\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
BorderAtt & & 82.33 \% & 87.77 \% & 77.37 \% & 0.08 s / 1 core & \\
HUAWEI Octopus & & 82.13 \% & 88.26 \% & 77.41 \% & 0.1 s / 1 core & \\
ADLAB & & 82.08 \% & 90.92 \% & 77.36 \% & 0.08 s / 1 core & \\
RangeRCNN-LV & & 81.85 \% & 88.76 \% & 77.18 \% & 0.1 s / 1 core & \\
PVGNet & & 81.81 \% & 89.94 \% & 77.09 \% & 0.05 s / 1 core & \\
Voxel R-CNN & & 81.62 \% & 90.90 \% & 77.06 \% & 0.04 s / GPU & \\
IC-PVRCNN & & 81.57 \% & 88.60 \% & 77.09 \% & 0.08 s / 1 core & \\
Deformable PV-RCNN & la & 81.46 \% & 88.25 \% & 76.96 \% & 0.08 s / 1 core & P. Bhattacharyya and K. Czarnecki: Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations. ECCV 2020 Perception for Autonomous Driving Workshop.\\
MMLab PV-RCNN & la & 81.43 \% & 90.25 \% & 76.82 \% & 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.\\
PC-RGNN & & 81.38 \% & 87.94 \% & 76.88 \% & 0.1 s / GPU & \\
RangeRCNN & la & 81.33 \% & 88.47 \% & 77.09 \% & 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.\\
HyBrid Feature Det & & 81.01 \% & 87.36 \% & 76.73 \% & 0.08 s / 1 core & \\
OneCoLab SicNet V2 & & 80.94 \% & 87.55 \% & 76.36 \% & 0.08 s / 1 core & \\
Associate-3Ddet\_v2 & & 80.77 \% & 91.53 \% & 75.23 \% & 0.04 s / 1 core & \\
CIA-SSD v2 & la & 80.71 \% & 89.61 \% & 75.06 \% & 0.03 s / 1 core & \\
CLOCs\_PVCas & & 80.67 \% & 88.94 \% & 77.15 \% & 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.\\
AIMC-RUC & & 80.63 \% & 89.90 \% & 75.32 \% & 0.08 s / 1 core & \\
OAP & & 80.63 \% & 89.18 \% & 73.04 \% & 0.06 s / 1 core & \\
HRI-MSP-L & la & 80.62 \% & 87.61 \% & 76.29 \% & 0.07 s / 1 core & \\
IC-SECOND & & 80.61 \% & 88.25 \% & 75.83 \% & 0.06 s / 1 core & \\
CVIS-DF3D\_v2 & & 80.48 \% & 87.20 \% & 76.01 \% & 0.05 s / 1 core & \\
SVGA-Net & la & 80.38 \% & 87.73 \% & 76.27 \% & 0.08 s / GPU & \\
SPANet & & 80.34 \% & 91.05 \% & 74.89 \% & 0.06 s / 1 core & \\
CVRS\_PF & & 80.33 \% & 88.04 \% & 75.21 \% & 0.09 s / 1 core & \\
CIA-SSD & la & 80.28 \% & 89.59 \% & 72.87 \% & 0.03 s / 1 core & \\
Baseline of CA RCNN & & 80.28 \% & 87.45 \% & 76.21 \% & 0.1 s / GPU & \\
CVIS-DF3D & & 80.28 \% & 87.45 \% & 76.21 \% & 0.05 s / 1 core & \\
CBi-GNN & & 80.18 \% & 91.50 \% & 74.76 \% & 0.03 s / 1 core & \\
deprecated & & 80.16 \% & 89.48 \% & 72.75 \% & deprecated / & \\
3D-CVF at SPA & la & 80.05 \% & 89.20 \% & 73.11 \% & 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.\\
CN & & 79.89 \% & 90.55 \% & 76.31 \% & 0.04 s / GPU & \\
VAL & & 79.87 \% & 89.35 \% & 70.27 \% & 0.03 s / 1 core & \\
SA-SSD & & 79.79 \% & 88.75 \% & 74.16 \% & 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.\\
CJJ & & 79.72 \% & 88.98 \% & 74.71 \% & 0.04 s / 1 core & \\
STD & & 79.71 \% & 87.95 \% & 75.09 \% & 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.\\
ISF & & 79.71 \% & 89.13 \% & 74.78 \% & 0.05 s / 1 core & \\
AF\_V1 & & 79.68 \% & 88.16 \% & 72.39 \% & 0.1 s / 1 core & \\
FCY & la & 79.67 \% & 89.19 \% & 74.35 \% & 0.02 s / GPU & \\
scssd-normal(0.3) & & 79.59 \% & 88.97 \% & 72.51 \% & 0.05 s / GPU & P. An, J. Liang, J. Ma, K. Yu and B. Fang: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.\\
3DSSD & & 79.57 \% & 88.36 \% & 74.55 \% & 0.04 s / GPU & Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.\\
PointRes & la & 79.55 \% & 88.73 \% & 74.17 \% & 0.013 s / 1 core & \\
Cas-SSD & & 79.50 \% & 88.73 \% & 72.46 \% & 0.1 s / 1 core & \\
scssd-normal(0.4) & & 79.49 \% & 88.70 \% & 74.25 \% & 0.05 s / 1 core & P. An, J. Liang, J. Ma, K. Yu and B. Fang: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.\\
Point-GNN & la & 79.47 \% & 88.33 \% & 72.29 \% & 0.6 s / GPU & W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.\\
PP-3D & & 79.47 \% & 88.33 \% & 72.29 \% & 0.1 s / 1 core & \\
nonet & & 79.42 \% & 88.28 \% & 75.77 \% & 0.08 s / 1 core & \\
RoIFusion & & 79.41 \% & 88.43 \% & 72.58 \% & 0.22 s / 1 core & \\
EPNet & & 79.28 \% & 89.81 \% & 74.59 \% & 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.\\
AP-RCNN & & 79.27 \% & 87.65 \% & 76.43 \% & 0.02 s / 1 core & \\
MGACNet & & 79.18 \% & 86.20 \% & 74.58 \% & 0.05 s / 1 core & \\
D3D & & 79.15 \% & 87.07 \% & 73.79 \% & 0.02 s / 1 core & \\
NLK-ALL & & 79.13 \% & 87.23 \% & 74.30 \% & 0.04 s / 1 core & \\
3D IoU-Net & & 79.03 \% & 87.96 \% & 72.78 \% & 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.\\
Noah CV Lab - SSL & & 78.99 \% & 85.50 \% & 71.75 \% & 0.1 s / GPU & \\
SERCNN & la & 78.96 \% & 87.74 \% & 74.30 \% & 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.\\
deprecated & & 78.83 \% & 87.89 \% & 73.52 \% & 0.06 s / 1 core & \\
FLID & & 78.78 \% & 86.73 \% & 71.24 \% & 0.04 s / GPU & \\
OneCoLab SicNet & & 78.65 \% & 87.41 \% & 74.16 \% & 0.08 s / 1 core & \\
PVF-NET & & 78.58 \% & 87.05 \% & 71.68 \% & 0.1 s / 1 core & \\
BLPNet\_V2 & & 78.57 \% & 87.10 \% & 71.67 \% & 0.04 s / 1 core & \\
Discrete-PointDet & & 78.51 \% & 88.53 \% & 71.29 \% & 0.02 s / 1 core & \\
MMLab-PartA^2 & la & 78.49 \% & 87.81 \% & 73.51 \% & 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.\\
SRDL & st la & 78.49 \% & 86.75 \% & 74.04 \% & 0.15 s / GPU & \\
F-3DNet & & 78.48 \% & 85.48 \% & 71.62 \% & 0.5 s / GPU & \\
CLOCs\_SecCas & & 78.45 \% & 86.38 \% & 72.45 \% & 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.\\
Patches - EMP & la & 78.41 \% & 89.84 \% & 73.15 \% & 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.\\
LZY\_RCNN & & 78.41 \% & 85.38 \% & 74.04 \% & 0.08 s / 1 core & \\
deprecated & & 78.32 \% & 89.34 \% & 71.21 \% & 0.06 s / GPU & \\
HotSpotNet & & 78.31 \% & 87.60 \% & 73.34 \% & 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.\\
cvMax & & 78.28 \% & 86.60 \% & 71.60 \% & 0.04 s / GPU & \\
KNN-GCNN & & 78.26 \% & 86.37 \% & 71.14 \% & 0.4 s / 1 core & \\
Chovy & & 78.02 \% & 86.86 \% & 73.20 \% & 0.04 s / GPU & \\
deprecated & & 77.97 \% & 86.76 \% & 73.00 \% & 0.04 s / GPU & \\
deprecated & & 77.97 \% & 86.53 \% & 67.90 \% & 0.05 s / 1 core & \\
CenterNet3D & & 77.90 \% & 86.20 \% & 73.03 \% & 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.\\
tbd & & 77.72 \% & 86.09 \% & 72.53 \% & 0.08 s / 1 core & \\
PPBA & & 77.65 \% & 84.16 \% & 71.21 \% & NA s / GPU & \\
TBU & & 77.65 \% & 84.16 \% & 71.21 \% & NA s / GPU & \\
UberATG-MMF & la & 77.43 \% & 88.40 \% & 70.22 \% & 0.08 s / GPU & M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.\\
Associate-3Ddet & & 77.40 \% & 85.99 \% & 70.53 \% & 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.\\
Fast Point R-CNN & la & 77.40 \% & 85.29 \% & 70.24 \% & 0.06 s / GPU & Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.\\
deprecated & & 77.31 \% & 86.44 \% & 70.91 \% & - / & \\
LZnet & & 77.22 \% & 85.71 \% & 70.76 \% & 0.08 s / 1 core & \\
Dccnet & & 77.22 \% & 86.67 \% & 69.97 \% & 0.05 s / 1 core & \\
Patches & la & 77.20 \% & 88.67 \% & 71.82 \% & 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.\\
deprecated & & 77.17 \% & 86.27 \% & 70.83 \% & 0.05 s / GPU & \\
DEFT & & 77.15 \% & 86.34 \% & 70.76 \% & 1 s / GPU & \\
VAR & & 77.08 \% & 84.92 \% & 72.21 \% & 0.1 s / 1 core & \\
CU-PointRCNN & & 76.87 \% & 86.55 \% & 73.17 \% & 0.1 s / GPU & \\
HRI-VoxelFPN & & 76.70 \% & 85.64 \% & 69.44 \% & 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.\\
CLOCs\_PointCas & & 76.68 \% & 87.50 \% & 71.21 \% & 0.1 s / GPU & \\
SARPNET & & 76.64 \% & 85.63 \% & 71.31 \% & 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.\\
TBD & & 76.57 \% & 85.33 \% & 72.05 \% & 0.05 s / GPU & \\
IGRP+ & & 76.54 \% & 86.90 \% & 71.77 \% & 0.18 s / 1 core & \\
3D IoU Loss & la & 76.50 \% & 86.16 \% & 71.39 \% & 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.\\
F-ConvNet & la & 76.39 \% & 87.36 \% & 66.69 \% & 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.\\
PiP & & 76.24 \% & 85.30 \% & 70.45 \% & 0.033 s / 1 core & \\
VICNet & & 76.18 \% & 85.21 \% & 70.60 \% & 0.05 s / 1 core & \\
SegVoxelNet & & 76.13 \% & 86.04 \% & 70.76 \% & 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.\\
NLK-3D & & 76.08 \% & 84.47 \% & 70.93 \% & 0.04 s / 1 core & \\
TANet & & 75.94 \% & 84.39 \% & 68.82 \% & 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.\\
IGRP & & 75.90 \% & 86.27 \% & 69.31 \% & 0.18 s / 1 core & \\
MVX-Net++ & & 75.86 \% & 85.99 \% & 70.70 \% & 0.15 s / 1 core & \\
PointCSE & & 75.82 \% & 86.46 \% & 70.47 \% & 0.02 s / 1 core & \\
PointRGCN & & 75.73 \% & 85.97 \% & 70.60 \% & 0.26 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
IE-PointRCNN & & 75.67 \% & 86.26 \% & 70.47 \% & 0.1 s / 1 core & \\
MMLab-PointRCNN & la & 75.64 \% & 86.96 \% & 70.70 \% & 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.\\
AB3DMOT & la on & 75.43 \% & 86.10 \% & 68.88 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
PPFNet & & 75.43 \% & 85.91 \% & 68.88 \% & 0.1 s / 1 core & \\
MDA & & 75.39 \% & 83.72 \% & 71.98 \% & 0.03 s / 1 core & \\
HR-SECOND & & 75.32 \% & 84.78 \% & 68.70 \% & 0.11 s / 1 core & \\
R-GCN & & 75.26 \% & 83.42 \% & 68.73 \% & 0.16 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
epBRM & la & 75.15 \% & 85.00 \% & 69.84 \% & 0.1 s / GPU & K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.\\
MuRF & & 75.11 \% & 84.81 \% & 69.99 \% & 0.05 s / GPU & \\
3DBN\_2 & & 75.06 \% & 84.90 \% & 72.10 \% & 0.12 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
MAFF-Net(DAF-Pillar) & & 75.04 \% & 85.52 \% & 67.61 \% & 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.\\
PBASN & & 75.02 \% & 83.16 \% & 69.72 \% & NA s / GPU & \\
PI-RCNN & & 74.82 \% & 84.37 \% & 70.03 \% & 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.\\
EPENet & & 74.72 \% & 85.19 \% & 70.05 \% & 0.04 s / 1 core & \\
Pointpillar\_TV & & 74.55 \% & 83.08 \% & 69.13 \% & 0.05 s / 1 core & \\
CentrNet-FG & & 74.47 \% & 83.67 \% & 69.94 \% & 0.03 s / 1 core & \\
RethinkDet3D & & 74.35 \% & 82.81 \% & 67.90 \% & 0.15 s / 1 core & \\
PointPillars & la & 74.31 \% & 82.58 \% & 68.99 \% & 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.\\
Bit & & 74.30 \% & 82.67 \% & 68.73 \% & 0.11 s / 1 core & \\
Prune & & 74.28 \% & 85.03 \% & 67.16 \% & 0.11 s / 1 core & \\
autoRUC & & 74.08 \% & 84.54 \% & 67.04 \% & 0.12 s / 1 core & \\
Simple3D Net & & 74.06 \% & 83.06 \% & 69.17 \% & 0.02 s / 1 core & \\
ARPNET & & 74.04 \% & 84.69 \% & 68.64 \% & 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.\\
PointPiallars\_SECA & & 73.99 \% & 82.62 \% & 69.98 \% & 0.06 s / 1 core & \\
VOXEL\_FPN\_HR & & 73.98 \% & 85.33 \% & 68.67 \% & 0.12 s / 8 cores & ERROR: Wrong syntax in BIBTEX file.\\
tt & & 73.92 \% & 84.14 \% & 69.15 \% & 0.08 s / 1 core & \\
autonet & & 73.83 \% & 82.66 \% & 67.93 \% & 0.12 s / 1 core & \\
PC-CNN-V2 & la & 73.79 \% & 85.57 \% & 65.65 \% & 0.5 s / GPU & X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.\\
C-GCN & & 73.62 \% & 83.49 \% & 67.01 \% & 0.147 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
baseline & & 73.55 \% & 82.92 \% & 67.42 \% & 0.12 s / 1 core & \\
3DBN & la & 73.53 \% & 83.77 \% & 66.23 \% & 0.13s / & X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.\\
BVVF & & 73.34 \% & 80.19 \% & 67.34 \% & 0.1 s / 1 core & \\
SCNet & la & 73.17 \% & 83.34 \% & 67.93 \% & 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.\\
TBD & & 73.02 \% & 82.74 \% & 67.97 \% & 0.04 s / 1 core & \\
DPointNet & & 73.02 \% & 79.25 \% & 68.53 \% & 0.09 s / 1 core & \\
PFF3D & la & 72.93 \% & 81.11 \% & 67.24 \% & 0.05 s / GPU & \\
RUC & & 72.65 \% & 80.76 \% & 68.74 \% & 0.12 s / 1 core & \\
AVOD-FPN & la & 71.76 \% & 83.07 \% & 65.73 \% & 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.\\
PointPainting & la & 71.70 \% & 82.11 \% & 67.08 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
RUC & & 71.40 \% & 80.98 \% & 65.98 \% & 0.12 s / 1 core & \\
RUC & & 71.32 \% & 81.07 \% & 64.69 \% & 0.12 s / 1 core & \\
WS3D & la & 70.59 \% & 80.99 \% & 64.23 \% & 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.\\
F-PointNet & la & 69.79 \% & 82.19 \% & 60.59 \% & 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.\\
RuiRUC & & 69.32 \% & 81.45 \% & 57.64 \% & 0.12 s / 1 core & \\
UberATG-ContFuse & la & 68.78 \% & 83.68 \% & 61.67 \% & 0.06 s / GPU & M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.\\
MLOD & la & 67.76 \% & 77.24 \% & 62.05 \% & 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.\\
AVOD & la & 66.47 \% & 76.39 \% & 60.23 \% & 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.\\
seivl & & 66.40 \% & 77.00 \% & 63.48 \% & 0.1 s / 1 core & \\
DAMNET & & 65.52 \% & 76.25 \% & 59.54 \% & 1 s / 1 core & \\
voxelrcnn & & 64.77 \% & 73.60 \% & 60.05 \% & 15 s / 1 core & \\
MV3D & la & 63.63 \% & 74.97 \% & 54.00 \% & 0.36 s / GPU & X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.\\
RCD & & 60.56 \% & 70.54 \% & 55.58 \% & 0.1 s / GPU & \\
ANM & & 59.07 \% & 74.99 \% & 47.64 \% & 0.12 s / 1 core & \\
A3DODWTDA & la & 56.82 \% & 62.84 \% & 48.12 \% & 0.08 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
PL++ (SDN+GDC) & st la & 54.88 \% & 68.38 \% & 49.16 \% & 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.\\
MV3D (LIDAR) & la & 54.54 \% & 68.35 \% & 49.16 \% & 0.24 s / GPU & X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.\\
CDN & st & 54.22 \% & 74.52 \% & 46.36 \% & 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.\\
tiny-stereo-v2 & st & 54.18 \% & 75.05 \% & 47.16 \% & 0.4 s / 1 core & \\
CG-Stereo & st & 53.58 \% & 74.39 \% & 46.50 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
tiny-stereo-v1 & st & 52.99 \% & 74.27 \% & 45.24 \% & 0.3 s / GPU & \\
DSGN & st & 52.18 \% & 73.50 \% & 45.14 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
SF & st la & 51.92 \% & 58.88 \% & 44.59 \% & 0.5 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
BirdNet+ & la & 51.85 \% & 70.14 \% & 50.03 \% & 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. arXiv:2003.04188 [cs.CV] 2020.\\
Complexer-YOLO & la & 47.34 \% & 55.93 \% & 42.60 \% & 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.\\
CDN-PL++ & st & 44.86 \% & 64.31 \% & 38.11 \% & 0.4 s / GPU & D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. arXiv preprint arXiv:2007.03085 2020.\\
Pseudo-LiDAR E2E & st & 43.92 \% & 64.75 \% & 38.14 \% & 0.4 s / GPU & \\
PB3D & st & 43.27 \% & 64.78 \% & 37.13 \% & 0.42 s / 1 core & \\
Pseudo-LiDAR++ & st & 42.43 \% & 61.11 \% & 36.99 \% & 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.\\
Stereo3D & st & 41.25 \% & 65.68 \% & 30.42 \% & 0.1 s / & \\
Disp R-CNN (velo) & st & 39.36 \% & 59.61 \% & 32.01 \% & 0.42 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.\\
ZoomNet & st & 38.64 \% & 55.98 \% & 30.97 \% & 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.\\
Disp R-CNN & st & 37.93 \% & 58.55 \% & 31.95 \% & 0.42 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.\\
stereo\_sa & st & 37.92 \% & 58.70 \% & 31.99 \% & 0.3 s / GPU & \\
OC Stereo & st & 37.60 \% & 55.15 \% & 30.25 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
RTS3D & & 37.38 \% & 58.51 \% & 31.12 \% & 0.03 s / GPU & \\
Pseudo-Lidar & st & 34.05 \% & 54.53 \% & 28.25 \% & 0.4 s / GPU & Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
m-prcnn & st & 31.21 \% & 53.96 \% & 24.52 \% & 0.43 s / 1 core & \\
Stereo R-CNN & st & 30.23 \% & 47.58 \% & 23.72 \% & 0.3 s / GPU & P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.\\
IDA-3D & st & 29.32 \% & 45.09 \% & 23.13 \% & 0.08 s / 1 core & \\
BirdNet & la & 27.26 \% & 40.99 \% & 25.32 \% & 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.\\
RT3D-GMP & st & 23.83 \% & 32.44 \% & 17.91 \% & 0.06 s / GPU & \\
RT3DStereo & st & 23.28 \% & 29.90 \% & 18.96 \% & 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.\\
ASOD & & 22.37 \% & 38.42 \% & 17.01 \% & 0.28 s / GPU & \\
RT3D & la & 19.14 \% & 23.74 \% & 18.86 \% & 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.\\
StereoFENet & st & 18.41 \% & 29.14 \% & 14.20 \% & 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.\\
ITS-MDPL & & 14.21 \% & 23.81 \% & 12.11 \% & 0.16 s / GPU & \\
PSMD & & 13.57 \% & 21.37 \% & 10.89 \% & 0.1 s / GPU & \\
deprecated & & 13.30 \% & 14.81 \% & 11.04 \% & / 1 core & \\
Det3D & & 13.26 \% & 24.00 \% & 9.94 \% & 0.5 s / 1 core & \\
S3D & & 12.75 \% & 14.58 \% & 10.72 \% & 0.1 s / 1 core & \\
Kinematic3D & & 12.72 \% & 19.07 \% & 9.17 \% & 0.12 s / 1 core & G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .\\
MTMono3d & & 12.44 \% & 18.54 \% & 10.09 \% & 0.05 s / 1 core & \\
DP3D & & 12.24 \% & 18.84 \% & 8.96 \% & 0.07 s / GPU & \\
YoloMono3D & & 12.06 \% & 18.28 \% & 8.42 \% & 0.05 s / GPU & \\
IAFA & & 12.01 \% & 17.81 \% & 10.61 \% & 0.04 s / 1 core & \\
D4LCN & & 11.72 \% & 16.65 \% & 9.51 \% & 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.\\
MP-Mono & & 11.65 \% & 16.78 \% & 9.01 \% & 0.16 s / GPU & \\
MCA & & 11.63 \% & 18.46 \% & 10.24 \% & 0.04 s / 1 core & \\
PG-MonoNet & & 11.51 \% & 15.91 \% & 9.01 \% & 0.19 s / GPU & \\
DA-3Ddet & & 11.50 \% & 16.77 \% & 8.93 \% & 0.4 s / GPU & \\
NL\_M3D & & 11.46 \% & 17.54 \% & 8.98 \% & 0.2 s / 1 core & \\
SSL-RTM3D & & 11.45 \% & 16.73 \% & 9.92 \% & 0.03 s / 1 core & \\
IMA & & 11.34 \% & 16.24 \% & 9.44 \% & 0.1 s / 1 core & \\
CDI3D & & 11.32 \% & 15.70 \% & 9.26 \% & 0.03 s / GPU & \\
LAPNet & & 11.29 \% & 18.02 \% & 8.50 \% & 0.03 s / 1 core & \\
DP3D & & 11.22 \% & 17.27 \% & 8.54 \% & 0.05 s / GPU & \\
LNET & & 11.21 \% & 12.79 \% & 9.94 \% & 0.05 s / 1 core & \\
RefinedMPL & & 11.14 \% & 18.09 \% & 8.94 \% & 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.\\
UM3D\_TUM & & 11.13 \% & 15.30 \% & 9.31 \% & 0.05 s / 1 core & \\
PatchNet & & 11.12 \% & 15.68 \% & 10.17 \% & 0.4 s / 1 core & X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.\\
AM3D & & 10.74 \% & 16.50 \% & 9.52 \% & 0.4 s / GPU & X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.\\
OCM3D & & 10.44 \% & 17.48 \% & 7.87 \% & 0.5 s / 1 core & \\
RTM3D & & 10.34 \% & 14.41 \% & 8.77 \% & 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.\\
MA & & 10.21 \% & 14.90 \% & 8.78 \% & 0.1 s / 1 core & \\
MonoPair & & 9.99 \% & 13.04 \% & 8.65 \% & 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.\\
SMOKE & & 9.76 \% & 14.03 \% & 7.84 \% & 0.03 s / GPU & Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.\\
M3D-RPN & & 9.71 \% & 14.76 \% & 7.42 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
SS3D\_HW & & 9.70 \% & 14.74 \% & 7.22 \% & 0.4 s / GPU & \\
Center3D & & 9.31 \% & 12.01 \% & 8.06 \% & 0.05 s / GPU & \\
TopNet-HighRes & la & 9.28 \% & 12.67 \% & 7.95 \% & 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.\\
Mono3CN & & 9.17 \% & 12.73 \% & 7.91 \% & 0.1 s / 1 core & \\
LCD3D & & 9.04 \% & 13.77 \% & 7.23 \% & 0.03 s / GPU & \\
RAR-Net & & 8.73 \% & 13.70 \% & 6.92 \% & 0.5 s / 1 core & \\
SSL-RTM3D Res18 & & 8.39 \% & 12.65 \% & 7.12 \% & 0.02 s / GPU & \\
SS3D & & 7.68 \% & 10.78 \% & 6.51 \% & 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.\\
anonymous & & 7.66 \% & 15.21 \% & 6.24 \% & 1 s / 1 core & \\
Mono3D\_PLiDAR & & 7.50 \% & 10.76 \% & 6.10 \% & 0.1 s / & X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.\\
MonoPSR & & 7.25 \% & 10.76 \% & 5.85 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
Decoupled-3D & & 7.02 \% & 11.08 \% & 5.63 \% & 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.\\
anonymous & & 6.77 \% & 13.18 \% & 5.63 \% & 1 s / 1 core & \\
VoxelJones & & 6.35 \% & 7.39 \% & 5.80 \% & .18 s / 1 core & M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.\\
MonoGRNet & & 5.74 \% & 9.61 \% & 4.25 \% & 0.04s / & Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.\\
A3DODWTDA (image) & & 5.27 \% & 6.88 \% & 4.45 \% & 0.8 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
MonoFENet & & 5.14 \% & 8.35 \% & 4.10 \% & 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.\\
OACV & & 4.77 \% & 8.13 \% & 3.78 \% & 0.23 s / GPU & \\
TLNet (Stereo) & st & 4.37 \% & 7.64 \% & 3.74 \% & 0.1 s / 1 core & Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
AACL & & 4.18 \% & 5.62 \% & 3.34 \% & 0.1 s / 1 core & \\
CSoR & la & 4.06 \% & 5.61 \% & 3.17 \% & 3.5 s / 4 cores & L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.\\
Shift R-CNN (mono) & & 3.87 \% & 6.88 \% & 2.83 \% & 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.\\
MVRA + I-FRCNN+ & & 3.27 \% & 5.19 \% & 2.49 \% & 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.\\
TopNet-UncEst & la & 3.02 \% & 3.24 \% & 2.26 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
GS3D & & 2.90 \% & 4.47 \% & 2.47 \% & 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.\\
3D-GCK & & 2.52 \% & 3.27 \% & 2.11 \% & 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.\\
SparVox3D & & 2.49 \% & 3.73 \% & 2.09 \% & 0.05 s / GPU & \\
ROI-10D & & 2.02 \% & 4.32 \% & 1.46 \% & 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.\\
FQNet & & 1.51 \% & 2.77 \% & 1.01 \% & 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.\\
3D-SSMFCNN & & 1.41 \% & 1.88 \% & 1.11 \% & 0.1 s / GPU & L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.\\
UDI-mono3D & & 0.41 \% & 0.51 \% & 0.43 \% & 0.05 s / 1 core & \\
PVNet & & 0.00 \% & 0.00 \% & 0.00 \% & 0,1 s / 1 core & \\
ANM & & 0.00 \% & 0.00 \% & 0.00 \% & 0.12 s / 1 core & \\
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}