\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
THICV-YDM & & 69.07 \% & 83.00 \% & 62.54 \% & 0.06 s / GPU & \\
VMVS & la & 68.19 \% & 79.98 \% & 63.18 \% & 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.\\
Sogo\_MM & & 67.31 \% & 80.02 \% & 61.99 \% & 1.5 s / GPU & \\
SubCNN & & 66.70 \% & 79.65 \% & 61.35 \% & 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.\\
F-ConvNet & la & 63.87 \% & 75.19 \% & 58.57 \% & 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.\\
3DOP & st & 61.48 \% & 74.22 \% & 55.89 \% & 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.\\
DeepStereoOP & & 60.15 \% & 73.76 \% & 55.30 \% & 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.\\
Pose-RCNN & & 59.84 \% & 76.24 \% & 53.59 \% & 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.\\
FFNet & & 58.87 \% & 69.24 \% & 53.75 \% & 1.07 s / GPU & \\
Mono3D & & 58.66 \% & 71.19 \% & 53.94 \% & 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.\\
OHS & & 56.89 \% & 66.97 \% & 52.75 \% & 0.03 s / 1 core & \\
MonoPSR & & 54.65 \% & 68.98 \% & 50.07 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
FRCNN+Or & & 52.15 \% & 67.03 \% & 47.14 \% & 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.\\
ARPNET & & 48.49 \% & 60.47 \% & 45.02 \% & 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 & 48.05 \% & 57.47 \% & 45.40 \% & 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.\\
PPFNet & & 47.73 \% & 55.78 \% & 44.56 \% & 0.1 s / 1 core & \\
MMLab-PointRCNN & la & 47.33 \% & 57.19 \% & 44.31 \% & 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.\\
CLF3D & la & 47.17 \% & 62.48 \% & 41.29 \% & 0.13 s / GPU & \\
Shift R-CNN (mono) & & 46.56 \% & 64.73 \% & 41.86 \% & 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.\\
VOXEL\_FPN\_HR & & 45.65 \% & 56.17 \% & 42.10 \% & 0.12 s / 8 cores & ERROR: Wrong syntax in BIBTEX file.\\
FOFNet & la & 44.33 \% & 55.61 \% & 40.85 \% & 0.04 s / GPU & \\
AVOD-FPN & la & 43.99 \% & 53.48 \% & 41.56 \% & 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.\\
DGIST-CellBox & & 43.86 \% & 48.68 \% & 41.52 \% & 0.1 s / GPU & \\
DDB & la & 43.21 \% & 52.02 \% & 40.81 \% & 0.05 s / GPU & \\
PiP & & 42.76 \% & 51.23 \% & 40.06 \% & 0.05 s / 1 core & \\
MonoPair & & 42.38 \% & 55.26 \% & 38.53 \% & 0.06 s / GPU & \\
SCANet & & 42.12 \% & 54.48 \% & 38.64 \% & 0.17 s / >8 cores & \\
HR-SECOND & & 40.81 \% & 51.12 \% & 37.48 \% & 0.11 s / 1 core & \\
CFR & la & 40.29 \% & 53.96 \% & 37.87 \% & 0.06 s / 1 core & \\
MagnifierNet & & 39.95 \% & 47.52 \% & 37.08 \% & 0.2 s / GPU & \\
CentrNet-v1 & la & 39.83 \% & 46.21 \% & 38.05 \% & 0.03 s / GPU & \\
AB3DMOT & la on & 39.76 \% & 50.30 \% & 36.90 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
SS3D & & 39.60 \% & 53.72 \% & 35.40 \% & 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.\\
SECOND & & 39.53 \% & 50.18 \% & 36.25 \% & 38 ms / & Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.\\
VCTNet & & 39.36 \% & 44.15 \% & 36.79 \% & 0.02 s / GPU & \\
CSFADet & & 38.41 \% & 46.75 \% & 35.44 \% & 0.05 s / GPU & \\
HBA-RCNN & & 38.10 \% & 44.41 \% & 35.27 \% & 0.4 s / 1 core & \\
DPM-VOC+VP & & 37.79 \% & 52.91 \% & 33.27 \% & 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.\\
Tencent\_ADlab\_Lidar & la & 37.23 \% & 44.01 \% & 35.54 \% & 0.1 s / GPU & \\
A-VoxelNet & & 36.24 \% & 42.48 \% & 34.36 \% & 0.029 s / GPU & \\
TANet & & 36.21 \% & 42.54 \% & 34.39 \% & 0.035s / GPU & \\
SAANet & & 36.08 \% & 46.09 \% & 34.14 \% & 0.10 s / 1 core & \\
AtrousDet & & 35.85 \% & 44.79 \% & 32.12 \% & 0.05 s / & \\
SCNet & la & 35.49 \% & 44.50 \% & 33.38 \% & 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.\\
IPOD & & 34.31 \% & 42.37 \% & 31.61 \% & 0.2 s / GPU & Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.\\
sensekitti & & 34.26 \% & 41.03 \% & 31.51 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
merge12-12 & & 34.10 \% & 43.60 \% & 30.43 \% & 0.2 s / 4 cores & \\
PP\_v1.0 & & 34.09 \% & 40.38 \% & 32.43 \% & 0.02s / 1 core & \\
cas+res+soft & & 34.01 \% & 43.51 \% & 30.28 \% & 0.2 s / 4 cores & \\
cas\_retina & & 33.98 \% & 43.80 \% & 31.12 \% & 0.2 s / 4 cores & \\
cas\_retina\_1\_13 & & 33.87 \% & 43.55 \% & 30.99 \% & 0.03 s / 4 cores & \\
DG3D & & 33.62 \% & 46.73 \% & 28.71 \% & 0.2 s / GPU & \\
SparsePool & & 33.35 \% & 43.86 \% & 29.99 \% & 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.\\
SparsePool & & 33.29 \% & 43.52 \% & 30.01 \% & 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.\\
LSVM-MDPM-sv & & 33.01 \% & 45.60 \% & 29.27 \% & 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.\\
PG-MonoNet & & 32.67 \% & 44.75 \% & 29.33 \% & 0.19 s / GPU & \\
cascadercnn & & 32.59 \% & 43.37 \% & 29.73 \% & 0.36 s / 4 cores & \\
ReSqueeze & & 32.47 \% & 38.49 \% & 30.04 \% & 0.03 s / GPU & \\
AVOD & la & 32.19 \% & 42.54 \% & 29.09 \% & 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.\\
Complexer-YOLO & la & 32.13 \% & 37.32 \% & 28.94 \% & 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.\\
yolo800 & & 32.12 \% & 40.53 \% & 28.83 \% & 0.13 s / 4 cores & \\
RPN+BF & & 32.12 \% & 41.19 \% & 28.83 \% & 0.6 s / GPU & L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.\\
bin & & 31.94 \% & 36.94 \% & 29.50 \% & 15ms s / GPU & \\
M3D-RPN & & 31.88 \% & 44.33 \% & 28.55 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
Point-GNN & la & 31.86 \% & 39.16 \% & 29.65 \% & 0.6 s / GPU & \\
ODES & & 31.79 \% & 37.79 \% & 28.66 \% & 0.02 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
SubCat & & 31.26 \% & 42.31 \% & 27.39 \% & 1.2 s / 6 cores & E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.\\
ZKNet & & 31.21 \% & 39.55 \% & 28.61 \% & 0.01 s / GPU & \\
RFCN & & 30.97 \% & 40.51 \% & 27.45 \% & 0.2 s / 4 cores & \\
LPN & & 30.84 \% & 38.60 \% & 28.49 \% & 0.2 s / GPU & \\
X\_MD & & 30.57 \% & 40.97 \% & 27.47 \% & 0.2 s / 1 core & \\
CHTTL MMF & & 30.45 \% & 41.08 \% & 27.57 \% & 0.1 s / GPU & \\
ELLIOT & la & 30.21 \% & 38.79 \% & 28.00 \% & 0.1 s / 1 core & \\
RFCN\_RFB & & 29.91 \% & 38.71 \% & 26.50 \% & 0.2 s / 4 cores & \\
deprecated & & 29.74 \% & 37.71 \% & 27.25 \% & 0.05 s / GPU & \\
NM & & 29.60 \% & 38.81 \% & 26.99 \% & 0.01 s / GPU & \\
fasterrcnn & & 29.48 \% & 38.63 \% & 26.89 \% & 0.2 s / 4 cores & \\
Multi-task DG & & 28.71 \% & 38.97 \% & 26.13 \% & 0.06 s / GPU & \\
detectron & & 28.68 \% & 39.55 \% & 25.97 \% & 0.01 s / 1 core & \\
FD2 & & 28.40 \% & 35.59 \% & 25.75 \% & 0.01 s / GPU & \\
MTDP & & 28.24 \% & 37.49 \% & 25.57 \% & 0.15 s / GPU & \\
centernet & & 27.53 \% & 37.41 \% & 24.35 \% & 0.01 s / GPU & \\
cascade\_gw & & 26.32 \% & 36.41 \% & 23.73 \% & 0.2 s / 4 cores & \\
Cmerge & & 25.09 \% & 34.53 \% & 22.43 \% & 0.2 s / 4 cores & \\
ACF & & 24.31 \% & 32.23 \% & 21.70 \% & 1 s / 1 core & P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.\\
Resnet101Faster rcnn & & 23.70 \% & 30.19 \% & 21.55 \% & 1 s / 1 core & \\
multi-task CNN & & 22.80 \% & 30.30 \% & 20.47 \% & 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.\\
ACF-MR & & 22.61 \% & 29.23 \% & 20.08 \% & 0.6 s / 1 core & R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.\\
OC Stereo & st & 22.02 \% & 31.36 \% & 20.20 \% & 0.35 s / 1 core & \\
Retinanet100 & & 21.71 \% & 29.72 \% & 19.12 \% & 0.2 s / 4 cores & \\
softyolo & & 21.56 \% & 30.46 \% & 20.01 \% & 0.16 s / 4 cores & \\
Lidar\_ROI+Yolo(UJS) & & 19.43 \% & 26.83 \% & 17.14 \% & 0.1 s / 1 core & \\
KD53-20 & & 19.36 \% & 25.10 \% & 17.54 \% & 0.19 s / 4 cores & \\
DPM-C8B1 & st & 19.17 \% & 27.79 \% & 16.48 \% & 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.\\
rpn & & 17.79 \% & 24.35 \% & 15.45 \% & 0.01 s / 1 core & \\
BirdNet & la & 16.45 \% & 21.07 \% & 15.65 \% & 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.\\
RefinedMPL & & 16.13 \% & 24.36 \% & 14.28 \% & 0.1 s / GPU & \\
RT3DStereo & st & 15.34 \% & 21.41 \% & 13.23 \% & 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.\\
100Frcnn & & 12.37 \% & 19.41 \% & 10.92 \% & 2 s / 4 cores & \\
MP & & 5.39 \% & 6.41 \% & 5.14 \% & 0.2 s / 1 core & \\
softretina & & 0.13 \% & 0.10 \% & 0.14 \% & 0.16 s / 4 cores & \\
JSyolo & & 0.06 \% & 0.11 \% & 0.07 \% & 0.16 s / 4 cores & \\
SN-net & & 0.00 \% & 0.00 \% & 0.00 \% & 0.8 s / GPU &
\end{tabular}