\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 & & 70.57 \% & 81.79 \% & 63.31 \% & 0.06 s / GPU & \\
VMVS & la & 67.66 \% & 78.57 \% & 63.83 \% & 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 & & 66.83 \% & 78.89 \% & 62.06 \% & 1.5 s / GPU & \\
SubCNN & & 66.28 \% & 78.33 \% & 61.37 \% & 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 & 64.32 \% & 72.73 \% & 59.07 \% & 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.\\
Pose-RCNN & & 59.89 \% & 74.10 \% & 54.21 \% & 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.\\
3DOP & st & 59.79 \% & 73.46 \% & 57.04 \% & 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 & & 59.28 \% & 73.37 \% & 56.87 \% & 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.\\
FFNet & & 59.17 \% & 69.17 \% & 54.95 \% & 0.22 s / GPU & \\
Mono3D & & 58.12 \% & 68.58 \% & 54.94 \% & 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.\\
MonoPSR & & 56.30 \% & 70.56 \% & 49.84 \% & 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 & & 52.62 \% & 66.84 \% & 48.72 \% & 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 & & 51.10 \% & 60.85 \% & 46.67 \% & 0.08 s / GPU & \\
PointPillars & la & 49.66 \% & 58.05 \% & 47.88 \% & 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.\\
MMLab-PointRCNN & la & 48.98 \% & 57.49 \% & 46.48 \% & 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.\\
Shift R-CNN (mono) & & 48.81 \% & 65.39 \% & 41.05 \% & 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.\\
FOFNet & la & 47.88 \% & 56.55 \% & 43.04 \% & 0.04 s / GPU & \\
CLF3D & la & 46.86 \% & 62.19 \% & 44.92 \% & 0.13 s / GPU & \\
SCANet & & 45.83 \% & 55.57 \% & 41.03 \% & 0.17 s / >8 cores & \\
AVOD-FPN & la & 44.92 \% & 53.36 \% & 43.77 \% & 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.\\
SECOND & & 43.51 \% & 51.56 \% & 38.78 \% & 38 ms / & Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.\\
SS3D & & 43.45 \% & 52.70 \% & 37.20 \% & 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.\\
CFR & la & 43.34 \% & 56.83 \% & 42.44 \% & 0.06 s / 1 core & \\
DGIST-CellBox & & 43.07 \% & 47.73 \% & 41.00 \% & 0.1 s / GPU & \\
AB3DMOT & la on & 42.30 \% & 51.71 \% & 38.96 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
DPM-VOC+VP & & 39.83 \% & 53.66 \% & 35.73 \% & 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.\\
SCNet & la & 39.19 \% & 46.26 \% & 37.83 \% & 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.\\
VCTNet & & 38.60 \% & 43.57 \% & 36.82 \% & 0.02 s / GPU & \\
HBA-RCNN & & 38.06 \% & 43.81 \% & 35.02 \% & 0.4 s / 1 core & \\
sensekitti & & 37.50 \% & 43.55 \% & 35.08 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
Tencent\_ADlab\_Lidar & la & 37.41 \% & 43.10 \% & 35.88 \% & 0.1 s / GPU & \\
cas\_retina\_1\_13 & & 36.62 \% & 46.29 \% & 35.40 \% & 0.03 s / 4 cores & \\
AVOD & la & 36.38 \% & 44.12 \% & 31.81 \% & 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.\\
TANet & & 36.27 \% & 41.71 \% & 35.19 \% & 0.035s / GPU & \\
A-VoxelNet & & 36.27 \% & 41.58 \% & 35.14 \% & 0.029 s / GPU & \\
AtrousDet & & 36.10 \% & 42.90 \% & 32.09 \% & 0.05 s / & \\
LSVM-MDPM-sv & & 35.49 \% & 47.00 \% & 32.42 \% & 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.\\
IPOD & & 35.32 \% & 41.46 \% & 31.59 \% & 0.2 s / GPU & \\
M3D-RPN & & 35.06 \% & 46.19 \% & 29.90 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
PP\_v1.0 & & 34.25 \% & 39.84 \% & 32.86 \% & 0.02s / 1 core & \\
SubCat & & 34.18 \% & 43.95 \% & 30.76 \% & 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.\\
CHTTL MMF & & 34.17 \% & 43.98 \% & 30.89 \% & 0.1 s / GPU & \\
ELLIOT & la & 34.11 \% & 41.90 \% & 32.18 \% & 0.1 s / 1 core & \\
cascadercnn & & 33.27 \% & 43.05 \% & 28.88 \% & 0.36 s / 4 cores & \\
cas\_retina & & 33.02 \% & 42.79 \% & 31.91 \% & 0.2 s / 4 cores & \\
merge12-12 & & 32.94 \% & 42.47 \% & 31.87 \% & 0.2 s / 4 cores & \\
cas+res+soft & & 32.84 \% & 42.36 \% & 31.66 \% & 0.2 s / 4 cores & \\
MLF & & 32.64 \% & 40.98 \% & 32.33 \% & 0.05 s / GPU & \\
RPN+BF & & 32.55 \% & 40.97 \% & 29.52 \% & 0.6 s / GPU & L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.\\
RFCN & & 32.48 \% & 40.51 \% & 28.66 \% & 0.2 s / 4 cores & \\
X\_MD & & 32.45 \% & 43.55 \% & 31.29 \% & 0.2 s / 1 core & \\
ReSqueeze & & 32.35 \% & 37.95 \% & 30.38 \% & 0.03 s / GPU & \\
bin & & 31.81 \% & 36.25 \% & 29.83 \% & 15ms s / GPU & \\
Complexer-YOLO & la & 31.80 \% & 37.80 \% & 31.26 \% & 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.\\
LPN & & 31.63 \% & 38.40 \% & 28.90 \% & 0.2 s / GPU & \\
centernet & & 31.62 \% & 40.82 \% & 28.94 \% & 0.01 s / GPU & \\
ZKNet & & 31.58 \% & 39.11 \% & 28.78 \% & 0.01 s / GPU & \\
ODES & & 31.43 \% & 36.84 \% & 29.00 \% & 0.02 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
yolo800 & & 31.42 \% & 40.42 \% & 30.50 \% & 0.13 s / 4 cores & \\
detectron & & 31.20 \% & 41.08 \% & 30.78 \% & 0.01 s / 1 core & \\
RFCN\_RFB & & 30.38 \% & 38.00 \% & 26.72 \% & 0.2 s / 4 cores & \\
fasterrcnn & & 29.82 \% & 38.69 \% & 28.45 \% & 0.2 s / 4 cores & \\
NM & & 29.74 \% & 38.40 \% & 27.97 \% & 0.01 s / GPU & \\
MTDP & & 29.04 \% & 36.90 \% & 25.96 \% & 0.15 s / GPU & \\
FD2 & & 28.59 \% & 35.53 \% & 26.02 \% & 0.01 s / GPU & \\
ACF & & 28.46 \% & 35.69 \% & 26.18 \% & 1 s / 1 core & P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.\\
Multi-task DG & & 27.81 \% & 37.18 \% & 27.32 \% & 0.06 s / GPU & \\
cascade\_gw & & 27.51 \% & 36.55 \% & 23.20 \% & 0.2 s / 4 cores & \\
multi-task CNN & & 26.98 \% & 33.58 \% & 23.07 \% & 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.\\
softyolo & & 26.04 \% & 34.86 \% & 25.28 \% & 0.16 s / 4 cores & \\
Cmerge & & 24.19 \% & 34.08 \% & 24.04 \% & 0.2 s / 4 cores & \\
Resnet101Faster rcnn & & 23.89 \% & 30.25 \% & 23.38 \% & 1 s / 1 core & \\
OC Stereo & st & 23.57 \% & 34.19 \% & 22.93 \% & 0.35 s / 1 core & \\
Lidar\_ROI+Yolo(UJS) & & 23.43 \% & 28.50 \% & 19.87 \% & 0.1 s / 1 core & \\
DPM-C8B1 & st & 23.37 \% & 31.08 \% & 20.72 \% & 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 & & 23.23 \% & 28.72 \% & 19.00 \% & 0.2 s / 4 cores & \\
ACF-MR & & 23.18 \% & 29.35 \% & 21.00 \% & 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.\\
KD53-20 & & 22.24 \% & 26.50 \% & 19.80 \% & 0.19 s / 4 cores & \\
rpn & & 22.07 \% & 30.16 \% & 21.44 \% & 0.01 s / 1 core & \\
RT3DStereo & st & 19.84 \% & 23.20 \% & 19.57 \% & 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 & & 18.55 \% & 23.61 \% & 18.34 \% & 2 s / 4 cores & \\
BirdNet & la & 17.26 \% & 21.34 \% & 16.67 \% & 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.\\
softretina & & 0.49 \% & 0.35 \% & 0.50 \% & 0.16 s / 4 cores & \\
JSyolo & & 0.23 \% & 0.20 \% & 0.25 \% & 0.16 s / 4 cores & \\
SN-net & & 0.00 \% & 0.00 \% & 0.00 \% & 0.8 s / GPU &
\end{tabular}