\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
TANet & & 51.38 \% & 60.85 \% & 47.54 \% & 0.035s / GPU & Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.\\
CentrNet-FG & & 50.87 \% & 60.56 \% & 48.16 \% & 0.03 s / 1 core & \\
Noah CV Lab - SSL & & 50.66 \% & 57.27 \% & 46.55 \% & 0.1 s / GPU & \\
MMLab PV-RCNN & la & 50.57 \% & 59.86 \% & 46.74 \% & 0.08 s / 1 core & S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.\\
HotSpotNet & & 50.53 \% & 57.39 \% & 46.65 \% & 0.04 s / 1 core & Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.\\
VMVS & la & 50.34 \% & 60.34 \% & 46.45 \% & 0.25 s / GPU & J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.\\
AVOD-FPN & la & 50.32 \% & 58.49 \% & 46.98 \% & 0.1 s / & J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.\\
3DSSD & & 49.94 \% & 60.54 \% & 45.73 \% & 0.04 s / GPU & Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.\\
PointPainting & la & 49.93 \% & 58.70 \% & 46.29 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
SemanticVoxels & & 49.93 \% & 58.91 \% & 47.31 \% & 0.04 s / GPU & J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.\\
MMLab-PartA^2 & la & 49.81 \% & 59.04 \% & 45.92 \% & 0.08 s / GPU & S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.\\
F-PointNet & la & 49.57 \% & 57.13 \% & 45.48 \% & 0.17 s / GPU & C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.\\
PPBA & & 49.34 \% & 57.23 \% & 46.86 \% & NA s / GPU & \\
F-ConvNet & la & 48.96 \% & 57.04 \% & 44.33 \% & 0.47 s / GPU & Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.\\
HVNet & & 48.86 \% & 54.84 \% & 46.33 \% & 0.03 s / GPU & M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.\\
RethinkDet3D & & 48.84 \% & 58.96 \% & 46.20 \% & 0.15 s / 1 core & \\
CentrNet-v1 & la & 48.78 \% & 57.58 \% & 45.94 \% & 0.03 s / GPU & \\
STD & & 48.72 \% & 60.02 \% & 44.55 \% & 0.08 s / GPU & Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.\\
PointPillars & la & 48.64 \% & 57.60 \% & 45.78 \% & 16 ms / & A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.\\
DDB & la & 48.35 \% & 57.68 \% & 45.44 \% & 0.05 s / GPU & \\
PiP & & 48.14 \% & 56.16 \% & 45.27 \% & 0.033 s / 1 core & \\
MVX-Net++ & & 48.04 \% & 56.63 \% & 45.44 \% & 0.15 s / 1 core & \\
PPFNet & & 47.92 \% & 55.04 \% & 44.95 \% & 0.1 s / 1 core & \\
Simple3D Net & & 47.27 \% & 56.05 \% & 44.70 \% & 0.02 s / GPU & \\
Point-GNN & la & 47.07 \% & 55.36 \% & 44.61 \% & 0.6 s / GPU & W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.\\
PP-3D & & 47.07 \% & 55.36 \% & 44.61 \% & 0.1 s / 1 core & \\
KNN-GCNN & & 46.77 \% & 55.11 \% & 44.43 \% & 0.4 s / 1 core & \\
TBU & & 46.76 \% & 55.15 \% & 44.60 \% & NA s / GPU & \\
SCNet & la & 46.73 \% & 56.87 \% & 42.74 \% & 0.04 s / GPU & Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.\\
MMLab-PointRCNN & la & 46.13 \% & 54.77 \% & 42.84 \% & 0.1 s / GPU & S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.\\
ARPNET & & 45.92 \% & 55.48 \% & 42.54 \% & 0.08 s / GPU & Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.\\
Deformable PV-RCNN & la & 45.82 \% & 52.03 \% & 43.81 \% & 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.\\
epBRM & la & 45.49 \% & 52.48 \% & 42.75 \% & 0.10 s / 1 core & K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.\\
MLOD & la & 45.40 \% & 55.09 \% & 41.42 \% & 0.12 s / GPU & J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.\\
Baseline of CA RCNN & & 44.85 \% & 52.42 \% & 42.56 \% & 0.1 s / GPU & \\
CVIS-DF3D & & 44.85 \% & 52.42 \% & 42.56 \% & 0.05 s / 1 core & \\
SVGA-Net & la & 44.84 \% & 52.42 \% & 42.56 \% & 0.08 s / GPU & \\
IC-PVRCNN & & 44.13 \% & 48.95 \% & 42.42 \% & 0.08 s / 1 core & \\
MGACNet & & 44.12 \% & 50.98 \% & 41.62 \% & 0.05 s / 1 core & \\
CVIS-DF3D\_v2 & & 43.97 \% & 51.14 \% & 41.94 \% & 0.05 s / 1 core & \\
IC-SECOND & & 43.11 \% & 49.25 \% & 41.35 \% & 0.06 s / 1 core & \\
3DBN\_2 & & 42.97 \% & 50.99 \% & 40.49 \% & 0.12 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
deprecated & & 41.85 \% & 47.88 \% & 40.09 \% & 0.06 s / 1 core & \\
VOXEL\_FPN\_HR & & 41.62 \% & 50.18 \% & 38.30 \% & 0.12 s / 8 cores & ERROR: Wrong syntax in BIBTEX file.\\
TBD & & 41.12 \% & 48.24 \% & 39.06 \% & 0.05 s / GPU & \\
PFF3D & la & 40.94 \% & 48.74 \% & 38.54 \% & 0.05 s / GPU & \\
PBASN & & 40.63 \% & 46.80 \% & 38.41 \% & NA s / GPU & \\
SRDL & st la & 40.30 \% & 47.68 \% & 38.42 \% & 0.15 s / GPU & \\
HR-SECOND & & 40.06 \% & 50.05 \% & 36.47 \% & 0.11 s / 1 core & \\
DAMNET & & 39.30 \% & 49.66 \% & 35.52 \% & 1 s / 1 core & \\
NLK-3D & & 39.22 \% & 49.79 \% & 36.75 \% & 0.04 s / 1 core & \\
AB3DMOT & la on & 38.79 \% & 47.51 \% & 35.85 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
MP & & 38.77 \% & 47.59 \% & 35.50 \% & 0.2 s / 1 core & \\
BirdNet+ & la & 38.28 \% & 45.53 \% & 35.37 \% & 0.1 s / & A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.\\
CSW3D & la & 37.96 \% & 49.27 \% & 33.83 \% & 0.03 s / 4 cores & J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.\\
NLK-ALL & & 37.61 \% & 47.88 \% & 33.86 \% & 0.04 s / 1 core & \\
Pointpillar\_TV & & 35.28 \% & 42.65 \% & 33.10 \% & 0.05 s / 1 core & \\
SparsePool & & 34.15 \% & 43.33 \% & 31.78 \% & 0.13 s / 8 cores & Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.\\
AVOD & la & 33.57 \% & 42.58 \% & 30.14 \% & 0.08 s / & J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.\\
SparsePool & & 33.22 \% & 41.55 \% & 29.66 \% & 0.13 s / 8 cores & Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.\\
FCY & la & 32.64 \% & 41.16 \% & 29.35 \% & 0.02 s / GPU & \\
SF & st la & 29.77 \% & 37.16 \% & 26.61 \% & 0.5 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
CG-Stereo & st & 29.56 \% & 39.24 \% & 25.87 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
Disp R-CNN & st & 25.36 \% & 36.06 \% & 21.62 \% & 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.\\
Disp R-CNN (velo) & st & 24.95 \% & 35.39 \% & 21.30 \% & 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.\\
PB3D & st & 23.62 \% & 33.00 \% & 20.35 \% & 0.42 s / 1 core & \\
BirdNet & la & 23.06 \% & 28.20 \% & 21.65 \% & 0.11 s / & J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
OC Stereo & st & 20.80 \% & 29.79 \% & 18.62 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
Stereo3D & st & 20.76 \% & 31.01 \% & 18.41 \% & 0.1 s / & \\
DSGN & st & 20.75 \% & 26.61 \% & 18.86 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
tiny-stereo-volume-v & & 20.07 \% & 28.64 \% & 17.84 \% & 0.4 s / 1 core & \\
Complexer-YOLO & la & 18.26 \% & 21.42 \% & 17.06 \% & 0.06 s / GPU & M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.\\
TopNet-Retina & la & 14.57 \% & 18.04 \% & 12.48 \% & 52ms / & S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
TopNet-HighRes & la & 13.50 \% & 19.43 \% & 11.93 \% & 101ms / & S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
RefinedMPL & & 7.92 \% & 13.09 \% & 7.25 \% & 0.15 s / GPU & J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.\\
MonoPair & & 7.04 \% & 10.99 \% & 6.29 \% & 0.06 s / GPU & Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.\\
TopNet-DecayRate & la & 6.59 \% & 8.78 \% & 6.25 \% & 92 ms / & S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
RT3D-GMP & st & 5.73 \% & 7.93 \% & 5.62 \% & 0.06 s / GPU & \\
Shift R-CNN (mono) & & 5.66 \% & 8.58 \% & 4.49 \% & 0.25 s / GPU & A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.\\
SS3D\_HW & & 5.47 \% & 8.81 \% & 4.79 \% & 0.4 s / GPU & \\
PG-MonoNet & & 5.43 \% & 7.06 \% & 4.55 \% & 0.19 s / GPU & \\
NL\_M3D & & 4.66 \% & 6.20 \% & 3.99 \% & 0.2 s / 1 core & \\
TopNet-UncEst & la & 4.60 \% & 6.88 \% & 3.79 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
MonoPSR & & 4.56 \% & 7.24 \% & 4.11 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
CDI3D & & 4.55 \% & 6.63 \% & 3.88 \% & 0.03 s / GPU & \\
MP-Mono & & 4.22 \% & 5.87 \% & 3.42 \% & 0.16 s / GPU & \\
M3D-RPN & & 4.05 \% & 5.65 \% & 3.29 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
Mono3CN & & 4.02 \% & 6.03 \% & 3.40 \% & 0.1 s / 1 core & \\
DP3D & & 4.01 \% & 5.71 \% & 3.64 \% & 0.05 s / GPU & \\
DP3D & & 3.86 \% & 5.25 \% & 3.10 \% & 0.07 s / GPU & \\
D4LCN & & 3.86 \% & 5.06 \% & 3.59 \% & 0.2 s / GPU & M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.\\
Center3D & & 3.71 \% & 5.67 \% & 3.52 \% & 0.05 s / GPU & \\
RT3DStereo & st & 3.65 \% & 4.72 \% & 3.00 \% & 0.08 s / GPU & H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.\\
MTMono3d & & 2.38 \% & 3.11 \% & 1.89 \% & 0.05 s / 1 core & \\
SS3D & & 2.09 \% & 2.48 \% & 1.61 \% & 48 ms / & E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.\\
UM3D\_TUM & & 1.79 \% & 3.60 \% & 1.79 \% & 0.05 s / 1 core & \\
UDI-mono3D & & 1.42 \% & 2.09 \% & 1.07 \% & 0.05 s / 1 core & \\
PVNet & & 0.01 \% & 0.00 \% & 0.01 \% & 0,1 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}