\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
HRI-MSP-L & la & 82.89 \% & 91.97 \% & 75.38 \% & 0.07 s / 1 core & \\
Deformable PV-RCNN & la & 80.05 \% & 88.52 \% & 74.20 \% & 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 & 79.70 \% & 86.43 \% & 72.96 \% & 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 & & 78.31 \% & 85.79 \% & 71.24 \% & 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.\\
IC-PVRCNN & & 77.69 \% & 88.39 \% & 71.45 \% & 0.08 s / 1 core & \\
MMLab-PartA^2 & la & 77.52 \% & 88.70 \% & 70.41 \% & 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.\\
PointPainting & la & 76.92 \% & 87.33 \% & 68.21 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
TBD & & 76.79 \% & 87.00 \% & 70.00 \% & 0.05 s / GPU & \\
F-ConvNet & la & 76.71 \% & 86.39 \% & 66.92 \% & 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.\\
CVIS-DF3D\_v2 & & 76.47 \% & 86.19 \% & 69.81 \% & 0.05 s / 1 core & \\
IC-SECOND & & 75.91 \% & 85.69 \% & 70.39 \% & 0.06 s / 1 core & \\
deprecated & & 75.78 \% & 83.89 \% & 70.36 \% & 0.06 s / 1 core & \\
VOXEL\_FPN\_HR & & 74.77 \% & 87.41 \% & 68.16 \% & 0.12 s / 8 cores & ERROR: Wrong syntax in BIBTEX file.\\
MVX-Net++ & & 74.65 \% & 86.53 \% & 67.43 \% & 0.15 s / 1 core & \\
RethinkDet3D & & 74.33 \% & 88.54 \% & 65.20 \% & 0.15 s / 1 core & \\
SRDL & st la & 74.24 \% & 87.85 \% & 67.84 \% & 0.15 s / GPU & \\
3DBN\_2 & & 73.69 \% & 87.96 \% & 66.91 \% & 0.12 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
MGACNet & & 73.43 \% & 85.32 \% & 66.87 \% & 0.05 s / 1 core & \\
NLK-ALL & & 73.32 \% & 86.61 \% & 66.56 \% & 0.04 s / 1 core & \\
Baseline of CA RCNN & & 73.22 \% & 85.17 \% & 66.44 \% & 0.1 s / GPU & \\
CVIS-DF3D & & 73.22 \% & 85.17 \% & 66.44 \% & 0.05 s / 1 core & \\
SVGA-Net & la & 73.21 \% & 85.22 \% & 66.45 \% & 0.08 s / GPU & \\
MMLab-PointRCNN & la & 72.81 \% & 85.94 \% & 65.84 \% & 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.\\
PiP & & 71.10 \% & 82.83 \% & 64.88 \% & 0.033 s / 1 core & \\
PVNet & & 70.50 \% & 83.44 \% & 64.47 \% & 0,1 s / 1 core & \\
NLK-3D & & 70.10 \% & 85.69 \% & 63.27 \% & 0.04 s / 1 core & \\
AP-RCNN & & 69.74 \% & 86.02 \% & 62.90 \% & 0.02 s / 1 core & \\
FCY & la & 69.71 \% & 82.71 \% & 63.29 \% & 0.02 s / GPU & \\
HR-SECOND & & 69.60 \% & 82.42 \% & 62.47 \% & 0.11 s / 1 core & \\
AB3DMOT & la on & 69.54 \% & 82.18 \% & 62.98 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
ARPNET & & 68.72 \% & 82.61 \% & 62.00 \% & 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.\\
PointPillars & la & 68.55 \% & 83.79 \% & 61.71 \% & 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.\\
IGRP+ & & 67.25 \% & 83.67 \% & 60.84 \% & 0.18 s / 1 core & \\
VICNet & & 66.83 \% & 86.12 \% & 59.74 \% & 0.05 s / 1 core & \\
CentrNet-FG & & 66.68 \% & 82.23 \% & 59.21 \% & 0.03 s / 1 core & \\
TANet & & 66.37 \% & 81.15 \% & 60.10 \% & 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.\\
Pointpillar\_TV & & 65.12 \% & 78.88 \% & 58.73 \% & 0.05 s / 1 core & \\
PFF3D & la & 64.06 \% & 78.02 \% & 58.06 \% & 0.05 s / GPU & \\
SubCNN & & 63.36 \% & 71.97 \% & 55.42 \% & 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.\\
Pose-RCNN & & 62.02 \% & 75.74 \% & 53.99 \% & 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.\\
SCNet & la & 61.11 \% & 77.77 \% & 54.82 \% & 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.\\
AVOD-FPN & la & 58.70 \% & 69.21 \% & 53.47 \% & 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.\\
Deep3DBox & & 58.56 \% & 68.31 \% & 50.30 \% & 1.5 s / GPU & A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.\\
3DOP & st & 58.45 \% & 72.24 \% & 51.91 \% & 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.\\
Complexer-YOLO & la & 58.28 \% & 65.41 \% & 54.27 \% & 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.\\
DeepStereoOP & & 56.55 \% & 69.36 \% & 49.37 \% & 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.\\
Mono3D & & 53.96 \% & 67.33 \% & 47.91 \% & 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.\\
DAMNET & & 51.78 \% & 69.61 \% & 47.49 \% & 1 s / 1 core & \\
AVOD & la & 51.05 \% & 64.81 \% & 45.12 \% & 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.\\
BirdNet+ & la & 50.94 \% & 69.92 \% & 47.01 \% & 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.\\
Mono3CN & & 50.58 \% & 66.58 \% & 45.21 \% & 0.1 s / 1 core & \\
FRCNN+Or & & 49.53 \% & 63.45 \% & 43.65 \% & 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.\\
MonoPSR & & 49.32 \% & 58.63 \% & 43.05 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
BirdNet & la & 45.03 \% & 62.69 \% & 41.88 \% & 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.\\
SparsePool & & 43.50 \% & 59.77 \% & 39.36 \% & 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.\\
UDI-mono3D & & 42.21 \% & 57.16 \% & 36.30 \% & 0.05 s / 1 core & \\
NL\_M3D & & 41.19 \% & 57.44 \% & 36.24 \% & 0.2 s / 1 core & \\
sensekitti & & 41.14 \% & 47.48 \% & 35.07 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
CG-Stereo & st & 40.64 \% & 60.24 \% & 35.55 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
MonoPair & & 39.47 \% & 53.36 \% & 33.95 \% & 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.\\
MTMono3d & & 39.06 \% & 55.32 \% & 31.70 \% & 0.05 s / 1 core & \\
MP-Mono & & 38.27 \% & 53.06 \% & 31.65 \% & 0.16 s / GPU & \\
SS3D\_HW & & 37.68 \% & 52.40 \% & 32.33 \% & 0.4 s / GPU & \\
LZnet & & 37.26 \% & 43.58 \% & 34.06 \% & 0.08 s / 1 core & \\
Disp R-CNN (velo) & st & 35.77 \% & 50.66 \% & 30.96 \% & 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 & st & 35.76 \% & 50.64 \% & 30.95 \% & 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.\\
Shift R-CNN (mono) & & 34.77 \% & 51.95 \% & 31.10 \% & 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.\\
SparsePool & & 34.56 \% & 43.33 \% & 31.09 \% & 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.\\
KNN-GCNN & & 34.03 \% & 39.32 \% & 31.17 \% & 0.4 s / 1 core & \\
MMCOM & & 32.52 \% & 35.29 \% & 28.89 \% & 0.04 s / 1 core & \\
HWFD & & 32.51 \% & 35.23 \% & 28.94 \% & 0.21 s / & \\
Point-GNN & la & 32.37 \% & 36.29 \% & 29.81 \% & 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 & & 32.37 \% & 36.29 \% & 29.81 \% & 0.1 s / 1 core & \\
dgist\_multiDetNet & & 31.84 \% & 36.92 \% & 28.02 \% & 0.08 s / & \\
D4LCN & & 31.70 \% & 48.03 \% & 26.99 \% & 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 & & 31.68 \% & 51.51 \% & 28.45 \% & 0.05 s / GPU & \\
Faster RCNN + Gr + A & & 31.55 \% & 36.35 \% & 28.43 \% & 1.29 s / GPU & \\
M3D-RPN & & 31.09 \% & 48.11 \% & 26.10 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
Faster RCNN + A & & 30.81 \% & 36.25 \% & 27.51 \% & 0.19 s / GPU & \\
Faster RCNN + G & & 30.61 \% & 36.19 \% & 27.22 \% & 1.1 s / GPU & \\
Multi-task DG & & 30.31 \% & 35.07 \% & 26.90 \% & 0.06 s / GPU & \\
Faster RCNN + A & & 30.12 \% & 36.03 \% & 26.98 \% & 0.19 s / GPU & \\
HR-faster-rcnn & & 29.82 \% & 36.89 \% & 26.45 \% & 0.1 s / 1 core & \\
DP3D & & 28.41 \% & 42.17 \% & 24.02 \% & 0.07 s / GPU & \\
SS3D & & 27.79 \% & 42.95 \% & 24.26 \% & 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.\\
DP3D & & 27.47 \% & 40.80 \% & 24.16 \% & 0.05 s / GPU & \\
PG-MonoNet & & 26.37 \% & 35.44 \% & 23.38 \% & 0.19 s / GPU & \\
GA2500 & & 26.08 \% & 32.91 \% & 22.06 \% & 0.2 s / 1 core & \\
GA\_rpn500 & & 26.08 \% & 32.91 \% & 22.06 \% & 1 s / 1 core & \\
DAM & & 26.05 \% & 34.25 \% & 22.30 \% & 1 s / GPU & \\
GA\_FULLDATA & & 25.80 \% & 33.35 \% & 22.70 \% & 1 s / 4 cores & \\
GA\_BALANCE & & 25.27 \% & 33.79 \% & 22.03 \% & 1 s / 1 core & \\
PMN & & 25.14 \% & 31.98 \% & 21.92 \% & 0.2 s / 1 core & \\
bigger\_ga & & 24.64 \% & 31.31 \% & 21.06 \% & 1 s / 1 core & \\
LAPNet & & 24.40 \% & 38.54 \% & 20.17 \% & 0.03 s / 1 core & \\
yolo4\_5l & & 23.96 \% & 31.36 \% & 21.02 \% & 0.02 s / 1 core & \\
PB3D & st & 23.93 \% & 38.00 \% & 21.58 \% & 0.42 s / 1 core & \\
CRCNNA & & 23.88 \% & 29.91 \% & 20.70 \% & 0.1 s / 1 core & \\
GAFM & & 22.84 \% & 31.62 \% & 19.88 \% & 0.5 s / 1 core & \\
JSU-NET & & 22.83 \% & 31.58 \% & 19.81 \% & 0.1 s / 1 core & \\
yolo4 & & 22.04 \% & 30.58 \% & 19.33 \% & 0.02 s / 1 core & \\
ga50 & & 21.59 \% & 29.77 \% & 18.77 \% & 1 s / 1 core & \\
Mag & & 21.08 \% & 28.60 \% & 18.58 \% & 0.07 s / 1 core & \\
yolo4\_5l & & 20.79 \% & 28.67 \% & 18.35 \% & 0.02 s / 1 core & \\
DSGN & st & 20.28 \% & 29.76 \% & 19.13 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
CDI3D & & 20.12 \% & 24.76 \% & 18.37 \% & 0.03 s / GPU & \\
LSVM-MDPM-sv & & 19.15 \% & 26.05 \% & 18.02 \% & 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.\\
OC Stereo & st & 18.99 \% & 29.07 \% & 16.40 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
DPM-VOC+VP & & 18.92 \% & 27.97 \% & 17.43 \% & 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.\\
Scan\_YOLO & & 18.63 \% & 27.15 \% & 16.42 \% & 0.1 s / 4 cores & \\
yolo\_rgb & & 17.93 \% & 26.18 \% & 16.30 \% & 0.07 s / GPU & \\
BdCost+DA+BB+MS & & 17.73 \% & 23.48 \% & 14.67 \% & TBD s / 4 cores & \\
RefinedMPL & & 16.02 \% & 26.54 \% & 13.20 \% & 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.\\
yolo\_depth & & 15.96 \% & 21.45 \% & 14.21 \% & 0.07 s / GPU & \\
DPM-C8B1 & st & 14.64 \% & 23.93 \% & 13.09 \% & 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.\\
EACV & & 14.32 \% & 20.90 \% & 12.56 \% & 0.04 s / 1 core & \\
BdCost+DA+BB & & 13.30 \% & 17.22 \% & 11.04 \% & TBD s / 4 cores & \\
RT3D-GMP & st & 8.32 \% & 11.73 \% & 7.24 \% & 0.06 s / GPU & \\
RT3DStereo & st & 3.88 \% & 5.46 \% & 3.54 \% & 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.\\
Simple3D Net & & 0.71 \% & 0.77 \% & 0.69 \% & 0.02 s / GPU & \\
CBNet & & 0.18 \% & 0.11 \% & 0.21 \% & 1 s / 4 cores & \\
UM3D\_TUM & & 0.00 \% & 0.00 \% & 0.00 \% & 0.05 s / 1 core &
\end{tabular}