1 core & \\
FEIF3D & la & 95.49 \% & 96.42 \% & 92.78 \% & 0.1 s / GPU & \\
LVP & & 95.47 \% & 98.45 \% & 92.56 \% & 0.04 s / 1 core & \\
spark2 & & 95.45 \% & 96.38 \% & 92.70 \% & 0.1 s / 1 core & \\
LoGoNet & & 95.44 \% & 96.59 \% & 92.89 \% & 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.\\
spark\_voxel\_rcnn & & 95.44 \% & 96.40 \% & 92.68 \% & 0.04 s / 1 core & \\
SGFNet & & 95.43 \% & 98.42 \% & 92.46 \% & 0.09 s / 1 core & \\
CAFI-Pillars & & 95.43 \% & 96.46 \% & 90.56 \% & 30ms / & \\
spark & & 95.42 \% & 96.33 \% & 92.68 \% & 0.1 s / 1 core & \\
voxel\_spark & & 95.42 \% & 96.36 \% & 92.68 \% & 0.04 s / GPU & \\
PSMS-Net & la & 95.42 \% & 96.67 \% & 90.54 \% & 0.1 s / 1 core & \\
SS-3DSSD & & 95.39 \% & 96.30 \% & 90.43 \% & 0.014s / 1 core & \\
SDGUFusion & & 95.39 \% & 98.54 \% & 92.77 \% & 0.5 s / 1 core & \\
VDF & & 95.37 \% & 98.55 \% & 92.41 \% & 0.03 s / GPU & \\
GD-MAE & & 95.36 \% & 98.31 \% & 90.19 \% & 0.07 s / 1 core & H. Yang, T. He, J. Liu, H. Chen, B. Wu, B. Lin, X. He and W. Ouyang: GD-MAE: Generative Decoder for MAE Pre- training on LiDAR Point Clouds. CVPR 2023.\\
Voxel\_Spark\_focal\_we & & 95.35 \% & 96.37 \% & 92.63 \% & 0.08 s / 1 core & \\
DVF-PV & & 95.35 \% & 96.40 \% & 92.37 \% & 0.1 s / 1 core & A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.\\
SFD++ & & 95.35 \% & 98.38 \% & 92.39 \% & 0.12 s / 1 core & \\
BADet & & 95.34 \% & 98.65 \% & 90.28 \% & 0.14 s / 1 core & R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.\\
Anonymous & & 95.33 \% & 96.39 \% & 92.65 \% & 0.1 s / 1 core & \\
LGNet-Car & & 95.31 \% & 96.51 \% & 92.55 \% & 0.11 s / 1 core & \\
SASA & la & 95.29 \% & 96.00 \% & 92.42 \% & 0.04 s / 1 core & C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.\\
3D-BCM & & 95.27 \% & 98.47 \% & 92.39 \% & 0.1 s / 1 core & \\
PG-RCNN & & 95.27 \% & 96.64 \% & 90.37 \% & 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.\\
Focals Conv & & 95.23 \% & 96.29 \% & 92.60 \% & 0.1 s / 1 core & Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.\\
EQ-PVRCNN & & 95.20 \% & 98.22 \% & 92.47 \% & 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.\\
TED-S Reproduced & & 95.19 \% & 98.43 \% & 92.55 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
CasA++ & & 95.17 \% & 95.81 \% & 94.10 \% & 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.\\
Simi-fusion & & 95.17 \% & 98.27 \% & 92.58 \% & 0.08 s / 1 core & \\
SE-SSD & la & 95.17 \% & 96.55 \% & 90.00 \% & 0.03 s / 1 core & W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.\\
LGSLNet & & 95.14 \% & 97.98 \% & 92.61 \% & 0.1 s / GPU & \\
TED\_S\_baseline & & 95.13 \% & 96.24 \% & 92.41 \% & 0.09 s / 1 core & \\
VoxSeT & & 95.13 \% & 96.15 \% & 90.38 \% & 33 ms / 1 core & C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.\\
PR-SSD & & 95.10 \% & 97.62 \% & 92.33 \% & 0.02 s / GPU & \\
RPF3D & & 95.08 \% & 96.26 \% & 90.28 \% & 0.1 s / 1 core & \\
HMFI & & 95.05 \% & 96.28 \% & 92.28 \% & 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.\\
SPANet & & 95.03 \% & 96.31 \% & 89.99 \% & 0.06 s / 1 core & Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.\\
Pyramid R-CNN & & 95.03 \% & 95.87 \% & 92.46 \% & 0.07 s / 1 core & J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.\\
VPFNet & & 95.01 \% & 96.03 \% & 92.41 \% & 0.2 s / 1 core & C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.\\
EPNet++ & & 95.00 \% & 96.70 \% & 91.82 \% & 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.\\
GF-pointnet & & 94.99 \% & 95.92 \% & 92.22 \% & 0.02 s / 1 core & \\
USVLab BSAODet & & 94.99 \% & 96.23 \% & 92.36 \% & 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.\\
RBEV-Voxel & & 94.99 \% & 96.41 \% & 90.10 \% & 0.08 s / GPU & \\
Voxel R-CNN & & 94.96 \% & 96.47 \% & 92.24 \% & 0.04 s / GPU & J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.\\
TEDx & & 94.94 \% & 96.11 \% & 92.15 \% & 0.01 s / 1 core & \\
PDV & & 94.91 \% & 96.06 \% & 92.30 \% & 0.1 s / 1 core & J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.\\
RAFDet & & 94.91 \% & 95.93 \% & 92.22 \% & 0.1 s / 1 core & \\
DEF-Model & & 94.86 \% & 96.16 \% & 91.88 \% & 0.03 s / 1 core & \\
SIENet & & 94.85 \% & 96.01 \% & 92.23 \% & 0.08 s / 1 core & Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.\\
SQD & & 94.85 \% & 98.20 \% & 92.26 \% & 0.06 s / 1 core & \\
VoTr-TSD & & 94.81 \% & 95.95 \% & 92.24 \% & 0.07 s / 1 core & J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.\\
GraphAlign & & 94.79 \% & 98.04 \% & 92.35 \% & 0.03 s / GPU & \\
L-AUG & & 94.76 \% & 95.80 \% & 91.94 \% & 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.\\
CZY\_PPF\_Net & & 94.74 \% & 98.03 \% & 92.03 \% & 0.1 s / 1 core & \\
AMVFNet & & 94.71 \% & 96.09 \% & 92.10 \% & 0.04 s / GPU & \\
Under Blind Review#1 & & 94.70 \% & 95.62 \% & 92.21 \% & 0.1 s / 1 core & \\
M3DeTR & & 94.70 \% & 97.37 \% & 91.89 \% & 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.\\
MonoSample (DID-M3D) & & 94.69 \% & 96.30 \% & 85.10 \% & 0.2 s / 1 core & \\
Spark\_partA22 & & 94.67 \% & 95.99 \% & 91.95 \% & 10 s / 1 core & \\
spark\_second\_focal\_w & & 94.67 \% & 95.43 \% & 91.82 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
U\_PV\_V2\_ep100\_80 & & 94.67 \% & 95.80 \% & 92.09 \% & 0... s / 1 core & \\
XView & & 94.66 \% & 95.88 \% & 92.07 \% & 0.1 s / 1 core & L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.\\
Spark\_PartA2\_Soft\_fo & & 94.65 \% & 95.79 \% & 91.99 \% & 0.1 s / 1 core & \\
LGNet-3classes & & 94.65 \% & 98.12 \% & 91.97 \% & 0.11 s / 1 core & \\
StructuralIF & & 94.64 \% & 96.12 \% & 91.85 \% & 0.02 s / 8 cores & J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.\\
F3D & & 94.64 \% & 95.98 \% & 92.09 \% & 0.01 s / 1 core & \\
HA-PillarNet & & 94.63 \% & 95.89 \% & 92.00 \% & 0.05 s / 1 core & \\
focalnet & & 94.62 \% & 98.05 \% & 92.13 \% & 0.05 s / 1 core & \\
U\_PV\_V2\_ep\_100\_100 & & 94.62 \% & 95.73 \% & 91.99 \% & 0.1 s / 1 core & \\
spark-part2 & & 94.61 \% & 95.70 \% & 91.96 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
focalnet & & 94.60 \% & 98.08 \% & 92.12 \% & 0.05 s / 1 core & \\
P2V-RCNN & & 94.59 \% & 96.01 \% & 92.13 \% & 0.1 s / 1 core & J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.\\
spark\_second & & 94.59 \% & 95.39 \% & 91.73 \% & . s / 1 core & \\
OFFNet & & 94.58 \% & 96.17 \% & 91.91 \% & 0.1 s / GPU & \\
CAT-Det & & 94.57 \% & 95.95 \% & 91.88 \% & 0.3 s / GPU & Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.\\
MMLab PV-RCNN & la & 94.57 \% & 98.15 \% & 91.85 \% & 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.\\
spark\_second2 & & 94.57 \% & 95.32 \% & 91.77 \% & 10 s / 1 core & \\
sec\_spark & & 94.57 \% & 95.36 \% & 91.73 \% & 0.03 s / GPU & \\
bs & & 94.53 \% & 96.05 \% & 91.71 \% & 0.1 s / 1 core & \\
DSA-PV-RCNN & la & 94.52 \% & 95.84 \% & 91.93 \% & 0.08 s / 1 core & P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.\\
RangeDet (Official) & & 94.51 \% & 95.48 \% & 91.57 \% & 0.02 s / 1 core & L. Fan, X. Xiong, F. Wang, N. Wang and Z. Zhang: RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.\\
SDGUFusion & & 94.49 \% & 98.14 \% & 92.02 \% & 0.5 s / 1 core & \\
PV-RCNN-Plus & & 94.49 \% & 95.71 \% & 91.98 \% & 1 s / 1 core & \\
MVRA + I-FRCNN+ & & 94.46 \% & 95.66 \% & 81.74 \% & 0.18 s / GPU & H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.\\
SVGA-Net & & 94.45 \% & 96.02 \% & 91.54 \% & 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.\\
PASS-PV-RCNN-Plus & & 94.45 \% & 95.77 \% & 91.89 \% & 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.\\
GeVo & & 94.44 \% & 95.87 \% & 91.86 \% & 0.05 s / 1 core & \\
DVFENet & & 94.44 \% & 95.33 \% & 91.55 \% & 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.\\
af & & 94.43 \% & 95.77 \% & 91.93 \% & 1 s / GPU & \\
RangeIoUDet & la & 94.42 \% & 95.69 \% & 91.70 \% & 0.02 s / GPU & Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.\\
VoxelFSD & & 94.42 \% & 95.72 \% & 91.78 \% & 0.08 s / 1 core & \\
AAMVFNet & & 94.34 \% & 95.85 \% & 91.73 \% & 0.04 s / GPU & \\
focal & & 94.31 \% & 98.27 \% & 91.91 \% & 100 s / 1 core & \\
Second\_baseline & & 94.27 \% & 95.18 \% & 91.27 \% & 0.03 s / 1 core & \\
MSIT-Det & & 94.27 \% & 97.20 \% & 86.71 \% & 0.06 s / 1 core & \\
SERCNN & la & 94.24 \% & 96.31 \% & 89.71 \% & 0.1 s / 1 core & D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.\\
EPNet & & 94.22 \% & 96.13 \% & 89.68 \% & 0.1 s / 1 core & T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.\\
SRDL & & 94.08 \% & 95.83 \% & 91.55 \% & 0.05 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
pointpillar\_spark\_fo & & 94.07 \% & 96.40 \% & 91.06 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SC-SSD & & 94.06 \% & 95.03 \% & 90.97 \% & 1 s / 1 core & \\
u\_second\_v4\_epoch\_10 & & 94.01 \% & 95.32 \% & 91.14 \% & 0.1 s / 1 core & \\
RangeRCNN & la & 93.90 \% & 95.47 \% & 91.53 \% & 0.06 s / GPU & Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.\\
U\_second\_v4\_ep\_100\_8 & & 93.82 \% & 94.84 \% & 90.92 \% & 0.1 s / 1 core & \\
Anonymous & & 93.80 \% & 96.80 \% & 88.70 \% & 0.04 s / 1 core & \\
pointpillars\_spark & & 93.79 \% & 96.81 \% & 90.83 \% & 0.02 s / GPU & \\
SIF & & 93.79 \% & 95.48 \% & 91.30 \% & 0.1 s / 1 core & P. An: SIF. Submitted to CVIU 2021.\\
spark\_pointpillar2 & & 93.79 \% & 96.63 \% & 90.75 \% & 10 s / 1 core & \\
DDF & & 93.79 \% & 96.86 \% & 88.72 \% & 0.1 s / 1 core & \\
DD3D & & 93.78 \% & 94.67 \% & 88.99 \% & 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) .\\
MGAF-3DSSD & & 93.77 \% & 94.45 \% & 86.25 \% & 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.\\
spark\_pointpillar & & 93.76 \% & 96.84 \% & 90.80 \% & 0.02 s / GPU & \\
MMLAB LIGA-Stereo & st & 93.71 \% & 96.40 \% & 86.00 \% & 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.\\
Sem-Aug & la & 93.69 \% & 96.78 \% & 88.69 \% & 0.1 s / GPU & L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.\\
IIOU & & 93.68 \% & 96.44 \% & 90.82 \% & 0.1 s / GPU & \\
IMLIDAR(base) & & 93.63 \% & 96.73 \% & 88.62 \% & 0.1 s / 1 core & \\
KPTr & & 93.59 \% & 96.52 \% & 90.58 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
3ONet & & 93.58 \% & 96.86 \% & 88.45 \% & 0.1 s / 1 core & H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.\\
Patches - EMP & la & 93.58 \% & 97.88 \% & 90.31 \% & 0.5 s / GPU & J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.\\
PA3DNet & & 93.55 \% & 96.56 \% & 88.56 \% & 0.1 s / GPU & M. Wang, L. Zhao and Y. Yue: PA3DNet: 3-D Vehicle Detection with Pseudo Shape Segmentation and Adaptive Camera- LiDAR Fusion. IEEE Transactions on Industrial Informatics 2023.\\
DGEnhCL & & 93.54 \% & 96.74 \% & 90.46 \% & 0.04 s / 1 core & \\
MVAF-Net & & 93.54 \% & 95.35 \% & 90.70 \% & 0.06 s / 1 core & G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.\\
IA-SSD (multi) & & 93.47 \% & 96.07 \% & 90.51 \% & 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.\\
casxv1 & & 93.42 \% & 96.66 \% & 90.67 \% & 0.01 s / 1 core & \\
casx & & 93.42 \% & 96.66 \% & 90.67 \% & 0.01 s / 1 core & \\
IA-SSD (single) & & 93.41 \% & 96.23 \% & 88.34 \% & 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.\\
IOUFusion & & 93.39 \% & 96.37 \% & 90.52 \% & 0.1 s / GPU & \\
CIA-SSD & la & 93.34 \% & 96.65 \% & 85.76 \% & 0.03 s / 1 core & W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.\\
SeSame-point & & 93.32 \% & 95.20 \% & 90.14 \% & N/A s / TITAN RTX & \\
Deep MANTA & & 93.31 \% & 98.83 \% & 82.95 \% & 0.7 s / GPU & F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.\\
LVFSD & & 93.31 \% & 95.24 \% & 90.46 \% & 0.06 s / & ERROR: Wrong syntax in BIBTEX file.\\
MMAE & la & 93.30 \% & 96.49 \% & 90.10 \% & 0.07 s / 1 core & \\
StereoDistill & & 93.29 \% & 97.57 \% & 87.48 \% & 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.\\
LPCG-Monoflex & & 93.26 \% & 96.68 \% & 83.34 \% & 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.\\
fuf & & 93.24 \% & 96.66 \% & 88.08 \% & 10 s / 1 core & \\
MonoLiG & & 93.23 \% & 96.56 \% & 83.42 \% & 0.03 s / 1 core & A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.\\
RAFDet & & 93.23 \% & 95.86 \% & 90.36 \% & 0.01 s / 1 core & \\
CityBrainLab-CT3D & & 93.20 \% & 96.26 \% & 90.44 \% & 0.07 s / 1 core & H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.\\
DFAF3D & & 93.20 \% & 96.54 \% & 90.03 \% & 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.\\
IIOU\_LDR & & 93.19 \% & 96.29 \% & 88.04 \% & 0.03 s / 1 core & \\
pointpillar\_baseline & & 93.17 \% & 95.12 \% & 88.61 \% & 0.01 s / 1 core & \\
PVTr & & 93.16 \% & 94.68 \% & 90.59 \% & 0.1 s / 1 core & \\
ROT\_S3D & & 93.15 \% & 96.23 \% & 88.01 \% & 0.1 s / GPU & \\
DA-Net & & 93.14 \% & 96.58 \% & 90.56 \% & 0.1 s / 1 core & \\
MonoInsight & & 93.14 \% & 96.17 \% & 83.36 \% & 0.03 s / 1 core & \\
MonoInsight & & 93.14 \% & 96.17 \% & 83.36 \% & 0.03 s / 1 core & \\
MonoLSS & & 93.11 \% & 95.99 \% & 83.14 \% & 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.\\
RAFDet & & 93.10 \% & 95.68 \% & 90.26 \% & 0.01 s / 1 core & \\
MOPNet & & 93.09 \% & 96.53 \% & 83.04 \% & 0.1 s / 1 core & \\
SNVC & st & 93.09 \% & 96.27 \% & 85.51 \% & 1 s / GPU & S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.\\
H^23D R-CNN & & 93.03 \% & 96.13 \% & 90.33 \% & 0.03 s / 1 core & J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.\\
FromVoxelToPoint & & 92.98 \% & 96.07 \% & 90.40 \% & 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.\\
P2P & & 92.95 \% & 96.61 \% & 87.77 \% & 0.1 s / GPU & \\
GA-RCNN & & 92.92 \% & 96.06 \% & 90.25 \% & 47ms / 1 core & \\
EBM3DOD & & 92.88 \% & 96.39 \% & 87.58 \% & 0.12 s / 1 core & F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.\\
Struc info fusion II & & 92.88 \% & 96.44 \% & 87.67 \% & 0.05 s / GPU & P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.\\
MG & & 92.87 \% & 96.25 \% & 89.89 \% & 0.1 s / 1 core & \\
HotSpotNet & & 92.74 \% & 96.20 \% & 89.68 \% & 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.\\
Struc info fusion I & & 92.71 \% & 96.24 \% & 87.55 \% & 0.05 s / 1 core & P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.\\
EBM3DOD baseline & & 92.70 \% & 96.31 \% & 87.44 \% & 0.05 s / 1 core & F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.\\
MonoCD & & 92.65 \% & 96.36 \% & 85.17 \% & n/a s / 1 core & \\
FastDet & & 92.62 \% & 97.85 \% & 89.30 \% & 0.01 s / 1 core & \\
SARPNET & & 92.58 \% & 95.82 \% & 87.33 \% & 0.05 s / 1 core & Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.\\
Patches & la & 92.57 \% & 96.31 \% & 87.41 \% & 0.15 s / GPU & J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.\\
R-GCN & & 92.53 \% & 96.16 \% & 87.45 \% & 0.16 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
PI-RCNN & & 92.52 \% & 96.15 \% & 87.47 \% & 0.1 s / 1 core & L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.\\
CenterNet3D & & 92.48 \% & 95.71 \% & 89.54 \% & 0.04 s / GPU & G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.\\
PointPainting & la & 92.43 \% & 98.36 \% & 89.49 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
3D IoU-Net & & 92.42 \% & 96.31 \% & 87.60 \% & 0.1 s / 1 core & J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.\\
CLOCs\_SecCas & & 92.37 \% & 95.16 \% & 88.43 \% & 0.1 s / 1 core & S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.\\
ACDet & & 92.36 \% & 96.07 \% & 89.18 \% & 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.\\
DASS & & 92.25 \% & 96.20 \% & 87.26 \% & 0.09 s / 1 core & O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.\\
S-AT GCN & & 92.24 \% & 95.02 \% & 90.46 \% & 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.\\
Sem-Aug-PointRCNN++ & & 92.20 \% & 95.64 \% & 87.48 \% & 0.1 s / 8 cores & L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.\\
SegVoxelNet & & 92.16 \% & 95.86 \% & 86.90 \% & 0.04 s / 1 core & H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.\\
PointRGCN & & 92.15 \% & 97.48 \% & 86.83 \% & 0.26 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
BAPartA2S-4h & & 92.04 \% & 95.63 \% & 89.14 \% & 0.1 s / 1 core & \\
F-ConvNet & la & 91.98 \% & 95.81 \% & 79.83 \% & 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.\\
PartA2\_basline & & 91.98 \% & 95.64 \% & 89.40 \% & 0.09 s / 1 core & \\
CAT2 & & 91.97 \% & 95.68 \% & 86.71 \% & 1 s / 1 core & \\
AB3DMOT & la on & 91.87 \% & 95.86 \% & 86.78 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
MLAFF & & 91.84 \% & 95.17 \% & 86.94 \% & 0.39 s / 2 cores & \\
PASS-PointPillar & & 91.82 \% & 95.15 \% & 88.31 \% & 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.\\
MMpointpillars & & 91.79 \% & 95.13 \% & 86.51 \% & 0.05 s / 1 core & \\
TF-PartA2 & & 91.78 \% & 95.34 \% & 88.82 \% & 0.1 s / 1 core & \\
MMLab-PointRCNN & la & 91.77 \% & 95.90 \% & 86.92 \% & 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.\\
MMLab-PartA^2 & la & 91.73 \% & 95.00 \% & 88.86 \% & 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.\\
MSAW & & 91.72 \% & 94.92 \% & 86.86 \% & 0.42 s / 2 cores & \\
HA PillarNet & & 91.70 \% & 95.17 \% & 86.62 \% & 0.05 s / 1 core & \\
C-GCN & & 91.57 \% & 95.63 \% & 86.13 \% & 0.147 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
XT-PartA2 & & 91.56 \% & 95.21 \% & 88.74 \% & 0.1 s / GPU & \\
PointRGBNet & & 91.33 \% & 95.39 \% & 86.29 \% & 0.08 s / 4 cores & P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.\\
mmFUSION & & 91.30 \% & 95.47 \% & 86.33 \% & 1s / 1 core & J. Ahmad and A. Del Bue: mmFUSION: Multimodal Fusion for 3D Objects Detection. arXiv preprint arXiv:2311.04058 2023.\\
mm3d\_PartA2 & & 91.28 \% & 95.05 \% & 88.48 \% & 0.1 s / GPU & \\
SeSame-pillar & & 91.26 \% & 95.07 \% & 87.94 \% & N/A s / TITAN RTX & \\
EgoNet & & 91.23 \% & 96.11 \% & 80.96 \% & 0.1 s / GPU & S. Li, Z. Yan, H. Li and K. Cheng: Exploring intermediate representation for monocular vehicle pose estimation. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.\\
MMpp & & 91.21 \% & 94.79 \% & 86.17 \% & 0.05 s / 1 core & \\
TBD & & 91.12 \% & 96.68 \% & 81.05 \% & 0.04 s / 1 core & \\
MonoAux-v2 & & 91.07 \% & 94.27 \% & 78.73 \% & 0.04 s / GPU & \\
PFF3D & la & 91.06 \% & 94.86 \% & 86.28 \% & 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.\\
SeSame-pillar w/scor & & 91.03 \% & 94.83 \% & 87.65 \% & N/A s / 1 core & \\
Stereo CenterNet & st & 91.02 \% & 96.54 \% & 83.15 \% & 0.04 s / GPU & Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.\\
FDGNet & & 90.92 \% & 96.35 \% & 82.90 \% & 0.1 s / 1 core & \\
APDM & & 90.88 \% & 92.90 \% & 87.59 \% & 0.7 s / 1 core & \\
SHUD & & 90.86 \% & 96.45 \% & 80.74 \% & 0.04 s / 1 core & \\
Mix-Teaching & & 90.84 \% & 96.31 \% & 83.11 \% & 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.\\
MonoFlex & & 90.82 \% & 95.95 \% & 83.11 \% & 0.03 s / GPU & Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.\\
LWLANet & & 90.79 \% & 94.16 \% & 80.74 \% & 0.1 s / 1 core & \\
Harmonic PointPillar & & 90.78 \% & 94.23 \% & 87.42 \% & 0.01 s / 1 core & H. Zhang, J. Mekala, Z. Nain, J. Park and H. Jung: 3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection for V2X Orchestration. will submit to IEEE Transactions on Vehicular Technology 2022.\\
MAFF-Net(DAF-Pillar) & & 90.78 \% & 94.17 \% & 83.17 \% & 0.04 s / 1 core & Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.\\
HRI-VoxelFPN & & 90.76 \% & 96.35 \% & 85.37 \% & 0.02 s / GPU & H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.\\
MM\_SECOND & & 90.75 \% & 94.85 \% & 85.58 \% & 0.05 s / GPU & \\
prcnn\_v18\_80\_100 & & 90.75 \% & 96.17 \% & 85.68 \% & 0.1 s / 1 core & \\
MonoAux & & 90.74 \% & 93.86 \% & 80.71 \% & 0.04 s / GPU & \\
KM3D & & 90.70 \% & 96.34 \% & 80.72 \% & 0.03 s / 1 core & P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.\\
PointPillars & la & 90.70 \% & 93.84 \% & 87.47 \% & 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.\\
WS3D & la & 90.69 \% & 94.85 \% & 85.94 \% & 0.1 s / GPU & Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.\\
MSFENet & & 90.68 \% & 96.32 \% & 82.81 \% & 0.1 s / 1 core & \\
EOTL & & 90.67 \% & 96.14 \% & 80.59 \% & 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.\\
DCD & & 90.66 \% & 96.31 \% & 83.01 \% & 0.03 s / 1 core & Y. Li, Y. Chen, J. He and Z. Zhang: Densely Constrained Depth Estimator for Monocular 3D Object Detection. European Conference on Computer Vision 2022.\\
NeurOCS & & 90.66 \% & 96.15 \% & 80.64 \% & 0.1 s / GPU & Z. Min, B. Zhuang, S. Schulter, B. Liu, E. Dunn and M. Chandraker: NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization. CVPR 2023.\\
MonoEF & & 90.65 \% & 96.19 \% & 82.95 \% & 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.\\
CIE & & 90.64 \% & 96.19 \% & 82.90 \% & 0.1 s / 1 core & Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.\\
MonoSGC & & 90.62 \% & 94.14 \% & 82.58 \% & 0.04 s / 1 core & \\
VSAC & & 90.57 \% & 96.16 \% & 87.72 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
DID-M3D & & 90.55 \% & 94.20 \% & 80.61 \% & 0.04 s / 1 core & L. Peng, X. Wu, Z. Yang, H. Liu and D. Cai: DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection. ECCV 2022.\\
BA2-Det+MonoFlex & & 90.51 \% & 96.20 \% & 80.66 \% & 0.03 s / 1 core & \\
QD-3DT & on & 90.49 \% & 92.61 \% & 80.32 \% & 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.\\
HomoLoss(monoflex) & & 90.49 \% & 95.86 \% & 80.66 \% & 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.\\
SeSame-voxel & & 90.42 \% & 95.76 \% & 87.40 \% & N/A s / TITAN RTX & \\
monodle & & 90.23 \% & 93.46 \% & 80.11 \% & 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 .\\
3D IoU Loss & la & 90.21 \% & 95.60 \% & 84.96 \% & 0.08 s / GPU & D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.\\
MonoCInIS & & 90.20 \% & 95.80 \% & 82.00 \% & 0,13 s / GPU & J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.\\
ARPNET & & 90.11 \% & 93.42 \% & 82.56 \% & 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.\\
TANet & & 90.11 \% & 93.52 \% & 84.61 \% & 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.\\
CG-Stereo & st & 89.98 \% & 96.28 \% & 82.21 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
Deep3DBox & & 89.88 \% & 94.62 \% & 76.40 \% & 1.5 s / GPU & A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.\\
CMKD & & 89.81 \% & 95.07 \% & 83.24 \% & 0.1 s / 1 core & Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.\\
PS-fld & & 89.78 \% & 95.60 \% & 81.68 \% & 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.\\
GPP & & 89.68 \% & 93.94 \% & 80.60 \% & 0.23 s / GPU & A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. IEEE Transactions on Intelligent Vehicles 2020.\\
MonoCDiT & & 89.58 \% & 95.44 \% & 79.68 \% & 0.05 s / GPU & \\
MonoSTL & & 89.58 \% & 95.09 \% & 79.68 \% & na s / 1 core & \\
SubCNN & & 89.53 \% & 94.11 \% & 79.14 \% & 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.\\
HINTED & & 89.41 \% & 93.97 \% & 83.95 \% & 0.04 s / 1 core & \\
SCNet & la & 89.36 \% & 95.23 \% & 84.03 \% & 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.\\
DFSemONet(Baseline) & & 89.32 \% & 94.77 \% & 85.91 \% & 0.04 s / GPU & \\
PI-SECOND & & 89.29 \% & 95.06 \% & 85.82 \% & 0.05 s / GPU & \\
AVOD & la & 89.22 \% & 94.98 \% & 82.14 \% & 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.\\
IAFA & & 89.14 \% & 92.96 \% & 79.40 \% & 0.04 s / 1 core & D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.\\
MonoSIM\_v2 & & 89.09 \% & 95.40 \% & 79.40 \% & 0.03 s / 1 core & \\
MonoDDE & & 89.07 \% & 96.72 \% & 81.42 \% & 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.\\
ADD & & 88.96 \% & 94.58 \% & 80.78 \% & 0.1 s / 1 core & Z. Wu, Y. Wu, J. Pu, X. Li and X. Wang: Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection. AAAI2023 .\\
AVOD-FPN & la & 88.61 \% & 94.65 \% & 83.71 \% & 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.\\
MonoUNI & & 88.50 \% & 94.10 \% & 78.35 \% & 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.\\
OPA-3D & & 88.44 \% & 96.41 \% & 76.17 \% & 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.\\
MVAF-Net(3-classes) & & 88.37 \% & 95.29 \% & 85.01 \% & 0.1 s / 1 core & \\
MVAF-Net(3-classes) & & 88.04 \% & 94.93 \% & 84.57 \% & 0.1 s / 1 core & \\
DeepStereoOP & & 87.81 \% & 93.68 \% & 77.60 \% & 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.\\
MonoRUn & & 87.64 \% & 95.44 \% & 77.75 \% & 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.\\
3DBN & la & 87.59 \% & 93.34 \% & 79.91 \% & 0.13s / & X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.\\
SVDM-VIEW & & 87.51 \% & 94.32 \% & 79.09 \% & 1 s / 1 core & \\
FQNet & & 87.49 \% & 93.66 \% & 73.61 \% & 0.5 s / 1 core & L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.\\
Shift R-CNN (mono) & & 87.47 \% & 93.75 \% & 77.19 \% & 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 & & 87.45 \% & 93.29 \% & 72.26 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
MonoRoIDepth & & 87.40 \% & 93.48 \% & 77.32 \% & 1 s / 1 core & \\
Mono3D & & 87.28 \% & 93.13 \% & 77.00 \% & 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.\\
SMOKE & & 87.02 \% & 92.94 \% & 77.12 \% & 0.03 s / GPU & Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.\\
3DOP & st & 86.93 \% & 91.31 \% & 76.72 \% & 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.\\
CDN & st & 86.90 \% & 95.79 \% & 79.05 \% & 0.6 s / GPU & D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.\\
RTM3D & & 86.73 \% & 91.75 \% & 77.18 \% & 0.05 s / GPU & P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.\\
MonoDTR & & 86.70 \% & 93.12 \% & 74.53 \% & 0.04 s / 1 core & K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.\\
SH3D & & 86.67 \% & 95.49 \% & 77.04 \% & 0.1 s / 1 core & \\
MonoFRD & & 86.58 \% & 95.01 \% & 76.82 \% & 0.01 s / 1 core & \\
MonoRCNN & & 86.48 \% & 91.90 \% & 66.71 \% & 0.07 s / GPU & X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.\\
MonoRCNN++ & & 86.37 \% & 94.22 \% & 71.52 \% & 0.07 s / GPU & X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.\\
BirdNet+ & la & 86.13 \% & 92.39 \% & 81.11 \% & 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.\\
MonoNeRD & & 86.13 \% & 94.24 \% & 76.38 \% & 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.\\
MEDL-U & & 86.11 \% & 94.27 \% & 79.84 \% & 1 s / GPU & \\
MonoPair & & 86.11 \% & 91.65 \% & 76.45 \% & 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.\\
DSGN & st & 86.03 \% & 95.42 \% & 78.27 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
DEVIANT & & 85.97 \% & 94.01 \% & 75.84 \% & 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.\\
MonoAuxNorm & & 85.93 \% & 92.29 \% & 77.78 \% & 0.02 s / GPU & \\
GUPNet & & 85.90 \% & 93.92 \% & 73.55 \% & 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.\\
MonoAIU & & 85.58 \% & 93.79 \% & 70.77 \% & 0.03 s / GPU & \\
MonoDETR & & 85.44 \% & 93.78 \% & 75.29 \% & 0.04 s / 1 core & R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection. arXiv preprint arXiv:2203.13310 2022.\\
DMF & st & 85.20 \% & 89.42 \% & 82.07 \% & 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.\\
StereoFENet & st & 85.14 \% & 91.28 \% & 76.80 \% & 0.15 s / 1 core & W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.\\
MonoSIM & & 85.06 \% & 93.74 \% & 77.83 \% & 0.16 s / 1 core & \\
Anonymous & & 84.89 \% & 93.86 \% & 70.37 \% & 0.1 s / 1 core & \\
DE\_Fusion & & 84.81 \% & 93.39 \% & 74.87 \% & 0.06 s / 1 core & \\
PL++ (SDN+GDC) & st la & 84.42 \% & 94.83 \% & 76.95 \% & 0.6 s / GPU & Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.\\
SS3D & & 84.38 \% & 92.57 \% & 69.82 \% & 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.\\
CDN-PL++ & st & 84.21 \% & 94.45 \% & 76.69 \% & 0.4 s / GPU & D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.\\
MonoFENet & & 84.09 \% & 91.42 \% & 75.93 \% & 0.15 s / 1 core & W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.\\
MonOAPC & & 83.97 \% & 92.34 \% & 74.42 \% & 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.\\
Complexer-YOLO & la & 83.89 \% & 91.77 \% & 79.24 \% & 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.\\
ZoomNet & st & 83.79 \% & 94.14 \% & 68.78 \% & 0.3 s / 1 core & L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.\\
DLE & & 83.19 \% & 94.06 \% & 61.13 \% & 0.06 s / & C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.\\
M3D-RPN & & 82.81 \% & 88.38 \% & 67.08 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
MonoTAKD V2 & & 82.72 \% & 93.72 \% & 77.10 \% & 0.1 s / 1 core & \\
MonoLTKD & & 82.72 \% & 93.72 \% & 77.10 \% & 0.04 s / 1 core & \\
MonoTAKD & & 82.72 \% & 93.72 \% & 77.10 \% & 0.1 s / 1 core & \\
MonoLTKD\_V3 & & 82.72 \% & 93.72 \% & 77.10 \% & 0.04 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SGM3D & & 82.51 \% & 93.46 \% & 72.67 \% & 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.\\
BKDStereo3D & & 82.12 \% & 93.50 \% & 60.34 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
Disp R-CNN (velo) & st & 82.09 \% & 93.31 \% & 69.78 \% & 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.\\
D4LCN & & 82.08 \% & 90.01 \% & 63.98 \% & 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 & & 81.96 \% & 89.43 \% & 63.74 \% & 0.15 s / 1 core & C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.\\
Disp R-CNN & st & 81.96 \% & 93.49 \% & 67.35 \% & 0.387 s / GPU & J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.\\
Pseudo-LiDAR++ & st & 81.87 \% & 94.14 \% & 74.29 \% & 0.4 s / GPU & Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.\\
BS3D & & 81.22 \% & 94.66 \% & 68.39 \% & 22 ms / & N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.\\
YOLOStereo3D & st & 80.88 \% & 93.65 \% & 61.17 \% & 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.\\
HomoLoss(imvoxelnet) & & 80.67 \% & 91.94 \% & 70.64 \% & 0.20 s / 1 core & J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.\\
FRCNN+Or & & 80.57 \% & 91.50 \% & 67.49 \% & 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.\\
DDMP-3D & & 80.20 \% & 90.73 \% & 61.82 \% & 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.\\
MonoTRKDv2 & & 80.12 \% & 93.57 \% & 74.40 \% & 40 s / 1 core & \\
Ground-Aware & & 80.05 \% & 90.98 \% & 60.51 \% & 0.05 s / 1 core & Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.\\
GrooMeD-NMS & & 79.93 \% & 90.05 \% & 63.43 \% & 0.12 s / 1 core & A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.\\
ESGN & st & 79.84 \% & 92.74 \% & 69.76 \% & 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.\\
PGD-FCOS3D & & 79.46 \% & 91.51 \% & 68.48 \% & 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.\\
YoloMono3D & & 78.50 \% & 91.43 \% & 58.80 \% & 0.05 s / GPU & Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.\\
3D-GCK & & 78.44 \% & 88.59 \% & 66.28 \% & 24 ms / & N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.\\
3D-SSMFCNN & & 77.82 \% & 77.84 \% & 68.67 \% & 0.1 s / GPU & L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.\\
BKDStereo3D w/o KD & & 77.76 \% & 91.96 \% & 58.69 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
DFR-Net & & 77.41 \% & 89.79 \% & 59.20 \% & 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.\\
AutoShape & & 77.31 \% & 86.41 \% & 64.06 \% & 0.04 s / 1 core & Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.\\
ImVoxelNet & & 77.18 \% & 89.07 \% & 67.35 \% & 0.2 s / GPU & D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.\\
Aug3D-RPN & & 76.89 \% & 84.89 \% & 60.21 \% & 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.\\
ODGS & & 76.22 \% & 80.49 \% & 71.64 \% & 0.1 s / 1 core & \\
FMF-occlusion-net & & 75.95 \% & 91.51 \% & 59.55 \% & 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.\\
SST [st] & st & 75.93 \% & 90.04 \% & 68.66 \% & 1 s / 1 core & \\
TS3D & st & 75.81 \% & 91.63 \% & 56.18 \% & 0.09 s / GPU & \\
3DVP & & 75.71 \% & 84.44 \% & 64.41 \% & 40 s / 8 cores & Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.\\
GS3D & & 75.63 \% & 85.79 \% & 61.85 \% & 2 s / 1 core & B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
Pose-RCNN & & 75.41 \% & 89.49 \% & 63.57 \% & 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.\\
SubCat & & 75.26 \% & 83.31 \% & 59.55 \% & 0.7 s / 6 cores & E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.\\
Plane-Constraints & & 75.18 \% & 82.46 \% & 66.51 \% & 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.\\
3D FCN & la & 74.54 \% & 86.65 \% & 67.73 \% & >5 s / 1 core & B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.\\
Mobile Stereo R-CNN & st & 74.13 \% & 88.80 \% & 59.84 \% & 1.8 s / & M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.\\
OC Stereo & st & 73.34 \% & 86.86 \% & 61.37 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
SeSame-point w/score & & 71.49 \% & 88.88 \% & 61.49 \% & N/A s / GPU & \\
GAC3D & & 70.49 \% & 83.27 \% & 52.04 \% & 0.25 s / 1 core & M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.\\
ROI-10D & & 68.14 \% & 75.32 \% & 58.98 \% & 0.2 s / GPU & F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.\\
BirdNet+ (legacy) & la & 67.65 \% & 91.82 \% & 65.11 \% & 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.\\
multi-task CNN & & 67.51 \% & 79.00 \% & 58.80 \% & 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.\\
CaDDN & & 67.31 \% & 78.28 \% & 59.52 \% & 0.63 s / GPU & C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.\\
Decoupled-3D & & 67.23 \% & 87.34 \% & 53.84 \% & 0.08 s / GPU & Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.\\
BdCost48LDCF & & 65.50 \% & 80.44 \% & 51.24 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
OC-DPM & & 65.32 \% & 77.35 \% & 51.00 \% & 10 s / 8 cores & B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.\\
RefinedMPL & & 64.02 \% & 87.95 \% & 52.06 \% & 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.\\
DPM-VOC+VP & & 63.58 \% & 79.09 \% & 46.59 \% & 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.\\
SeSame-voxel w/score & & 63.45 \% & 73.43 \% & 57.52 \% & N/A s / GPU & \\
AOG-View & & 62.62 \% & 77.62 \% & 48.27 \% & 3 s / 1 core & B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.\\
CIE + DM3D & & 61.42 \% & 79.31 \% & 53.35 \% & 0.1 s / 1 core & Ananimities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.\\
LSVM-MDPM-sv & & 57.48 \% & 70.23 \% & 42.54 \% & 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.\\
SAMME48LDCF & & 57.26 \% & 76.28 \% & 43.55 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
BirdNet & la & 56.94 \% & 79.20 \% & 54.88 \% & 0.11 s / & J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
VeloFCN & la & 51.05 \% & 70.03 \% & 44.82 \% & 1 s / GPU & B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .\\
Mono3D\_PLiDAR & & 49.39 \% & 76.90 \% & 41.13 \% & 0.1 s / & X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.\\
DPM-C8B1 & st & 48.00 \% & 57.76 \% & 35.52 \% & 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.\\
LTN & & 46.54 \% & 48.96 \% & 41.58 \% & 0.4 s / GPU & T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.\\
sensekitti & & 46.12 \% & 49.16 \% & 42.79 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
Kinematic3D & & 45.50 \% & 58.33 \% & 34.81 \% & 0.12 s / 1 core & G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .\\
WeakM3D & & 41.50 \% & 41.21 \% & 38.11 \% & 0.08 s / 1 core & L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.\\
MonoCInIS & & 40.75 \% & 45.00 \% & 34.48 \% & 0,14 s / GPU & J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.\\
3D-CVF at SPA & la & 39.79 \% & 40.44 \% & 36.10 \% & 0.06 s / 1 core & J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.\\
Cube R-CNN & & 39.78 \% & 38.09 \% & 35.40 \% & 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.\\
SPG\_mini & la & 38.75 \% & 39.26 \% & 38.57 \% & 0.09 s / GPU & Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.\\
SPG & la & 38.73 \% & 40.02 \% & 38.52 \% & 0.09 s / 1 core & Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.\\
SA-SSD & & 38.30 \% & 39.40 \% & 37.07 \% & 0.04 s / 1 core & C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.\\
BtcDet & la & 38.00 \% & 39.26 \% & 36.82 \% & 0.09 s / GPU & Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.\\
SSL-PointGNN & & 37.21 \% & 38.55 \% & 36.53 \% & 0.56 s / GPU & E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.\\
Point-GNN & la & 37.20 \% & 38.66 \% & 36.29 \% & 0.6 s / GPU & W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.\\
RT3D-GMP & st & 36.31 \% & 44.06 \% & 27.32 \% & 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.\\
AOG & & 29.81 \% & 33.28 \% & 23.91 \% & 3 s / 4 cores & T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016.B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.\\
SubCat48LDCF & & 26.68 \% & 34.33 \% & 19.44 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
RT3DStereo & st & 21.41 \% & 25.58 \% & 17.52 \% & 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.\\
CSoR & la & 20.82 \% & 30.65 \% & 17.14 \% & 3.5 s / 4 cores & L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.\\
RT3D & la & 18.96 \% & 24.41 \% & 19.85 \% & 0.09 s / GPU & Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.\\
VoxelJones & & 15.41 \% & 17.83 \% & 14.13 \% & .18 s / 1 core & M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.\\
Associate-3Ddet & & 1.20 \% & 0.52 \% & 1.38 \% & 0.05 s / 1 core & L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.\\
init & & 0.01 \% & 0.01 \% & 0.01 \% & 0.03 s / 1 core & \\
DA3D+KM3D+v2-99 & & 0.00 \% & 0.00 \% & 0.00 \% & 0.120s / GPU & \\
mdab & & 0.00 \% & 0.00 \% & 0.00 \% & 0.02 s / 1 core & \\
DA3D+KM3D & & 0.00 \% & 0.00 \% & 0.00 \% & 0.02 s / GPU & \\
DA3D & & 0.00 \% & 0.00 \% & 0.00 \% & 0.03 s / 1 core &
\end{tabular}