\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
ZEEWAIN-AI & & 96.14 \% & 95.22 \% & 88.94 \% & 0.3 s / GPU & \\
CLOCs\_PVCas & & 95.96 \% & 96.76 \% & 91.08 \% & 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.\\
EA-M-RCNN(BorderAtt) & & 95.88 \% & 96.68 \% & 90.89 \% & 0.08 s / 1 core & \\
PVGNet & & 95.80 \% & 96.87 \% & 93.05 \% & 0.05 s / 1 core & \\
ADLAB & & 95.69 \% & 96.69 \% & 90.81 \% & 0.05 s / 1 core & \\
SE-SSD & la & 95.60 \% & 96.69 \% & 90.53 \% & 0.03 s / 1 core & \\
HUAWEI Octopus & & 95.50 \% & 96.30 \% & 92.81 \% & 0.1 s / 1 core & \\
SPANet & & 95.46 \% & 96.54 \% & 90.47 \% & 0.06 s / 1 core & \\
PLNL-3DSSD & la & 95.38 \% & 96.37 \% & 90.31 \% & 0.08 s / GPU & \\
PC-CNN-V2 & la & 95.20 \% & 96.06 \% & 89.37 \% & 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.\\
F-PointNet & la & 95.17 \% & 95.85 \% & 85.42 \% & 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.\\
SA-SSD & & 95.16 \% & 97.92 \% & 90.15 \% & 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.\\
Voxel R-CNN & & 95.11 \% & 96.49 \% & 92.45 \% & 0.04 s / GPU & \\
TBD & & 95.10 \% & 96.48 \% & 92.62 \% & 0.1 s / 1 core & \\
3DSSD & & 95.10 \% & 97.69 \% & 92.18 \% & 0.04 s / GPU & Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.\\
3DIoU++ & & 95.06 \% & 96.37 \% & 90.52 \% & 0.1 s / 1 core & \\
PV-RCNN-v2 & & 95.05 \% & 96.08 \% & 92.42 \% & 0.06 s / 1 core & \\
MVRA + I-FRCNN+ & & 94.98 \% & 95.87 \% & 82.52 \% & 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.\\
MSG-PGNN & & 94.92 \% & 95.98 \% & 92.42 \% & 0.08 s / 1 core & \\
Fast VP-RCNN & & 94.91 \% & 96.01 \% & 92.22 \% & 0.05 s / 1 core & \\
SIENet & & 94.90 \% & 95.98 \% & 92.39 \% & 0.08 s / 1 core & \\
DomainAdp+PVRCNN & la & 94.85 \% & 95.99 \% & 92.27 \% & 0.09 s / GPU & \\
XView & & 94.77 \% & 95.89 \% & 92.23 \% & 0.1 s / 1 core & \\
MMLab PV-RCNN & la & 94.70 \% & 98.17 \% & 92.04 \% & 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.\\
3DIoU\_v2 & & 94.70 \% & 96.15 \% & 92.37 \% & 0.2 s / 1 core & \\
CVRS VIC-Net & & 94.69 \% & 95.79 \% & 91.89 \% & 0.06 s / 1 core & \\
PC-RGNN & & 94.68 \% & 95.80 \% & 92.20 \% & 0.1 s / GPU & \\
D3D & & 94.66 \% & 95.43 \% & 89.72 \% & 0.02 s / 1 core & \\
DSA-PV-RCNN & la & 94.64 \% & 95.86 \% & 92.10 \% & 0.08 s / 1 core & \\
FSA-PVRCNN & la & 94.63 \% & 95.81 \% & 92.06 \% & 0.06 s / 1 core & \\
nonet & & 94.62 \% & 95.86 \% & 91.86 \% & 0.08 s / 1 core & \\
RangeIoUDet & la & 94.61 \% & 95.74 \% & 91.98 \% & 0.02 s / 1 core & \\
MSL3D & & 94.60 \% & 95.76 \% & 92.16 \% & 0.03 s / GPU & \\
Multi-Sensor3D & & 94.60 \% & 95.76 \% & 92.16 \% & 0.03 s / GPU & \\
CN & & 94.60 \% & 97.86 \% & 89.81 \% & 0.04 s / GPU & \\
HyBrid Feature Det & & 94.59 \% & 95.76 \% & 92.18 \% & 0.08 s / 1 core & \\
ReFineNet & & 94.59 \% & 95.75 \% & 92.12 \% & 0.08 s / 1 core & \\
MGACNet & & 94.57 \% & 95.35 \% & 91.77 \% & 0.05 s / 1 core & \\
RangeRCNN-LV & & 94.51 \% & 95.93 \% & 92.07 \% & 0.1 s / 1 core & \\
TuSimple & & 94.47 \% & 95.12 \% & 86.45 \% & 1.6 s / GPU & F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.\\
PF-GAP & & 94.47 \% & 96.13 \% & 90.15 \% & 0.02 s / 1 core & \\
EPNet & & 94.44 \% & 96.15 \% & 89.99 \% & 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.\\
GNN-RCNN & & 94.44 \% & 95.85 \% & 91.96 \% & 0.1 s / 1 core & \\
SERCNN & la & 94.42 \% & 96.33 \% & 89.96 \% & 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.\\
CVRS VIC-RCNN & & 94.38 \% & 95.89 \% & 91.90 \% & 0.08 s / 1 core & \\
CVRS\_PF & & 94.37 \% & 95.56 \% & 91.43 \% & 0.09 s / 1 core & \\
CVIS-DF3D\_v2 & & 94.33 \% & 95.70 \% & 91.72 \% & 0.05 s / 1 core & \\
SVGA-Net & la & 94.28 \% & 95.69 \% & 91.73 \% & 0.08 s / GPU & \\
UberATG-MMF & la & 94.25 \% & 97.41 \% & 89.87 \% & 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.\\
SRDL & st la & 94.24 \% & 95.86 \% & 91.80 \% & 0.15 s / GPU & \\
Baseline of CA RCNN & & 94.23 \% & 95.84 \% & 91.80 \% & 0.1 s / GPU & \\
CVIS-DF3D & & 94.23 \% & 95.84 \% & 91.80 \% & 0.05 s / 1 core & \\
tbd & & 94.21 \% & 95.68 \% & 91.49 \% & 0.08 s / 1 core & \\
TBD & & 94.21 \% & 95.51 \% & 91.69 \% & 0.06 s / 1 core & \\
GAP-soft-filter & & 94.20 \% & 95.81 \% & 91.53 \% & 0.1 s / 1 core & \\
HR-faster-rcnn & & 94.14 \% & 95.41 \% & 86.88 \% & 0.1 s / 1 core & \\
RangeRCNN & la & 94.03 \% & 95.48 \% & 91.74 \% & 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.\\
Faraway-Frustum & la & 93.99 \% & 95.81 \% & 91.72 \% & 0.1 s / GPU & H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.\\
SIF & & 93.95 \% & 95.51 \% & 91.57 \% & 0.1 s / 1 core & \\
OAP & & 93.93 \% & 96.85 \% & 86.37 \% & 0.06 s / 1 core & \\
HRI-MSP-L & la & 93.92 \% & 95.51 \% & 91.42 \% & 0.07 s / 1 core & \\
AF\_V1 & & 93.87 \% & 94.45 \% & 86.37 \% & 0.1 s / 1 core & \\
Associate-3Ddet\_v2 & & 93.77 \% & 96.83 \% & 88.57 \% & 0.04 s / 1 core & \\
Patches - EMP & la & 93.75 \% & 97.91 \% & 90.56 \% & 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.\\
CIA-SSD & la & 93.72 \% & 96.87 \% & 86.20 \% & 0.03 s / 1 core & W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.\\
VAL & & 93.71 \% & 96.92 \% & 83.76 \% & 0.03 s / 1 core & \\
XView-PartA^2 & & 93.71 \% & 95.42 \% & 91.26 \% & 0.1 s / 1 core & \\
HIKVISION-ADLab-HZ & & 93.69 \% & 96.70 \% & 88.66 \% & 0.1 s / 1 core & \\
deprecated & & 93.68 \% & 96.92 \% & 86.15 \% & deprecated / & \\
modat3D & on & 93.66 \% & 94.26 \% & 83.63 \% & 0.03 s / GPU & \\
MVAF-Net & & 93.66 \% & 95.37 \% & 90.90 \% & 0.06 s / 1 core & G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.\\
TBD & & 93.64 \% & 95.31 \% & 91.21 \% & 0.07 s / 1 core & \\
CBi-GNN & & 93.60 \% & 98.89 \% & 88.47 \% & 0.03 s / 1 core & \\
MVX-Net++ & & 93.58 \% & 96.41 \% & 88.51 \% & 0.15 s / 1 core & \\
AM-SSD & & 93.58 \% & 96.78 \% & 90.61 \% & 0.04 s / 1 core & \\
MonoPair & & 93.55 \% & 96.61 \% & 83.55 \% & 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.\\
EBM3DOD & & 93.54 \% & 96.81 \% & 88.33 \% & 0.12 s / 1 core & F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.\\
CM3DV & & 93.53 \% & 96.79 \% & 88.35 \% & 0.02 s / 1 core & \\
CIA-SSD v2 & la & 93.52 \% & 96.63 \% & 88.21 \% & 0.03 s / 1 core & \\
Deep MANTA & & 93.50 \% & 98.89 \% & 83.21 \% & 0.7 s / GPU & F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.\\
Point-GNN & la & 93.50 \% & 96.58 \% & 88.35 \% & 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 & & 93.50 \% & 96.58 \% & 88.35 \% & 0.1 s / 1 core & \\
FPGNN & & 93.49 \% & 96.58 \% & 88.35 \% & 0.05 s / 1 core & \\
FCY & la & 93.49 \% & 96.74 \% & 88.39 \% & 0.02 s / GPU & \\
Seg-RCNN & & 93.49 \% & 96.74 \% & 88.10 \% & 0.08 s / 1 core & \\
CJJ & & 93.48 \% & 96.68 \% & 90.63 \% & 0.04 s / 1 core & \\
AIMC-RUC & & 93.47 \% & 96.75 \% & 88.35 \% & 0.08 s / 1 core & \\
PointRes & la & 93.47 \% & 96.69 \% & 90.46 \% & 0.013 s / 1 core & \\
dgist\_multiDetNet & & 93.46 \% & 94.99 \% & 85.46 \% & 0.08 s / & \\
CDE-Net(0.3) & & 93.45 \% & 96.72 \% & 88.31 \% & 0.05 s / GPU & P. An, J. Liang, J. Ma, K. Yu and B. Fang: Multi sensor fusion 3d object detection method. Submitted to OSA 2021.\\
EBM3DOD baseline & & 93.45 \% & 96.72 \% & 88.25 \% & 0.05 s / 1 core & F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.\\
Cas-SSD & & 93.41 \% & 96.73 \% & 88.30 \% & 0.1 s / 1 core & \\
RRC & & 93.40 \% & 95.68 \% & 87.37 \% & 3.6 s / GPU & J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.\\
DGIST MT-CNN & & 93.39 \% & 95.16 \% & 85.50 \% & 0.09 s / GPU & \\
KNN-GCNN & & 93.39 \% & 96.19 \% & 88.17 \% & 0.4 s / 1 core & \\
F-3DNet & & 93.38 \% & 96.51 \% & 88.32 \% & 0.5 s / GPU & \\
HR-Cascade-RCNN & & 93.37 \% & 95.74 \% & 87.44 \% & 0.3 s / 1 core & \\
3D-CVF at SPA & la & 93.36 \% & 96.78 \% & 86.11 \% & 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.\\
PSS & & 93.36 \% & 96.64 \% & 90.52 \% & 0.05 s / 1 core & \\
FLID & & 93.35 \% & 95.90 \% & 85.69 \% & 0.04 s / GPU & \\
ISF-v2 & & 93.34 \% & 96.73 \% & 90.54 \% & 0.04 s / 1 core & \\
CDE-Net(0.4) & & 93.31 \% & 96.59 \% & 88.23 \% & 0.05 s / 1 core & P. An, J. Liang, J. Ma, K. Yu and B. Fang: Multi sensor fusion 3d object detection method. Submitted to OSA 2021.\\
STD & & 93.22 \% & 96.14 \% & 90.53 \% & 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.\\
SARPNET & & 93.21 \% & 96.07 \% & 88.09 \% & 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.\\
H^23D R-CNN & & 93.20 \% & 96.20 \% & 90.55 \% & 0.03 s / 1 core & \\
RoIFusion & & 93.19 \% & 96.29 \% & 88.14 \% & 0.22 s / 1 core & \\
Fast Point R-CNN & la & 93.18 \% & 96.13 \% & 87.68 \% & 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.\\
sensekitti & & 93.17 \% & 94.79 \% & 84.38 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
RethinkDet3D & & 93.14 \% & 96.16 \% & 88.17 \% & 0.15 s / 1 core & \\
Discrete-PointDet & & 93.14 \% & 96.36 \% & 87.82 \% & 0.02 s / 1 core & \\
SJTU-HW & & 93.11 \% & 96.30 \% & 82.21 \% & 0.85s / GPU & S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.\\
BLPNet\_V2 & & 93.11 \% & 96.07 \% & 88.06 \% & 0.04 s / 1 core & \\
PVF-NET & & 93.08 \% & 96.03 \% & 88.04 \% & 0.1 s / 1 core & \\
3DIoU+++ & & 93.06 \% & 96.08 \% & 90.53 \% & 0.1 s / 1 core & \\
HVPR & & 93.04 \% & 95.91 \% & 87.88 \% & 0.02 s / GPU & \\
SerialR-FCN+SG-NMS & & 93.03 \% & 95.81 \% & 83.00 \% & 0.2 s / 1 core & \\
NLK-ALL & & 92.98 \% & 95.73 \% & 88.13 \% & 0.04 s / 1 core & \\
CLOCs\_SecCas & & 92.95 \% & 95.43 \% & 89.21 \% & 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.\\
cvMax & & 92.84 \% & 96.14 \% & 87.87 \% & 0.04 s / GPU & \\
TBD & & 92.82 \% & 96.06 \% & 88.00 \% & 0.04 s / 1 core & \\
HotSpotNet & & 92.81 \% & 96.21 \% & 89.80 \% & 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.\\
deprecated & & 92.79 \% & 95.56 \% & 91.62 \% & 0.06 s / 1 core & \\
deprecated & & 92.79 \% & 96.12 \% & 87.78 \% & 0.04 s / GPU & \\
PointCSE & & 92.78 \% & 95.99 \% & 87.66 \% & 0.02 s / 1 core & \\
IGRP & & 92.78 \% & 96.28 \% & 87.81 \% & 0.18 s / 1 core & \\
DPointNet & & 92.77 \% & 95.55 \% & 89.63 \% & 0.07s / 1 core & \\
Mono3CN & & 92.76 \% & 95.51 \% & 84.80 \% & 0.1 s / 1 core & \\
MuRF & & 92.74 \% & 95.74 \% & 87.64 \% & 0.05 s / GPU & \\
SegVoxelNet & & 92.73 \% & 96.00 \% & 87.60 \% & 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.\\
Patches & la & 92.72 \% & 96.34 \% & 87.63 \% & 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.\\
CenterNet3D & & 92.69 \% & 95.76 \% & 89.81 \% & 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.\\
Chovy & & 92.69 \% & 96.06 \% & 89.74 \% & 0.04 s / GPU & \\
R-GCN & & 92.67 \% & 96.19 \% & 87.66 \% & 0.16 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
NLK-3D & & 92.67 \% & 95.44 \% & 87.72 \% & 0.04 s / 1 core & \\
PI-RCNN & & 92.66 \% & 96.17 \% & 87.68 \% & 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.\\
PointPainting & la & 92.58 \% & 98.39 \% & 89.71 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
SIEV-Net & & 92.56 \% & 95.56 \% & 87.40 \% & 0.05 s / 1 core & \\
DASS & & 92.53 \% & 96.23 \% & 87.75 \% & 0.09 s / 1 core & O. Unal, L. Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. 2020.\\
3D IoU-Net & & 92.47 \% & 96.31 \% & 87.67 \% & 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.\\
PPBA & & 92.46 \% & 95.22 \% & 87.53 \% & NA s / GPU & \\
TBU & & 92.46 \% & 95.22 \% & 87.53 \% & NA s / GPU & \\
VAR & & 92.46 \% & 95.11 \% & 89.68 \% & 0.1 s / 1 core & \\
Associate-3Ddet & & 92.45 \% & 95.61 \% & 87.32 \% & 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.\\
Dccnet & & 92.34 \% & 96.00 \% & 86.85 \% & 0.05 s / 1 core & \\
PointRGCN & & 92.33 \% & 97.51 \% & 87.07 \% & 0.26 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
LZY\_RCNN & & 92.28 \% & 93.58 \% & 89.76 \% & 0.08 s / 1 core & \\
CCFNET & & 92.25 \% & 95.85 \% & 89.36 \% & 0.1 s / 1 core & \\
LSNet & & 92.23 \% & 96.06 \% & 87.35 \% & 0.09 s / GPU & \\
F-ConvNet & la & 92.19 \% & 95.85 \% & 80.09 \% & 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.\\
MDA & & 92.17 \% & 94.88 \% & 89.54 \% & 0.03 s / 1 core & \\
PFF3D & la & 92.15 \% & 95.37 \% & 87.54 \% & 0.05 s / GPU & \\
yolo4 & & 92.13 \% & 94.20 \% & 79.89 \% & 0.02 s / 1 core & \\
TBD & & 92.12 \% & 93.48 \% & 89.56 \% & 0.05 s / GPU & \\
PVNet & & 92.12 \% & 94.84 \% & 89.27 \% & 0,1 s / 1 core & \\
PBASN & & 92.07 \% & 95.51 \% & 87.04 \% & NA s / GPU & \\
SDP+RPN & & 92.03 \% & 95.16 \% & 79.16 \% & 0.4 s / GPU & F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.\\
AB3DMOT & la on & 92.00 \% & 95.88 \% & 86.98 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
MMLab-PointRCNN & la & 91.90 \% & 95.92 \% & 87.11 \% & 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.\\
MKFFNet & & 91.88 \% & 95.29 \% & 89.21 \% & 0.1 s / 1 core & \\
MMLab-PartA^2 & la & 91.86 \% & 95.03 \% & 89.06 \% & 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.\\
Pointpillar\_TV & & 91.82 \% & 94.82 \% & 88.57 \% & 0.05 s / 1 core & \\
epBRM & la & 91.77 \% & 94.59 \% & 88.45 \% & 0.1 s / GPU & K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.\\
3DBN\_2 & & 91.75 \% & 95.34 \% & 89.12 \% & 0.12 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
C-GCN & & 91.73 \% & 95.64 \% & 86.37 \% & 0.147 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
ITVD & & 91.73 \% & 95.85 \% & 79.31 \% & 0.3 s / GPU & Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.\\
yolo4\_5l & & 91.71 \% & 93.35 \% & 79.49 \% & 0.02 s / 1 core & \\
SINet+ & & 91.67 \% & 94.17 \% & 78.60 \% & 0.3 s / & X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.\\
VOXEL\_3D & & 91.61 \% & 94.50 \% & 86.37 \% & 0.1 s / 1 core & \\
Cascade MS-CNN & & 91.60 \% & 94.26 \% & 78.84 \% & 0.25 s / GPU & Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019.Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.\\
tt & & 91.59 \% & 95.15 \% & 88.72 \% & 0.08 s / 1 core & \\
MKFFNet & & 91.54 \% & 95.32 \% & 89.02 \% & 0.01s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
V3D & & 91.52 \% & 94.46 \% & 86.34 \% & 0.1 s / 1 core & \\
MKFFNet & & 91.51 \% & 95.19 \% & 89.01 \% & 0.1 s / 1 core & \\
GA-Aug & & 91.46 \% & 94.55 \% & 84.85 \% & 0.04 s / GPU & \\
MAFF-Net(DAF-Pillar) & & 91.46 \% & 94.38 \% & 83.89 \% & 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.\\
HRI-VoxelFPN & & 91.44 \% & 96.65 \% & 86.18 \% & 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.\\
CU-PointRCNN & & 91.34 \% & 97.25 \% & 86.98 \% & 0.1 s / GPU & \\
deprecated & & 91.31 \% & 96.90 \% & 83.91 \% & 0.06 s / GPU & \\
SC(DLA34+DCO) & st & 91.27 \% & 96.61 \% & 83.50 \% & 0.07 s / GPU & \\
CentrNet-FG & & 91.21 \% & 94.05 \% & 88.45 \% & 0.03 s / 1 core & \\
GAA & & 91.20 \% & 94.50 \% & 82.97 \% & 0.04 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
PointPillars & la & 91.19 \% & 94.00 \% & 88.17 \% & 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.\\
IOU-SSD & & 91.18 \% & 94.25 \% & 87.58 \% & 0.05 s / 1 core & \\
LTN & & 91.18 \% & 94.68 \% & 81.51 \% & 0.4 s / GPU & T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.\\
autonet & & 91.17 \% & 93.70 \% & 88.10 \% & 0.12 s / 1 core & \\
WS3D & la & 91.15 \% & 95.13 \% & 86.52 \% & 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.\\
EPENet & & 91.11 \% & 94.31 \% & 88.02 \% & 0.04 s / 1 core & \\
anonymous & & 91.08 \% & 96.57 \% & 82.86 \% & 1 s / 1 core & \\
SSL-RTM3D & & 91.07 \% & 96.44 \% & 81.19 \% & 0.03 s / 1 core & \\
FII-CenterNet & & 91.03 \% & 94.48 \% & 83.00 \% & 0.09 s / GPU & \\
Aston-EAS & & 91.02 \% & 93.91 \% & 77.93 \% & 0.24 s / GPU & J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.\\
MonoFlex & & 91.02 \% & 96.01 \% & 83.38 \% & 0.03 s / GPU & \\
ARPNET & & 90.99 \% & 94.00 \% & 83.49 \% & 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.\\
Bit & & 90.96 \% & 93.84 \% & 87.47 \% & 0.11 s / 1 core & \\
MonoEF & & 90.88 \% & 96.32 \% & 83.27 \% & 0.03 s / 1 core & \\
PatchNet & & 90.87 \% & 93.82 \% & 79.62 \% & 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.\\
MV3D & la & 90.83 \% & 96.47 \% & 78.63 \% & 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.\\
DLE & & 90.81 \% & 93.83 \% & 80.93 \% & 0.04 s / GPU & \\
3D IoU Loss & la & 90.79 \% & 95.92 \% & 85.65 \% & 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.\\
SINet\_VGG & & 90.79 \% & 93.59 \% & 77.53 \% & 0.2 s / & X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.\\
TBD & & 90.77 \% & 95.25 \% & 86.59 \% & 0.3 s / 1 core & \\
OCM3D & & 90.70 \% & 94.36 \% & 84.56 \% & 0.5 s / 1 core & \\
Simple3D Net & & 90.70 \% & 93.54 \% & 87.81 \% & 0.02 s / 1 core & \\
TANet & & 90.67 \% & 93.67 \% & 85.31 \% & 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.\\
yolo4 & & 90.63 \% & 94.71 \% & 80.38 \% & 0.02 s / 1 core & \\
baseline & & 90.59 \% & 93.29 \% & 87.18 \% & 0.12 s / 1 core & \\
Det3D & & 90.54 \% & 94.35 \% & 84.40 \% & 0.5 s / 1 core & \\
FADNet & & 90.49 \% & 96.15 \% & 80.71 \% & 0.04 s / GPU & \\
IGRP+ & & 90.42 \% & 96.03 \% & 87.63 \% & 0.18 s / 1 core & \\
CG-Stereo & st & 90.38 \% & 96.31 \% & 82.80 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
yolo4\_5l & & 90.38 \% & 91.79 \% & 80.64 \% & 0.02 s / 1 core & \\
SCNet & la & 90.30 \% & 95.59 \% & 85.09 \% & 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.\\
APL-Second & & 90.20 \% & 93.20 \% & 82.95 \% & 0.05 s / 1 core & \\
Deep3DBox & & 90.19 \% & 94.71 \% & 76.82 \% & 1.5 s / GPU & A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.\\
FQNet & & 90.17 \% & 94.72 \% & 76.78 \% & 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.\\
DeepStereoOP & & 90.06 \% & 95.15 \% & 79.91 \% & 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.\\
SubCNN & & 89.98 \% & 94.26 \% & 79.78 \% & 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.\\
MLOD & la & 89.97 \% & 94.88 \% & 84.98 \% & 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.\\
GPP & & 89.96 \% & 94.02 \% & 81.13 \% & 0.23 s / GPU & A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. IEEE Transactions on Intelligent Vehicles 2020.\\
LCA & & 89.94 \% & 93.40 \% & 82.76 \% & 0.05 s / 1 core & \\
AVOD & la & 89.88 \% & 95.17 \% & 82.83 \% & 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.\\
SINet\_PVA & & 89.86 \% & 92.72 \% & 76.47 \% & 0.11 s / & X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.\\
MCA & & 89.72 \% & 93.42 \% & 79.96 \% & 0.04 s / 1 core & \\
UDI-mono3D & & 89.67 \% & 94.39 \% & 80.29 \% & 0.05 s / 1 core & \\
3DOP & st & 89.55 \% & 92.96 \% & 79.38 \% & 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.\\
IAFA & & 89.46 \% & 93.08 \% & 79.83 \% & 0.04 s / 1 core & \\
Mono3D & & 89.37 \% & 94.52 \% & 79.15 \% & 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.\\
4d-MSCNN & st & 89.37 \% & 92.40 \% & 77.00 \% & 0.3 min / GPU & P. Ferraz, B. Oliveira, F. Ferreira, C. Silva Martins and others: Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision. IET Intelligent Transport Systems 2020.\\
R-FCN(FPN) & & 89.35 \% & 93.53 \% & 79.35 \% & 0.2 s / 1 core & \\
Scan\_YOLO & & 88.95 \% & 90.69 \% & 79.85 \% & 0.1 s / 4 cores & \\
AVOD-FPN & la & 88.92 \% & 94.70 \% & 84.13 \% & 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.\\
autoRUC & & 88.88 \% & 94.23 \% & 81.35 \% & 0.12 s / 1 core & \\
Prune & & 88.85 \% & 94.20 \% & 81.31 \% & 0.11 s / 1 core & \\
AM3D & & 88.71 \% & 92.55 \% & 77.78 \% & 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.\\
EACV & & 88.70 \% & 94.51 \% & 81.15 \% & 0.04 s / 1 core & \\
MS-CNN & & 88.68 \% & 93.87 \% & 76.11 \% & 0.4 s / GPU & Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.\\
PMN & & 88.65 \% & 93.64 \% & 77.94 \% & 0.2 s / 1 core & \\
MonoPSR & & 88.50 \% & 93.63 \% & 73.36 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
BFF & & 88.49 \% & 90.84 \% & 78.84 \% & 8.4 s / 4 cores & \\
Shift R-CNN (mono) & & 88.48 \% & 94.07 \% & 78.34 \% & 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.\\
PSMD & & 88.47 \% & 93.67 \% & 75.62 \% & 0.1 s / GPU & \\
RCD & & 88.46 \% & 92.52 \% & 83.73 \% & 0.1 s / GPU & A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.\\
MM-MRFC & fl la & 88.46 \% & 95.54 \% & 78.14 \% & 0.05 s / GPU & A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.\\
AACL & & 88.35 \% & 93.56 \% & 73.57 \% & 0.1 s / 1 core & \\
3DBN & la & 88.29 \% & 93.74 \% & 80.74 \% & 0.13s / & X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.\\
UDI-mono3D & & 88.16 \% & 93.93 \% & 79.57 \% & 0.05 s / 1 core & \\
anonymous & & 88.16 \% & 96.22 \% & 75.72 \% & 1 s / 1 core & \\
tiny-stereo-v2 & st & 88.16 \% & 96.49 \% & 80.74 \% & 0.4 s / 1 core & \\
tiny-stereo-v1 & st & 88.00 \% & 96.14 \% & 80.59 \% & 0.3 s / GPU & \\
CDI3D & & 87.97 \% & 91.46 \% & 80.14 \% & 0.03 s / GPU & \\
MonoRUn & & 87.91 \% & 95.48 \% & 78.10 \% & 0.07 s / GPU & \\
Multi-task DG & & 87.72 \% & 95.50 \% & 75.51 \% & 0.06 s / GPU & \\
Object Transformer & & 87.67 \% & 93.33 \% & 79.98 \% & 0.05 s / 1 core & \\
MMCOM & & 87.58 \% & 95.08 \% & 77.48 \% & 0.04 s / 1 core & \\
SMOKE & & 87.51 \% & 93.21 \% & 77.66 \% & 0.03 s / GPU & Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.\\
DAMNET & & 87.39 \% & 92.48 \% & 82.41 \% & 1 s / 1 core & \\
MA & & 87.29 \% & 93.21 \% & 79.82 \% & 0.1 s / 1 core & \\
CDN & st & 87.19 \% & 95.85 \% & 79.43 \% & 0.6 s / GPU & D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.\\
IMA & & 87.17 \% & 92.67 \% & 77.46 \% & 0.1 s / 1 core & \\
RTM3D & & 86.93 \% & 91.82 \% & 77.41 \% & 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.\\
yolo\_rgb & & 86.90 \% & 90.01 \% & 77.52 \% & 0.07 s / GPU & \\
NL\_M3D & & 86.80 \% & 91.31 \% & 72.37 \% & 0.2 s / 1 core & \\
voxelrcnn & & 86.69 \% & 94.60 \% & 79.91 \% & 15 s / 1 core & \\
DSGN & st & 86.43 \% & 95.53 \% & 78.75 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
OSE & st & 86.21 \% & 95.64 \% & 76.83 \% & 0.1 s / GPU & \\
Stereo R-CNN & st & 85.98 \% & 93.98 \% & 71.25 \% & 0.3 s / GPU & P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.\\
StereoFENet & st & 85.70 \% & 91.48 \% & 77.62 \% & 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.\\
PLDet3d & & 85.51 \% & 88.65 \% & 77.30 \% & 0.11 s / 1 core & \\
ResNet-RRC\_Car & & 85.33 \% & 91.45 \% & 74.27 \% & 0.06 s / GPU & H. Jeon and others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.\\
PL++ (SDN+GDC) & st la & 85.15 \% & 94.95 \% & 77.78 \% & 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.\\
M3D-RPN & & 85.08 \% & 89.04 \% & 69.26 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
Center3D & & 85.05 \% & 95.14 \% & 73.06 \% & 0.05 s / GPU & \\
CDN-PL++ & st & 85.01 \% & 94.66 \% & 77.60 \% & 0.4 s / GPU & D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.\\
SDP+CRC (ft) & & 85.00 \% & 92.06 \% & 71.71 \% & 0.6 s / GPU & F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.\\
bifpn\_fsrn & & 84.93 \% & 93.68 \% & 74.45 \% & 0.07 s / 1 core & \\
ResNet-RRC (pruned) & & 84.93 \% & 89.59 \% & 73.26 \% & 0.11 s / GPU & \\
IDA-3D & st & 84.92 \% & 92.79 \% & 74.75 \% & 0.08 s / 1 core & \\
SS3D & & 84.92 \% & 92.72 \% & 70.35 \% & 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.\\
MP-Mono & & 84.83 \% & 91.58 \% & 65.89 \% & 0.16 s / GPU & \\
ResNet-RRC & & 84.81 \% & 89.43 \% & 73.18 \% & 0.11 s / GPU & \\
MonoFENet & & 84.63 \% & 91.68 \% & 76.71 \% & 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.\\
MV3D (LIDAR) & la & 84.39 \% & 93.08 \% & 79.27 \% & 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.\\
Complexer-YOLO & la & 84.16 \% & 91.92 \% & 79.62 \% & 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.\\
ZoomNet & st & 83.92 \% & 94.22 \% & 69.00 \% & 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.\\
LAPNet & & 83.85 \% & 90.81 \% & 65.37 \% & 0.03 s / 1 core & \\
D4LCN & & 83.67 \% & 90.34 \% & 65.33 \% & 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.\\
seivl & & 83.60 \% & 90.35 \% & 81.76 \% & 0.1 s / 1 core & \\
Deprecated & & 83.39 \% & 89.00 \% & 64.29 \% & Deprecated / & \\
DAMono3D & & 83.36 \% & 88.94 \% & 64.23 \% & 0.09s / 1 core & \\
Faster R-CNN & & 83.16 \% & 88.97 \% & 72.62 \% & 2 s / GPU & S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.\\
Mag & & 83.15 \% & 94.24 \% & 70.63 \% & 0.07 s / 1 core & \\
MTMono3d & & 83.11 \% & 90.55 \% & 75.48 \% & 0.05 s / 1 core & \\
SSL-RTM3D Res18 & & 82.97 \% & 93.35 \% & 73.11 \% & 0.02 s / GPU & \\
Pseudo-LiDAR++ & st & 82.90 \% & 94.46 \% & 75.45 \% & 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.\\
Disp R-CNN & st & 82.86 \% & 93.64 \% & 68.33 \% & 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.\\
DP3D & & 82.81 \% & 87.85 \% & 66.80 \% & 0.05 s / GPU & \\
BS3D & & 82.72 \% & 95.35 \% & 70.01 \% & 22 ms / & N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.\\
Disp R-CNN (velo) & st & 82.64 \% & 93.31 \% & 68.20 \% & 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.\\
DP3D & & 82.63 \% & 87.90 \% & 66.62 \% & 0.07 s / GPU & \\
deprecated & & 82.23 \% & 92.21 \% & 67.87 \% & / 1 core & \\
S3D & & 82.18 \% & 91.77 \% & 67.82 \% & 0.1 s / 1 core & \\
Stereo3D & st & 82.15 \% & 94.81 \% & 62.17 \% & 0.1 s / & \\
LNET & & 82.02 \% & 91.49 \% & 67.71 \% & 0.05 s / 1 core & \\
FRCNN+Or & & 82.00 \% & 92.91 \% & 68.79 \% & 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.\\
DDMP-3D & & 81.70 \% & 91.15 \% & 63.12 \% & 0.18 s / 1 core & \\
yyyyolo & & 81.33 \% & 94.36 \% & 68.72 \% & 0.01 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
LCD3D & & 81.25 \% & 91.29 \% & 64.55 \% & 0.03 s / GPU & \\
A3DODWTDA (image) & & 81.25 \% & 78.96 \% & 70.56 \% & 0.8 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
RefineNet & & 81.01 \% & 91.91 \% & 65.67 \% & 0.20 s / GPU & R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016.R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.\\
CaDDN & & 80.73 \% & 93.61 \% & 71.09 \% & 0.63 s / GPU & \\
UM3D\_TUM & & 80.36 \% & 92.88 \% & 65.95 \% & 0.05 s / 1 core & \\
3D-GCK & & 80.19 \% & 89.55 \% & 68.08 \% & 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.\\
YoloMono3D & & 79.63 \% & 92.37 \% & 59.69 \% & 0.05 s / GPU & \\
DA-3Ddet & & 79.47 \% & 89.49 \% & 63.04 \% & 0.4 s / GPU & \\
ITS-MDPL & & 79.20 \% & 92.45 \% & 71.88 \% & 0.16 s / GPU & \\
A3DODWTDA & la & 79.15 \% & 82.98 \% & 68.30 \% & 0.08 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
MTNAS & & 78.82 \% & 88.96 \% & 67.07 \% & 0.02 s / 1 core & \\
spLBP & & 78.66 \% & 81.66 \% & 61.69 \% & 1.5 s / 8 cores & Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.\\
3D-SSMFCNN & & 78.19 \% & 77.92 \% & 69.19 \% & 0.1 s / GPU & L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.\\
MonoGRNet & & 77.94 \% & 88.65 \% & 63.31 \% & 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.\\
VN3D & & 77.90 \% & 86.89 \% & 72.05 \% & 0.02 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
Reinspect & & 77.48 \% & 90.27 \% & 66.73 \% & 2s / 1 core & R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.\\
multi-task CNN & & 77.18 \% & 86.12 \% & 68.09 \% & 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.\\
Regionlets & & 76.99 \% & 88.75 \% & 60.49 \% & 1 s / >8 cores & X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.\\
3DVP & & 76.98 \% & 84.95 \% & 65.78 \% & 40 s / 8 cores & Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.\\
SubCat & & 76.36 \% & 84.10 \% & 60.56 \% & 0.7 s / 6 cores & E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.\\
GS3D & & 76.35 \% & 86.23 \% & 62.67 \% & 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.\\
AOG & & 76.24 \% & 86.08 \% & 61.51 \% & 3 s / 4 cores & T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016.B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.\\
Pose-RCNN & & 75.83 \% & 89.59 \% & 64.06 \% & 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.\\
3D FCN & la & 74.65 \% & 86.74 \% & 67.85 \% & >5 s / 1 core & B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.\\
OC Stereo & st & 74.60 \% & 87.39 \% & 62.56 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
yolo\_depth & & 74.40 \% & 88.71 \% & 65.58 \% & 0.07 s / GPU & \\
RTS3D & & 73.08 \% & 80.48 \% & 64.02 \% & 0.03 s / GPU & \\
NCL & & 71.91 \% & 64.71 \% & 71.78 \% & NA s / 1 core & \\
Kinematic3D & & 71.73 \% & 89.67 \% & 54.97 \% & 0.12 s / 1 core & G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .\\
AOG-View & & 71.26 \% & 85.01 \% & 55.73 \% & 3 s / 1 core & B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.\\
DAM & & 70.78 \% & 90.08 \% & 61.38 \% & 1 s / GPU & \\
MV-RGBD-RF & la & 70.70 \% & 77.89 \% & 57.41 \% & 4 s / 4 cores & A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.\\
Vote3Deep & la & 70.30 \% & 78.95 \% & 63.12 \% & 1.5 s / 4 cores & M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.\\
ROI-10D & & 70.16 \% & 76.56 \% & 61.15 \% & 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.\\
RetinaMono & & 69.01 \% & 75.18 \% & 58.98 \% & 0.02 s / 1 core & \\
BirdNet+ & la & 68.05 \% & 92.10 \% & 65.61 \% & 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.\\
Decoupled-3D & & 67.92 \% & 87.78 \% & 54.53 \% & 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.\\
SparVox3D & & 67.88 \% & 83.76 \% & 52.56 \% & 0.05 s / GPU & \\
Pseudo-Lidar & st & 67.79 \% & 85.40 \% & 58.50 \% & 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.\\
OC-DPM & & 67.06 \% & 79.07 \% & 52.61 \% & 10 s / 8 cores & B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.\\
DPM-VOC+VP & & 66.72 \% & 82.15 \% & 49.01 \% & 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.\\
BdCost48LDCF & & 66.63 \% & 81.38 \% & 52.20 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
RefinedMPL & & 65.24 \% & 88.29 \% & 53.20 \% & 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.\\
MDPM-un-BB & & 64.06 \% & 79.74 \% & 49.07 \% & 60 s / 4 core & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.\\
TLNet (Stereo) & st & 63.53 \% & 76.92 \% & 54.58 \% & 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.\\
PDV-Subcat & & 63.24 \% & 78.27 \% & 47.67 \% & 7 s / 1 core & J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.\\
PG-MonoNet & & 62.75 \% & 70.87 \% & 54.34 \% & 0.19 s / GPU & \\
MODet & la & 62.54 \% & 66.06 \% & 60.04 \% & 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.\\
SubCat48LDCF & & 61.16 \% & 78.86 \% & 44.69 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
DPM-C8B1 & st & 60.21 \% & 75.24 \% & 44.73 \% & 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.\\
FPIOD & la & 60.04 \% & 78.81 \% & 50.13 \% & 0.05 s / 1 core & \\
SAMME48LDCF & & 58.38 \% & 77.47 \% & 44.43 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
LSVM-MDPM-sv & & 58.36 \% & 71.11 \% & 43.22 \% & 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.\\
BirdNet & la & 57.12 \% & 79.30 \% & 55.16 \% & 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.\\
ACF-SC & & 56.60 \% & 69.90 \% & 43.61 \% & & C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.\\
LSVM-MDPM-us & & 55.95 \% & 68.94 \% & 41.45 \% & 10 s / 4 cores & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.\\
ACF & & 54.09 \% & 63.05 \% & 41.81 \% & 0.2 s / 1 core & P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .\\
Mono3D\_PLiDAR & & 53.36 \% & 80.85 \% & 44.80 \% & 0.1 s / & X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.\\
RT3D-GMP & st & 51.95 \% & 62.41 \% & 39.14 \% & 0.06 s / GPU & \\
VeloFCN & la & 51.82 \% & 70.53 \% & 45.70 \% & 1 s / GPU & B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .\\
SF & st la & 46.68 \% & 60.62 \% & 38.22 \% & 0.5 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
Vote3D & la & 45.94 \% & 54.38 \% & 40.48 \% & 0.5 s / 4 cores & D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.\\
TopNet-HighRes & la & 45.85 \% & 58.04 \% & 41.11 \% & 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.\\
RT3DStereo & st & 45.81 \% & 56.53 \% & 37.63 \% & 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.\\
Multimodal Detection & la & 45.46 \% & 63.91 \% & 37.25 \% & 0.06 s / GPU & A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.\\
RT3D & la & 39.69 \% & 50.33 \% & 40.04 \% & 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.\\
VoxelJones & & 36.31 \% & 43.89 \% & 34.16 \% & .18 s / 1 core & M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.\\
CSoR & la & 21.66 \% & 31.52 \% & 17.99 \% & 3.5 s / 4 cores & L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.\\
mBoW & la & 21.59 \% & 35.22 \% & 16.89 \% & 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.\\
DepthCN & la & 21.18 \% & 37.45 \% & 16.08 \% & 2.3 s / GPU & A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.\\
YOLOv2 & & 14.31 \% & 26.74 \% & 10.94 \% & 0.02 s / GPU & J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.\\
TopNet-UncEst & la & 6.24 \% & 7.24 \% & 5.42 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
TopNet-Retina & la & 5.00 \% & 6.82 \% & 4.52 \% & 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.\\
TopNet-DecayRate & la & 0.01 \% & 0.00 \% & 0.01 \% & 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.\\
LaserNet & & 0.00 \% & 0.00 \% & 0.00 \% & 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.\\
Neighbor-VoteNet & & 0.00 \% & 0.00 \% & 0.00 \% & 0.1 s / 1 core &
\end{tabular}