\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-PartA^2 & la & 76.74 \% & 85.37 \% & 69.63 \% & 0.08 s / GPU & S. Shi, Z. Wang, X. Wang and H. Li: Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. arXiv preprint arXiv:1907.03670 2019.\\
F-ConvNet & la & 74.96 \% & 84.38 \% & 66.45 \% & 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.\\
MMLab-PointRCNN & la & 72.35 \% & 83.40 \% & 65.50 \% & 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 & 71.04 \% & 83.20 \% & 63.86 \% & 0.04 s / GPU & \\
AB3DMOT & la on & 68.91 \% & 80.68 \% & 62.30 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
ARPNET & & 68.28 \% & 80.71 \% & 61.86 \% & 0.08 s / GPU & \\
PointPillars & la & 68.16 \% & 82.43 \% & 61.96 \% & 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.\\
A-VoxelNet & & 66.02 \% & 80.19 \% & 59.21 \% & 0.029 s / GPU & \\
Tencent\_ADlab\_Lidar & la & 65.60 \% & 80.04 \% & 59.60 \% & 0.1 s / GPU & \\
Sogo\_MM & & 63.59 \% & 70.70 \% & 56.15 \% & 1.5 s / GPU & \\
SubCNN & & 63.41 \% & 71.39 \% & 56.34 \% & 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 & & 62.25 \% & 74.85 \% & 55.09 \% & 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.\\
SCANet & & 61.96 \% & 72.59 \% & 55.26 \% & 0.17 s / >8 cores & \\
Deep3DBox & & 59.37 \% & 68.58 \% & 51.97 \% & 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 & 58.59 \% & 71.95 \% & 52.35 \% & 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.\\
PP\_v1.0 & & 58.34 \% & 74.19 \% & 52.29 \% & 0.02s / 1 core & \\
CFR & la & 57.83 \% & 74.57 \% & 55.63 \% & 0.06 s / 1 core & \\
AVOD-FPN & la & 57.53 \% & 67.61 \% & 54.16 \% & 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 & & 57.20 \% & 80.97 \% & 55.14 \% & 38 ms / & Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.\\
ELLIOT & la & 56.42 \% & 74.07 \% & 51.61 \% & 0.1 s / 1 core & \\
Complexer-YOLO & la & 56.32 \% & 64.51 \% & 56.23 \% & 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 & & 55.62 \% & 67.49 \% & 48.85 \% & 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.\\
AVOD & la & 54.43 \% & 64.36 \% & 47.67 \% & 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.\\
Mono3D & & 53.11 \% & 65.74 \% & 48.87 \% & 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.\\
FRCNN+Or & & 50.91 \% & 63.41 \% & 45.46 \% & 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 & & 49.30 \% & 58.93 \% & 43.45 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
CLF3D & la & 46.66 \% & 64.55 \% & 39.30 \% & 0.13 s / GPU & \\
X\_MD & & 45.90 \% & 61.86 \% & 39.14 \% & 0.2 s / 1 core & \\
sensekitti & & 42.12 \% & 46.65 \% & 36.66 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
VCTNet & & 37.64 \% & 46.21 \% & 33.46 \% & 0.02 s / GPU & \\
Shift R-CNN (mono) & & 34.77 \% & 54.31 \% & 34.04 \% & 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.\\
ODES & & 33.74 \% & 37.75 \% & 30.34 \% & 0.02 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
M3D-RPN & & 33.07 \% & 51.41 \% & 31.46 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
SS3D & & 31.17 \% & 44.77 \% & 28.96 \% & 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.\\
BirdNet & la & 30.76 \% & 41.48 \% & 28.66 \% & 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.\\
bin & & 29.53 \% & 34.66 \% & 25.98 \% & 15ms s / GPU & \\
IPOD & & 28.88 \% & 36.04 \% & 25.66 \% & 0.2 s / GPU & \\
AtrousDet & & 28.47 \% & 33.27 \% & 25.62 \% & 0.05 s / & \\
ReSqueeze & & 27.40 \% & 35.39 \% & 24.32 \% & 0.03 s / GPU & \\
LPN & & 27.01 \% & 32.96 \% & 25.01 \% & 0.2 s / GPU & \\
merge12-12 & & 26.91 \% & 32.25 \% & 23.72 \% & 0.2 s / 4 cores & \\
cas+res+soft & & 26.83 \% & 32.33 \% & 23.63 \% & 0.2 s / 4 cores & \\
cascadercnn & & 26.62 \% & 33.02 \% & 23.01 \% & 0.36 s / 4 cores & \\
detectron & & 26.36 \% & 27.44 \% & 23.20 \% & 0.01 s / 1 core & \\
Multi-task DG & & 26.19 \% & 34.00 \% & 22.94 \% & 0.06 s / GPU & \\
FD2 & & 24.65 \% & 35.58 \% & 21.97 \% & 0.01 s / GPU & \\
cas\_retina & & 24.58 \% & 30.82 \% & 23.79 \% & 0.2 s / 4 cores & \\
cas\_retina\_1\_13 & & 24.37 \% & 30.31 \% & 25.81 \% & 0.03 s / 4 cores & \\
DPM-VOC+VP & & 23.22 \% & 31.24 \% & 21.62 \% & 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.\\
LSVM-MDPM-sv & & 23.14 \% & 28.89 \% & 22.28 \% & 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.\\
NM & & 22.22 \% & 27.72 \% & 20.40 \% & 0.01 s / GPU & \\
fasterrcnn & & 22.08 \% & 28.60 \% & 19.31 \% & 0.2 s / 4 cores & \\
yolo800 & & 21.69 \% & 28.20 \% & 19.53 \% & 0.13 s / 4 cores & \\
a & & 21.65 \% & 31.22 \% & 20.51 \% & 0.35 s / 1 core & \\
ZKNet & & 21.50 \% & 28.20 \% & 19.12 \% & 0.01 s / GPU & \\
RFCN & & 20.80 \% & 26.55 \% & 19.22 \% & 0.2 s / 4 cores & \\
RFCN\_RFB & & 20.44 \% & 25.95 \% & 18.78 \% & 0.2 s / 4 cores & \\
cascade\_gw & & 19.56 \% & 26.76 \% & 17.09 \% & 0.2 s / 4 cores & \\
DPM-C8B1 & st & 19.25 \% & 27.16 \% & 17.95 \% & 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.\\
Cmerge & & 19.19 \% & 26.37 \% & 18.56 \% & 0.2 s / 4 cores & \\
MTDP & & 18.95 \% & 23.33 \% & 17.24 \% & 0.15 s / GPU & \\
centernet & & 18.36 \% & 23.40 \% & 16.35 \% & 0.01 s / GPU & \\
Retinanet100 & & 15.16 \% & 18.64 \% & 12.49 \% & 0.2 s / 4 cores & \\
softyolo & & 12.14 \% & 16.84 \% & 10.51 \% & 0.16 s / 4 cores & \\
100Frcnn & & 11.79 \% & 17.33 \% & 10.99 \% & 2 s / 4 cores & \\
rpn & & 11.30 \% & 14.62 \% & 8.94 \% & 0.01 s / 1 core & \\
Lidar\_ROI+Yolo(UJS) & & 9.31 \% & 13.88 \% & 9.12 \% & 0.1 s / 1 core & \\
KD53-20 & & 6.15 \% & 7.81 \% & 6.35 \% & 0.19 s / 4 cores & \\
RT3DStereo & st & 5.26 \% & 6.60 \% & 3.68 \% & 0.08 s / GPU & \\
softretina & & 0.20 \% & 0.14 \% & 0.10 \% & 0.16 s / 4 cores & \\
JSyolo & & 0.15 \% & 0.15 \% & 0.15 \% & 0.16 s / 4 cores &
\end{tabular}