\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
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.\\
OHS & & 78.31 \% & 85.79 \% & 71.24 \% & 0.04 s / 1 core & \\
& & 78.23 \% & 85.65 \% & 71.60 \% & / & \\
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.\\
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.\\
& & 75.59 \% & 83.45 \% & 68.42 \% & / & \\
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 & \\
3DBN\_2 & & 73.69 \% & 87.96 \% & 66.91 \% & 0.12 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
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.\\
FOFNet & la & 72.48 \% & 86.89 \% & 65.63 \% & 0.04 s / GPU & \\
PiP & & 71.10 \% & 82.83 \% & 64.88 \% & 0.05 s / 1 core & \\
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.\\
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.\\
Tencent\_ADlab\_Lidar & la & 65.85 \% & 81.05 \% & 59.17 \% & 0.1 s / GPU & \\
SAANet & & 65.52 \% & 82.29 \% & 58.81 \% & 0.10 s / 1 core & \\
FCY & la & 64.64 \% & 80.76 \% & 58.05 \% & 0.02 s / GPU & \\
Sogo\_MM & & 63.50 \% & 71.57 \% & 55.24 \% & 1.5 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.\\
deprecated & & 63.08 \% & 83.73 \% & 53.51 \% & 0.05 s / GPU & \\
CentrNet-v1 & la & 62.11 \% & 78.10 \% & 55.54 \% & 0.03 s / GPU & \\
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.\\
PP-3D & & 60.09 \% & 76.73 \% & 53.41 \% & 0.1 s / 1 core & \\
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.\\
DDB & la & 58.65 \% & 75.36 \% & 52.85 \% & 0.05 s / GPU & \\
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.\\
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.\\
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 / & \\
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.\\
HG-Mono & & 38.48 \% & 54.04 \% & 32.01 \% & 0.46 s / GPU & \\
SS3D\_HW & & 37.68 \% & 52.40 \% & 32.33 \% & 0.4 s / GPU & \\
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.\\
PG-MonoNet & & 34.11 \% & 49.05 \% & 28.14 \% & 0.19 s / GPU & \\
KNN-GCNN & & 34.03 \% & 39.32 \% & 31.17 \% & 0.4 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.\\
dgist\_multiDetNet & & 32.22 \% & 36.52 \% & 28.66 \% & 0.05 s / 1 core & \\
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.\\
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 & \\
DGIST-CellBox & & 30.34 \% & 35.69 \% & 27.10 \% & 0.1 s / GPU & \\
Faster RCNN + A & & 30.12 \% & 36.03 \% & 26.98 \% & 0.19 s / GPU & \\
bin & & 29.63 \% & 35.40 \% & 25.98 \% & 15ms s / GPU & \\
DP3D & & 28.41 \% & 42.17 \% & 24.02 \% & 0.07 s / GPU & \\
AtrousDet & & 28.26 \% & 34.10 \% & 24.69 \% & 0.05 s / & \\
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 & \\
ReSqueeze & & 27.40 \% & 36.26 \% & 24.04 \% & 0.03 s / GPU & \\
cascadercnn & & 26.59 \% & 33.81 \% & 23.48 \% & 0.36 s / 4 cores & \\
merge12-12 & & 26.39 \% & 33.49 \% & 22.83 \% & 0.2 s / 4 cores & \\
cas+res+soft & & 26.32 \% & 33.63 \% & 22.75 \% & 0.2 s / 4 cores & \\
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 & \\
CSFADet & & 25.77 \% & 32.19 \% & 22.78 \% & 0.05 s / GPU & \\
GA\_BALANCE & & 25.27 \% & 33.79 \% & 22.03 \% & 1 s / 1 core & \\
cas\_retina & & 25.24 \% & 31.74 \% & 22.30 \% & 0.2 s / 4 cores & \\
cas\_retina\_1\_13 & & 25.01 \% & 31.17 \% & 22.12 \% & 0.03 s / 4 cores & \\
Multi-task DG & & 24.72 \% & 33.39 \% & 21.63 \% & 0.06 s / GPU & \\
bigger\_ga & & 24.64 \% & 31.31 \% & 21.06 \% & 1 s / 1 core & \\
CRCNNA & & 23.88 \% & 29.91 \% & 20.70 \% & 0.1 s / 1 core & \\
FD2 & & 23.83 \% & 35.75 \% & 20.79 \% & 0.01 s / GPU & \\
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 & \\
fasterrcnn & & 21.52 \% & 28.50 \% & 18.86 \% & 0.2 s / 4 cores & \\
ZKNet & & 21.51 \% & 28.26 \% & 18.83 \% & 0.01 s / GPU & \\
LPN & & 21.11 \% & 27.67 \% & 18.82 \% & 0.2 s / GPU & \\
RFCN & & 20.77 \% & 26.80 \% & 18.25 \% & 0.2 s / 4 cores & \\
yolo800 & & 20.66 \% & 27.38 \% & 18.77 \% & 0.13 s / 4 cores & \\
RFCN\_RFB & & 20.40 \% & 26.19 \% & 17.91 \% & 0.2 s / 4 cores & \\
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.\\
Cmerge & & 19.78 \% & 27.75 \% & 16.58 \% & 0.2 s / 4 cores & \\
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.\\
cascade\_gw & & 18.74 \% & 27.00 \% & 16.35 \% & 0.2 s / 4 cores & \\
MTDP & & 18.02 \% & 23.30 \% & 16.07 \% & 0.15 s / GPU & \\
BdCost+DA+BB+MS & & 17.73 \% & 23.48 \% & 14.67 \% & TBD s / 4 cores & \\
centernet & & 17.55 \% & 23.39 \% & 15.59 \% & 0.01 s / GPU & \\
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.\\
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.\\
Retinanet100 & & 13.34 \% & 19.09 \% & 11.79 \% & 0.2 s / 4 cores & \\
BdCost+DA+BB & & 13.30 \% & 17.22 \% & 11.04 \% & TBD s / 4 cores & \\
softyolo & & 11.12 \% & 15.91 \% & 9.84 \% & 0.16 s / 4 cores & \\
100Frcnn & & 11.07 \% & 16.90 \% & 9.63 \% & 2 s / 4 cores & \\
Lidar\_ROI+Yolo(UJS) & & 8.95 \% & 13.15 \% & 7.96 \% & 0.1 s / 1 core & \\
RT3D-GMP & st & 8.32 \% & 11.73 \% & 7.24 \% & 0.06 s / GPU & \\
KD53-20 & & 4.86 \% & 7.19 \% & 4.74 \% & 0.19 s / 4 cores & \\
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.\\
Scan\_YOLO & & 2.13 \% & 2.70 \% & 2.11 \% & 0.1 s / 4 cores & \\
MP & & 0.97 \% & 0.62 \% & 0.89 \% & 0.2 s / 1 core & \\
Simple3D Net & & 0.71 \% & 0.77 \% & 0.69 \% & 0.02 s / GPU & \\
CBNet & & 0.18 \% & 0.11 \% & 0.21 \% & 1 s / 4 cores & \\
softretina & & 0.11 \% & 0.07 \% & 0.08 \% & 0.16 s / 4 cores & \\
JSyolo & & 0.02 \% & 0.01 \% & 0.02 \% & 0.16 s / 4 cores & \\
UM3D\_TUM & & 0.00 \% & 0.00 \% & 0.00 \% & 0.05 s / 1 core &
\end{tabular}