\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
ADLAB & & 91.66 \% & 95.56 \% & 86.92 \% & 0.08 s / 1 core & \\
SPANet & & 91.59 \% & 95.59 \% & 86.53 \% & 0.06 s / 1 core & \\
PVGNet & & 91.26 \% & 94.36 \% & 86.63 \% & 0.05 s / 1 core & \\
SA-SSD & & 91.03 \% & 95.03 \% & 85.96 \% & 0.04 s / 1 core & C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.\\
MMLab PV-RCNN & la & 90.65 \% & 94.98 \% & 86.14 \% & 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.\\
CN & & 90.50 \% & 94.51 \% & 85.86 \% & 0.04 s / GPU & \\
Deformable PV-RCNN & la & 90.13 \% & 92.42 \% & 85.93 \% & 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.\\
Associate-3Ddet\_v2 & & 90.00 \% & 95.55 \% & 84.72 \% & 0.04 s / 1 core & \\
CIA-SSD & la & 89.84 \% & 93.74 \% & 82.39 \% & 0.03 s / 1 core & \\
AIMC-RUC & & 89.80 \% & 93.64 \% & 84.64 \% & 0.08 s / 1 core & \\
CLOCs\_PVCas & & 89.80 \% & 93.05 \% & 86.57 \% & 0.1 s / 1 core & S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.\\
CIA-SSD v2 & la & 89.80 \% & 93.49 \% & 84.39 \% & 0.03 s / 1 core & \\
deprecated & & 89.77 \% & 93.68 \% & 82.31 \% & deprecated / & \\
BorderAtt & & 89.76 \% & 94.67 \% & 86.73 \% & 0.08 s / 1 core & \\
CBi-GNN & & 89.74 \% & 95.92 \% & 84.54 \% & 0.03 s / 1 core & \\
OAP & & 89.72 \% & 93.13 \% & 82.25 \% & 0.06 s / 1 core & \\
D3D & & 89.72 \% & 93.37 \% & 84.72 \% & 0.02 s / 1 core & \\
3D-CVF at SPA & la & 89.56 \% & 93.52 \% & 82.45 \% & 0.06 s / 1 core & J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.\\
scssd-normal(0.3) & & 89.54 \% & 95.26 \% & 82.31 \% & 0.05 s / GPU & P. An, J. Liang, J. Ma, K. Yu and B. Fang: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.\\
Cas-SSD & & 89.47 \% & 93.31 \% & 84.35 \% & 0.1 s / 1 core & \\
FCY & la & 89.46 \% & 95.27 \% & 84.34 \% & 0.02 s / GPU & \\
PointRes & la & 89.42 \% & 93.17 \% & 84.25 \% & 0.013 s / 1 core & \\
HUAWEI Octopus & & 89.39 \% & 92.58 \% & 86.55 \% & 0.1 s / 1 core & \\
scssd-normal(0.4) & & 89.38 \% & 94.91 \% & 84.29 \% & 0.05 s / 1 core & P. An, J. Liang, J. Ma, K. Yu and B. Fang: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.\\
CJJ & & 89.20 \% & 92.90 \% & 84.30 \% & 0.04 s / 1 core & \\
STD & & 89.19 \% & 94.74 \% & 86.42 \% & 0.08 s / GPU & Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.\\
Point-GNN & la & 89.17 \% & 93.11 \% & 83.90 \% & 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 & & 89.17 \% & 93.11 \% & 83.90 \% & 0.1 s / 1 core & \\
Noah CV Lab - SSL & & 89.16 \% & 90.18 \% & 81.73 \% & 0.1 s / GPU & \\
RoIFusion & & 89.06 \% & 92.90 \% & 83.96 \% & 0.22 s / 1 core & \\
3DSSD & & 89.02 \% & 92.66 \% & 85.86 \% & 0.04 s / GPU & Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.\\
CLOCs\_PointCas & & 88.99 \% & 92.60 \% & 81.74 \% & 0.1 s / GPU & \\
Discrete-PointDet & & 88.95 \% & 94.56 \% & 83.56 \% & 0.02 s / 1 core & \\
NLK-ALL & & 88.89 \% & 92.25 \% & 84.13 \% & 0.04 s / 1 core & \\
Voxel R-CNN & & 88.83 \% & 94.85 \% & 86.13 \% & 0.04 s / GPU & \\
HVNet & & 88.82 \% & 92.83 \% & 83.38 \% & 0.03 s / GPU & M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.\\
PointCSE & & 88.81 \% & 92.58 \% & 83.64 \% & 0.02 s / 1 core & \\
RangeRCNN-LV & & 88.81 \% & 92.41 \% & 85.96 \% & 0.1 s / 1 core & \\
F-3DNet & & 88.76 \% & 92.68 \% & 83.63 \% & 0.5 s / GPU & \\
cvMax & & 88.64 \% & 92.12 \% & 83.72 \% & 0.04 s / GPU & \\
deprecated & & 88.59 \% & 92.18 \% & 83.60 \% & 0.04 s / GPU & \\
KNN-GCNN & & 88.57 \% & 91.73 \% & 83.32 \% & 0.4 s / 1 core & \\
PVF-NET & & 88.57 \% & 92.20 \% & 83.45 \% & 0.1 s / 1 core & \\
IC-SECOND & & 88.57 \% & 91.94 \% & 85.43 \% & 0.06 s / 1 core & \\
MuRF & & 88.56 \% & 91.57 \% & 83.46 \% & 0.05 s / GPU & \\
BLPNet\_V2 & & 88.55 \% & 92.24 \% & 83.44 \% & 0.04 s / 1 core & \\
Chovy & & 88.54 \% & 92.34 \% & 83.68 \% & 0.04 s / GPU & \\
CenterNet3DV1.5 & & 88.51 \% & 91.78 \% & 85.50 \% & 0.04 s / 1 core & G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.\\
nonet & & 88.49 \% & 91.97 \% & 85.33 \% & 0.08 s / 1 core & \\
EPNet & & 88.47 \% & 94.22 \% & 83.69 \% & 0.1 s / 1 core & T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.\\
CenterNet3D & & 88.46 \% & 91.80 \% & 83.62 \% & 0.04 s / GPU & G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.\\
deprecated & & 88.44 \% & 92.14 \% & 85.11 \% & 0.06 s / 1 core & \\
PC-RGNN & & 88.43 \% & 92.08 \% & 85.81 \% & 0.1 s / GPU & \\
RangeRCNN & la & 88.40 \% & 92.15 \% & 85.74 \% & 0.06 s / GPU & Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.\\
Patches & la & 88.39 \% & 92.72 \% & 83.19 \% & 0.15 s / GPU & J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.\\
3D IoU-Net & & 88.38 \% & 94.76 \% & 81.93 \% & 0.1 s / 1 core & J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.\\
OneCoLab SicNet V2 & & 88.23 \% & 91.94 \% & 85.50 \% & 0.08 s / 1 core & \\
CLOCs\_SecCas & & 88.23 \% & 91.16 \% & 82.63 \% & 0.1 s / 1 core & S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.\\
NLK-3D & & 88.22 \% & 91.54 \% & 83.33 \% & 0.04 s / 1 core & \\
CVRS\_PF & & 88.22 \% & 91.81 \% & 84.91 \% & 0.09 s / 1 core & \\
UberATG-MMF & la & 88.21 \% & 93.67 \% & 81.99 \% & 0.08 s / GPU & M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.\\
IC-PVRCNN & & 88.20 \% & 92.35 \% & 85.64 \% & 0.08 s / 1 core & \\
Patches - EMP & la & 88.17 \% & 94.49 \% & 84.75 \% & 0.5 s / GPU & J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.\\
SVGA-Net & la & 88.17 \% & 92.01 \% & 85.43 \% & 0.08 s / GPU & \\
Baseline of CA RCNN & & 88.13 \% & 91.91 \% & 85.40 \% & 0.1 s / GPU & \\
CVIS-DF3D & & 88.13 \% & 91.91 \% & 85.40 \% & 0.05 s / 1 core & \\
PointPainting & la & 88.11 \% & 92.45 \% & 83.36 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
SERCNN & la & 88.10 \% & 94.11 \% & 83.43 \% & 0.1 s / 1 core & D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.\\
Associate-3Ddet & & 88.09 \% & 91.40 \% & 82.96 \% & 0.05 s / 1 core & L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.\\
HotSpotNet & & 88.09 \% & 94.06 \% & 83.24 \% & 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.\\
DEFT & & 88.06 \% & 92.06 \% & 83.22 \% & 1 s / GPU & \\
CVIS-DF3D\_v2 & & 88.06 \% & 91.85 \% & 85.37 \% & 0.05 s / 1 core & \\
OneCoLab SicNet & & 88.06 \% & 92.17 \% & 83.60 \% & 0.08 s / 1 core & \\
deprecated & & 88.05 \% & 91.96 \% & 83.21 \% & 0.05 s / GPU & \\
deprecated & & 88.04 \% & 91.97 \% & 83.22 \% & - / & \\
SRDL & st la & 88.03 \% & 91.88 \% & 85.18 \% & 0.15 s / GPU & \\
Dccnet & & 88.01 \% & 92.09 \% & 82.45 \% & 0.05 s / 1 core & \\
UberATG-HDNET & la & 87.98 \% & 93.13 \% & 81.23 \% & 0.05 s / GPU & B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.\\
LZY\_RCNN & & 87.94 \% & 91.74 \% & 83.64 \% & 0.08 s / 1 core & \\
SPA & & 87.90 \% & 91.70 \% & 83.18 \% & 0.1 s / 1 core & \\
tbd & & 87.88 \% & 91.36 \% & 84.75 \% & 0.08 s / 1 core & \\
Fast Point R-CNN & la & 87.84 \% & 90.87 \% & 80.52 \% & 0.06 s / GPU & Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.\\
MMLab-PartA^2 & la & 87.79 \% & 91.70 \% & 84.61 \% & 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.\\
HRI-MSP-L & la & 87.78 \% & 91.74 \% & 85.14 \% & 0.07 s / 1 core & \\
MGACNet & & 87.68 \% & 90.93 \% & 84.60 \% & 0.05 s / 1 core & \\
deprecated & & 87.63 \% & 93.66 \% & 80.35 \% & 0.06 s / GPU & \\
VAL & & 87.63 \% & 93.57 \% & 79.89 \% & 0.03 s / 1 core & \\
MODet & la & 87.56 \% & 90.80 \% & 82.69 \% & 0.05 s / & Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.\\
AB3DMOT & la on & 87.53 \% & 91.99 \% & 81.03 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
PointRGCN & & 87.49 \% & 91.63 \% & 80.73 \% & 0.26 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
deprecated & & 87.46 \% & 92.54 \% & 77.39 \% & 0.05 s / 1 core & \\
IE-PointRCNN & & 87.43 \% & 92.11 \% & 81.10 \% & 0.1 s / 1 core & \\
PC-CNN-V2 & la & 87.40 \% & 91.19 \% & 79.35 \% & 0.5 s / GPU & X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.\\
MMLab-PointRCNN & la & 87.39 \% & 92.13 \% & 82.72 \% & 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.\\
MAFF-Net(DAF-Pillar) & & 87.34 \% & 90.79 \% & 77.66 \% & 0.04 s / 1 core & Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.\\
VAR & & 87.31 \% & 90.68 \% & 82.67 \% & 0.1 s / 1 core & \\
PiP & & 87.25 \% & 90.87 \% & 83.38 \% & 0.033 s / 1 core & \\
HRI-VoxelFPN & & 87.21 \% & 92.75 \% & 79.82 \% & 0.02 s / GPU & H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.\\
CentrNet-v1 & la & 87.19 \% & 90.72 \% & 83.34 \% & 0.03 s / GPU & \\
MDA & & 87.13 \% & 90.67 \% & 82.80 \% & 0.03 s / 1 core & \\
epBRM & la & 87.13 \% & 90.70 \% & 81.92 \% & 0.1 s / GPU & K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.\\
Pointpillar\_TV & & 87.08 \% & 90.50 \% & 81.98 \% & 0.05 s / 1 core & \\
EPENet & & 87.00 \% & 90.98 \% & 82.99 \% & 0.04 s / 1 core & \\
SARPNET & & 86.92 \% & 92.21 \% & 81.68 \% & 0.05 s / 1 core & Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.\\
ARPNET & & 86.81 \% & 90.06 \% & 79.41 \% & 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.\\
AF\_V1 & & 86.80 \% & 91.57 \% & 82.65 \% & 0.1 s / 1 core & \\
PointPiallars\_SECA & & 86.79 \% & 90.15 \% & 82.87 \% & 0.06 s / 1 core & \\
C-GCN & & 86.78 \% & 91.11 \% & 80.09 \% & 0.147 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
FLID & & 86.77 \% & 91.58 \% & 81.14 \% & 0.04 s / GPU & \\
CentrNet-FG & & 86.72 \% & 90.30 \% & 82.99 \% & 0.03 s / 1 core & \\
CU-PointRCNN & & 86.69 \% & 92.65 \% & 82.66 \% & 0.1 s / GPU & \\
tt & & 86.68 \% & 90.57 \% & 81.98 \% & 0.08 s / 1 core & \\
PointPillars & la & 86.56 \% & 90.07 \% & 82.81 \% & 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.\\
TANet & & 86.54 \% & 91.58 \% & 81.19 \% & 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.\\
MVX-Net++ & & 86.53 \% & 91.86 \% & 81.41 \% & 0.15 s / 1 core & \\
SCNet & la & 86.48 \% & 90.07 \% & 81.30 \% & 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.\\
RUC & & 86.46 \% & 90.06 \% & 82.20 \% & 0.12 s / 1 core & \\
Simple3D Net & & 86.46 \% & 89.82 \% & 82.60 \% & 0.02 s / 1 core & \\
DDB & la & 86.45 \% & 89.91 \% & 82.21 \% & 0.05 s / GPU & \\
PPFNet & & 86.44 \% & 92.35 \% & 81.48 \% & 0.1 s / 1 core & \\
autonet & & 86.42 \% & 89.81 \% & 81.25 \% & 0.12 s / 1 core & \\
HR-SECOND & & 86.40 \% & 91.68 \% & 81.40 \% & 0.11 s / 1 core & \\
SegVoxelNet & & 86.37 \% & 91.62 \% & 83.04 \% & 0.04 s / 1 core & H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.\\
VOXEL\_FPN\_HR & & 86.36 \% & 90.28 \% & 81.20 \% & 0.12 s / 8 cores & ERROR: Wrong syntax in BIBTEX file.\\
CP & la & 86.30 \% & 92.14 \% & 82.97 \% & 0.1 s / 1 core & \\
Bit & & 86.27 \% & 89.74 \% & 81.19 \% & 0.11 s / 1 core & \\
3D IoU Loss & la & 86.22 \% & 91.36 \% & 81.20 \% & 0.08 s / GPU & D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.\\
IGRP & & 86.21 \% & 92.04 \% & 81.30 \% & 0.18 s / 1 core & \\
MP & & 86.16 \% & 90.24 \% & 78.86 \% & 0.2 s / 1 core & \\
DPointNet & & 86.12 \% & 88.55 \% & 79.82 \% & 0.09 s / 1 core & \\
R-GCN & & 86.05 \% & 91.91 \% & 81.05 \% & 0.16 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
RethinkDet3D & & 86.05 \% & 91.32 \% & 81.13 \% & 0.15 s / 1 core & \\
UberATG-PIXOR++ & la & 86.01 \% & 93.28 \% & 80.11 \% & 0.035 s / GPU & B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.\\
TBD & & 86.00 \% & 89.79 \% & 83.37 \% & 0.05 s / GPU & \\
PPBA & & 85.85 \% & 91.30 \% & 80.92 \% & NA s / GPU & \\
TBU & & 85.85 \% & 91.30 \% & 80.92 \% & NA s / GPU & \\
F-ConvNet & la & 85.84 \% & 91.51 \% & 76.11 \% & 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.\\
RUC & & 85.84 \% & 88.54 \% & 81.15 \% & 0.12 s / 1 core & \\
BVVF & & 85.83 \% & 91.20 \% & 80.76 \% & 0.1 s / 1 core & \\
PI-RCNN & & 85.81 \% & 91.44 \% & 81.00 \% & 0.1 s / 1 core & L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.\\
SFB-SECOND & & 85.63 \% & 91.38 \% & 78.60 \% & 0.1 s / 1 core & \\
PBASN & & 85.62 \% & 90.95 \% & 80.49 \% & NA s / GPU & \\
UberATG-ContFuse & la & 85.35 \% & 94.07 \% & 75.88 \% & 0.06 s / GPU & M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.\\
3DBN\_2 & & 85.30 \% & 91.37 \% & 82.57 \% & 0.12 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
PFF3D & la & 85.08 \% & 89.61 \% & 80.42 \% & 0.05 s / GPU & \\
AVOD & la & 84.95 \% & 89.75 \% & 78.32 \% & 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.\\
WS3D & la & 84.93 \% & 90.96 \% & 77.96 \% & 0.1 s / GPU & Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.\\
baseline & & 84.88 \% & 89.25 \% & 80.18 \% & 0.12 s / 1 core & \\
AVOD-FPN & la & 84.82 \% & 90.99 \% & 79.62 \% & 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.\\
Prune & & 84.81 \% & 90.48 \% & 77.40 \% & 0.11 s / 1 core & \\
autoRUC & & 84.80 \% & 90.44 \% & 77.43 \% & 0.12 s / 1 core & \\
F-PointNet & la & 84.67 \% & 91.17 \% & 74.77 \% & 0.17 s / GPU & C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.\\
RUC & & 84.40 \% & 89.11 \% & 79.33 \% & 0.12 s / 1 core & \\
3DBN & la & 83.94 \% & 89.66 \% & 76.50 \% & 0.13s / & X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.\\
3DNN & & 83.68 \% & 88.06 \% & 77.00 \% & 0.09 s / GPU & \\
MLOD & la & 82.68 \% & 90.25 \% & 77.97 \% & 0.12 s / GPU & J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.\\
DAMNET & & 82.14 \% & 87.90 \% & 75.52 \% & 1 s / 1 core & \\
voxelrcnn & & 81.41 \% & 88.21 \% & 75.26 \% & 15 s / 1 core & \\
RuiRUC & & 80.20 \% & 86.90 \% & 67.77 \% & 0.12 s / 1 core & \\
UberATG-PIXOR & la & 80.01 \% & 83.97 \% & 74.31 \% & 0.035 s / & B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.\\
NLK & & 79.15 \% & 82.59 \% & 72.65 \% & 0.02 s / 1 core & \\
MV3D (LIDAR) & la & 78.98 \% & 86.49 \% & 72.23 \% & 0.24 s / GPU & X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.\\
MV3D & la & 78.93 \% & 86.62 \% & 69.80 \% & 0.36 s / GPU & X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.\\
seivl & & 77.43 \% & 85.43 \% & 75.51 \% & 0.1 s / 1 core & \\
RCD & & 75.83 \% & 82.26 \% & 69.61 \% & 0.1 s / GPU & \\
ANM & & 75.40 \% & 84.78 \% & 61.28 \% & 0.12 s / 1 core & \\
LaserNet & & 74.52 \% & 79.19 \% & 68.45 \% & 12 ms / GPU & G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
PL++ (SDN+GDC) & st la & 73.80 \% & 84.61 \% & 65.59 \% & 0.6 s / GPU & Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.\\
A3DODWTDA & la & 73.26 \% & 79.58 \% & 62.77 \% & 0.08 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
Complexer-YOLO & la & 68.96 \% & 77.24 \% & 64.95 \% & 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.\\
TopNet-Retina & la & 68.16 \% & 80.16 \% & 63.43 \% & 52ms / & S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
tiny-stereo-volume & st & 66.55 \% & 85.74 \% & 57.55 \% & 0.3 s / GPU & \\
CG-Stereo & st & 66.44 \% & 85.29 \% & 58.95 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
CDN & st & 66.24 \% & 83.32 \% & 57.65 \% & 0.6 s / GPU & \\
SF & st la & 65.74 \% & 74.20 \% & 58.35 \% & 0.5 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
DSGN & st & 65.05 \% & 82.90 \% & 56.60 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
TopNet-DecayRate & la & 64.60 \% & 79.74 \% & 58.04 \% & 92 ms / & S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
BirdNet+ & la & 63.33 \% & 84.80 \% & 61.23 \% & 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.\\
3D FCN & la & 61.67 \% & 70.62 \% & 55.61 \% & >5 s / 1 core & B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.\\
CDN-PL++ & st & 61.04 \% & 81.27 \% & 52.84 \% & 0.4 s / GPU & \\
tiny-stereo-volume-v & & 60.57 \% & 80.35 \% & 51.70 \% & 0.4 s / 1 core & \\
BirdNet & la & 59.83 \% & 84.17 \% & 57.35 \% & 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.\\
TopNet-UncEst & la & 59.67 \% & 72.05 \% & 51.67 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
Pseudo-LiDAR E2E & st & 58.84 \% & 79.58 \% & 52.06 \% & 0.4 s / GPU & \\
PB3D & st & 58.04 \% & 79.75 \% & 49.78 \% & 0.42 s / 1 core & \\
Pseudo-LiDAR++ & st & 58.01 \% & 78.31 \% & 51.25 \% & 0.4 s / GPU & Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.\\
ZoomNet & st & 54.91 \% & 72.94 \% & 44.14 \% & 0.3 s / 1 core & L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.\\
VoxelJones & & 53.96 \% & 66.21 \% & 47.66 \% & .18 s / 1 core & M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.\\
TopNet-HighRes & la & 53.05 \% & 67.84 \% & 46.99 \% & 101ms / & S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.\\
Disp R-CNN & st & 52.37 \% & 73.87 \% & 43.67 \% & 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 (velo) & st & 52.37 \% & 74.12 \% & 43.79 \% & 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.\\
RTS3D & & 51.79 \% & 72.17 \% & 43.19 \% & 0.03 s / GPU & \\
OC Stereo & st & 51.47 \% & 68.89 \% & 42.97 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
Stereo3D & st & 50.28 \% & 76.10 \% & 36.86 \% & 0.1 s / & \\
stereo\_sa & st & 49.61 \% & 71.47 \% & 42.71 \% & 0.3 s / GPU & \\
RT3D-GMP & st & 49.57 \% & 61.28 \% & 38.70 \% & 0.06 s / GPU & \\
RT3DStereo & st & 46.82 \% & 58.81 \% & 38.38 \% & 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.\\
Pseudo-Lidar & st & 45.00 \% & 67.30 \% & 38.40 \% & 0.4 s / GPU & Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
RT3D & la & 44.00 \% & 56.44 \% & 42.34 \% & 0.09 s / GPU & Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.\\
m-prcnn & st & 42.81 \% & 67.82 \% & 33.63 \% & 0.43 s / 1 core & \\
IDA-3D & st & 42.47 \% & 61.87 \% & 34.59 \% & 0.08 s / 1 core & \\
Stereo R-CNN & st & 41.31 \% & 61.92 \% & 33.42 \% & 0.3 s / GPU & P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.\\
ASOD & & 33.63 \% & 54.61 \% & 26.76 \% & 0.28 s / GPU & \\
StereoFENet & st & 32.96 \% & 49.29 \% & 25.90 \% & 0.15 s / 1 core & W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.\\
deprecated & & 30.56 \% & 34.56 \% & 25.69 \% & / 1 core & \\
S3D & & 30.44 \% & 35.25 \% & 25.68 \% & 0.1 s / 1 core & \\
LNET & & 29.68 \% & 34.30 \% & 25.11 \% & 0.05 s / 1 core & \\
Det3D & & 20.80 \% & 35.46 \% & 16.00 \% & 0.5 s / 1 core & \\
ITS-MDPL & & 19.52 \% & 32.80 \% & 16.96 \% & 0.16 s / GPU & \\
PSMD & & 19.33 \% & 28.63 \% & 15.31 \% & 0.1 s / GPU & \\
MTMono3d & & 18.54 \% & 27.00 \% & 15.71 \% & 0.05 s / 1 core & \\
IAFA & & 17.88 \% & 25.88 \% & 15.35 \% & 0.04 s / 1 core & \\
RefinedMPL & & 17.60 \% & 28.08 \% & 13.95 \% & 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.\\
Kinematic3D & & 17.52 \% & 26.69 \% & 13.10 \% & 0.12 s / 1 core & G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .\\
AM3D & & 17.32 \% & 25.03 \% & 14.91 \% & 0.4 s / GPU & X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.\\
YoloMono3D & & 17.15 \% & 26.79 \% & 12.56 \% & 0.05 s / GPU & \\
OCM3D & & 17.13 \% & 27.87 \% & 13.53 \% & 0.5 s / 1 core & \\
IMA & & 17.08 \% & 23.93 \% & 14.75 \% & 0.1 s / 1 core & \\
MCA & & 17.07 \% & 25.93 \% & 14.80 \% & 0.04 s / 1 core & \\
DP3D & & 16.96 \% & 26.51 \% & 12.82 \% & 0.07 s / GPU & \\
PatchNet & & 16.86 \% & 22.97 \% & 14.97 \% & 0.4 s / 1 core & X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.\\
UM3D\_TUM & & 16.69 \% & 23.63 \% & 14.17 \% & 0.05 s / 1 core & \\
PG-MonoNet & & 16.31 \% & 23.31 \% & 13.03 \% & 0.19 s / GPU & \\
SSL-RTM3D & & 16.20 \% & 23.44 \% & 14.47 \% & 0.03 s / 1 core & \\
CDI3D & & 16.06 \% & 22.06 \% & 13.43 \% & 0.03 s / GPU & \\
D4LCN & & 16.02 \% & 22.51 \% & 12.55 \% & 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.\\
MP-Mono & & 16.01 \% & 23.45 \% & 12.07 \% & 0.16 s / GPU & \\
NL\_M3D & & 15.93 \% & 24.15 \% & 12.11 \% & 0.2 s / 1 core & \\
DA-3Ddet & & 15.90 \% & 23.35 \% & 12.11 \% & 0.4 s / GPU & \\
DP3D & & 15.44 \% & 23.98 \% & 12.24 \% & 0.05 s / GPU & \\
MA & & 15.43 \% & 22.01 \% & 14.01 \% & 0.1 s / 1 core & \\
MonoPair & & 14.83 \% & 19.28 \% & 12.89 \% & 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.\\
Decoupled-3D & & 14.82 \% & 23.16 \% & 11.25 \% & 0.08 s / GPU & Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.\\
Decoupled-3D v2 & & 14.66 \% & 24.62 \% & 11.46 \% & 0.08 s / GPU & \\
SMOKE & & 14.49 \% & 20.83 \% & 12.75 \% & 0.03 s / GPU & Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.\\
RTM3D & & 14.20 \% & 19.17 \% & 11.99 \% & 0.05 s / GPU & P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.\\
Mono3CN & & 14.17 \% & 19.82 \% & 12.30 \% & 0.1 s / 1 core & \\
LCD3D & & 13.99 \% & 21.97 \% & 11.43 \% & 0.03 s / GPU & \\
Center3D & & 13.98 \% & 18.89 \% & 12.44 \% & 0.05 s / GPU & \\
Mono3D\_PLiDAR & & 13.92 \% & 21.27 \% & 11.25 \% & 0.1 s / & X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.\\
SS3D\_HW & & 13.70 \% & 20.28 \% & 9.86 \% & 0.4 s / GPU & \\
M3D-RPN & & 13.67 \% & 21.02 \% & 10.23 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
SSL-RTM3D Res18 & & 13.37 \% & 19.71 \% & 11.10 \% & 0.02 s / GPU & \\
CSoR & la & 13.07 \% & 18.67 \% & 10.34 \% & 3.5 s / 4 cores & L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.\\
RAR-Net & & 13.01 \% & 20.63 \% & 10.19 \% & 0.5 s / 1 core & \\
MonoPSR & & 12.58 \% & 18.33 \% & 9.91 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
SS3D & & 11.52 \% & 16.33 \% & 9.93 \% & 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.\\
MonoGRNet & & 11.17 \% & 18.19 \% & 8.73 \% & 0.04s / & Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.\\
MonoFENet & & 11.03 \% & 17.03 \% & 9.05 \% & 0.15 s / 1 core & W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.\\
anonymous & & 10.96 \% & 20.42 \% & 9.23 \% & 1 s / 1 core & \\
OACV & & 10.13 \% & 16.24 \% & 8.28 \% & 0.23 s / GPU & \\
anonymous & & 10.06 \% & 18.80 \% & 8.56 \% & 1 s / 1 core & \\
A3DODWTDA (image) & & 8.66 \% & 10.37 \% & 7.06 \% & 0.8 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
TLNet (Stereo) & st & 7.69 \% & 13.71 \% & 6.73 \% & 0.1 s / 1 core & Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
Shift R-CNN (mono) & & 6.82 \% & 11.84 \% & 5.27 \% & 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.\\
AACL & & 6.75 \% & 8.55 \% & 5.68 \% & 0.1 s / 1 core & \\
GS3D & & 6.08 \% & 8.41 \% & 4.94 \% & 2 s / 1 core & B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
MVRA + I-FRCNN+ & & 5.84 \% & 9.05 \% & 4.50 \% & 0.18 s / GPU & H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.\\
ROI-10D & & 4.91 \% & 9.78 \% & 3.74 \% & 0.2 s / GPU & F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.\\
3D-GCK & & 4.57 \% & 5.79 \% & 3.64 \% & 24 ms / & N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.\\
FQNet & & 3.23 \% & 5.40 \% & 2.46 \% & 0.5 s / 1 core & L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.\\
UDI-mono3D & & 2.79 \% & 3.38 \% & 2.37 \% & 0.05 s / 1 core & \\
3D-SSMFCNN & & 2.63 \% & 3.20 \% & 2.40 \% & 0.1 s / GPU & L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.\\
VeloFCN & la & 0.14 \% & 0.02 \% & 0.21 \% & 1 s / GPU & B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .\\
ANM & & 0.00 \% & 0.00 \% & 0.00 \% & 0.12 s / 1 core & \\
PVNet & & 0.00 \% & 0.00 \% & 0.00 \% & 0,1 s / 1 core & \\
multi-task CNN & & 0.00 \% & 0.00 \% & 0.00 \% & 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.\\
mBoW & la & 0.00 \% & 0.00 \% & 0.00 \% & 10 s / 1 core & J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
\end{tabular}