\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
THU CV-AI & & 91.97 \% & 91.96 \% & 84.57 \% & 0.38 s / GPU & \\
DH-ARI & & 91.48 \% & 90.87 \% & 82.25 \% & 4s / GPU & \\
HRI-SH & & 90.71 \% & 91.34 \% & 84.28 \% & 3.6 s / GPU & \\
BM-NET & & 90.50 \% & 90.81 \% & 83.92 \% & 0.5 s / GPU & \\
MVRA + I-FRCNN+ & & 90.36 \% & 90.78 \% & 80.48 \% & 0.18 s / GPU & \\
FichaDL & & 90.36 \% & 90.88 \% & 80.15 \% & 0.1 s / GPU & \\
TuSimple & & 90.33 \% & 90.77 \% & 82.86 \% & 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.\\
RRC & & 90.23 \% & 90.61 \% & 87.44 \% & 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.\\
CFENet & & 90.22 \% & 90.33 \% & 84.85 \% & 4 s / GPU & \\
UberATG-MMF & la & 90.17 \% & 91.82 \% & 88.54 \% & 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.\\
PC-CNN-V2 & la & 90.15 \% & 90.79 \% & 87.58 \% & 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.\\
EM-FPS & & 90.15 \% & 90.61 \% & 84.01 \% & 0.15 s / GPU & \\
HRI-FusionRCNN & & 90.10 \% & 90.75 \% & 81.09 \% & 0.1 s / 1 core & \\
THICV-YDM & & 90.08 \% & 90.40 \% & 80.41 \% & 0.06 s / GPU & \\
SJTU-HW & & 90.08 \% & 90.81 \% & 79.98 \% & 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.\\
MDC & la & 90.03 \% & 90.72 \% & 80.87 \% & 0.17 s / GPU & \\
Deep MANTA & & 90.03 \% & 97.25 \% & 80.62 \% & 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.\\
sensekitti & & 90.00 \% & 90.76 \% & 81.83 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
F-PointNet & la & 90.00 \% & 90.78 \% & 80.80 \% & 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.\\
Cascade MS-CNN & & 89.95 \% & 90.68 \% & 78.40 \% & 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.\\
ECV-NET & & 89.93 \% & 90.61 \% & 81.81 \% & 0.4 s / GPU & \\
HRI-VoxelFPN & & 89.89 \% & 90.66 \% & 80.97 \% & 0.02 s / GPU & B. Wang, J. An and J. Cao: Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds. arXiv preprint arXiv:1907.05286v2 2019.\\
ART-Det & & 89.89 \% & 95.18 \% & 80.03 \% & 0.067s / GPU & \\
FNV2 & & 89.88 \% & 90.51 \% & 80.66 \% & 0.18 s / GPU & \\
Det-RGBD & st & 89.83 \% & 90.45 \% & 80.56 \% & 0.58 s / GPU & \\
MBR-SSD & & 89.82 \% & 90.32 \% & 82.28 \% & 4.0 s / GPU & \\
F-ConvNet & la & 89.79 \% & 90.44 \% & 80.66 \% & 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.\\
PointRCNN-deprecated & la & 89.75 \% & 90.77 \% & 80.98 \% & 0.1 s / GPU & \\
SINet+ & & 89.73 \% & 90.51 \% & 77.82 \% & 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.\\
STD & & 89.72 \% & 90.57 \% & 88.90 \% & 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.\\
RGB3D & la & 89.71 \% & 90.75 \% & 88.21 \% & 0.39 s / GPU & \\
Fast Point R-CNNv1.1 & la & 89.71 \% & 90.59 \% & 88.13 \% & 0.06 s / GPU & Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. ICCV 2019.\\
Alibaba-AILabsX & la & 89.69 \% & 90.59 \% & 80.83 \% & 0.2 s / GPU & \\
AILabs3D & la & 89.68 \% & 90.57 \% & 80.67 \% & 0.6 s / GPU & \\
Alibaba-AILabsX & la & 89.65 \% & 90.47 \% & 80.81 \% & 0.05 s / 1 core & \\
Aston-EAS & & 89.64 \% & 90.49 \% & 77.95 \% & 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.\\
Patches & la & 89.61 \% & 90.75 \% & 87.42 \% & 0.15 s / GPU & \\
epBRM & la & 89.58 \% & 90.50 \% & 87.51 \% & 0.1 s / GPU & \\
SINet\_VGG & & 89.56 \% & 90.60 \% & 78.19 \% & 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.\\
Patches - EMP & la & 89.55 \% & 94.64 \% & 88.02 \% & 0.5 s / GPU & \\
PFPN & & 89.54 \% & 90.52 \% & 80.76 \% & 0.02 s / 4 cores & \\
Fast Point R-CNN & la & 89.51 \% & 90.58 \% & 87.89 \% & 0.06 s / GPU & \\
DH-ARI & & 89.47 \% & 90.31 \% & 84.78 \% & 0.2 s / 1 core & \\
SDP+RPN & & 89.42 \% & 89.90 \% & 78.54 \% & 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.\\
VAT-Net & & 89.41 \% & 90.69 \% & 79.97 \% & 1 s / GPU & \\
SegVoxelNet & & 89.37 \% & 90.62 \% & 88.02 \% & 0.04 s / 1 core & \\
MMLab-PartA^2 & la & 89.34 \% & 90.60 \% & 87.57 \% & 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.\\
ZRNet(ResNet-50) & & 89.34 \% & 89.91 \% & 79.28 \% & 0.04 s / GPU & \\
MMLab-PointRCNN & la & 89.32 \% & 90.74 \% & 85.73 \% & 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.\\
ARPNET & & 89.30 \% & 90.50 \% & 80.64 \% & 0.08 s / GPU & \\
IPOD & & 89.30 \% & 90.20 \% & 87.37 \% & 0.2 s / GPU & \\
TBA & & 89.30 \% & 90.42 \% & 87.17 \% & 0.07 s / 1 core & \\
AB3DMOT & la on & 89.28 \% & 90.67 \% & 86.45 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
3D IoU Loss & la & 89.26 \% & 90.34 \% & 80.62 \% & 0.08 s / GPU & D. Zhou: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.\\
MVX-Net & la & 89.25 \% & 90.43 \% & 80.47 \% & 0.06 s / GPU & \\
ITVD & & 89.23 \% & 90.57 \% & 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.\\
PointPillars & la & 89.22 \% & 90.33 \% & 87.04 \% & 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.\\
MMV & & 89.22 \% & 90.59 \% & 80.49 \% & 0.4 s / GPU & \\
MPNet & la & 89.21 \% & 90.60 \% & 86.19 \% & 0.02 s / GPU & \\
CONV-BOX & la & 89.20 \% & 90.35 \% & 87.88 \% & 0.2 s / & \\
VCTNet & & 89.20 \% & 89.60 \% & 80.04 \% & 0.02 s / GPU & \\
4D-MSCNN+CRL & st & 89.19 \% & 90.32 \% & 76.26 \% & 0.2 s / GPU & \\
Tencent\_ADlab\_Lidar & la & 89.17 \% & 90.43 \% & 85.82 \% & 0.1 s / GPU & \\
Sogo\_MM & & 89.17 \% & 90.80 \% & 79.58 \% & 1.5 s / GPU & \\
MV3D & la & 89.17 \% & 90.53 \% & 80.16 \% & 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.\\
NU-optim & & 89.17 \% & 90.24 \% & 87.92 \% & 0.04 s / GPU & \\
A-VoxelNet & & 89.15 \% & 90.27 \% & 80.43 \% & 0.029 s / GPU & \\
MVSLN & & 89.14 \% & 90.65 \% & 80.64 \% & 0.1s s / 1 core & \\
SRF & & 89.14 \% & 90.34 \% & 80.52 \% & 0.05 s / GPU & \\
PTS & la & 89.12 \% & 90.38 \% & 80.46 \% & 0.01 s / 1 core & \\
YOLOv3.5 & & 89.10 \% & 89.70 \% & 79.79 \% & 0.05 s / GPU & \\
SINet\_PVA & & 89.08 \% & 90.44 \% & 75.85 \% & 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.\\
SECOND-V1.5 & la & 89.06 \% & 90.52 \% & 80.40 \% & 0.04 s / GPU & \\
AtrousDet & & 89.01 \% & 90.25 \% & 78.98 \% & 0.05 s / & \\
CLA & & 88.99 \% & 90.51 \% & 75.50 \% & 0.3 s / GPU & C. Zhang and J. Kim: Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
InNet & & 88.95 \% & 90.26 \% & 79.46 \% & 0.16 s / GPU & \\
Shift R-CNN (mono) & & 88.90 \% & 90.56 \% & 79.86 \% & 0.25 s / GPU & A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.\\
SubCNN & & 88.86 \% & 90.75 \% & 79.24 \% & 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.\\
Deep3DBox & & 88.86 \% & 90.47 \% & 77.60 \% & 1.5 s / GPU & A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.\\
MonoPSR & & 88.84 \% & 90.18 \% & 71.44 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
FQNet & & 88.83 \% & 90.45 \% & 77.55 \% & 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.\\
MS-CNN & & 88.83 \% & 90.46 \% & 74.76 \% & 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.\\
RAR-Net & & 88.83 \% & 90.45 \% & 77.55 \% & 0.5 s / 1 core & \\
ZRNet & & 88.82 \% & 89.77 \% & 79.07 \% & 0.04 s / GPU & \\
FOFNet & la & 88.81 \% & 90.58 \% & 80.38 \% & 0.04 s / GPU & \\
CFR & la & 88.77 \% & 90.53 \% & 80.23 \% & 0.06 s / 1 core & \\
RCN-resnet101 & & 88.75 \% & 89.08 \% & 79.97 \% & 0.3 s / GPU & \\
DeepStereoOP & & 88.75 \% & 90.34 \% & 79.39 \% & 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.\\
3DBN & la & 88.62 \% & 90.30 \% & 80.08 \% & 0.13s / & X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.\\
SAG-Net & & 88.61 \% & 89.25 \% & 79.72 \% & 0.2 s / GPU & \\
PP\_v1.0 & & 88.57 \% & 90.41 \% & 84.23 \% & 0.02s / 1 core & \\
PAD & & 88.47 \% & 90.36 \% & 83.19 \% & 0.15 s / 1 core & \\
SECOND & & 88.40 \% & 90.40 \% & 80.21 \% & 38 ms / & Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.\\
3DOP & st & 88.34 \% & 90.09 \% & 78.79 \% & 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.\\
GPP & & 88.24 \% & 90.42 \% & 79.02 \% & 0.23 s / GPU & A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. arXiv preprint arXiv:1811.06666 2018.\\
DA & & 88.23 \% & 90.45 \% & 74.21 \% & 0.08 s / 1 core & \\
MM-MRFC & fl la & 88.20 \% & 90.93 \% & 78.02 \% & 0.05 s / GPU & A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.\\
AVOD & la & 88.08 \% & 89.73 \% & 80.14 \% & 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.\\
Multi-3D & la & 88.04 \% & 90.10 \% & 75.31 \% & 0.15 s / 1 core & \\
cas+res+soft & & 88.00 \% & 89.82 \% & 77.66 \% & 0.2 s / 4 cores & \\
SeoulRobotics-HFD & la & 87.86 \% & 90.02 \% & 79.95 \% & 0.035 s / & \\
Mono3D & & 87.86 \% & 90.27 \% & 78.09 \% & 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.\\
merge12-12 & & 87.81 \% & 89.88 \% & 77.42 \% & 0.2 s / 4 cores & \\
DFD & & 87.78 \% & 90.02 \% & 79.78 \% & 0.05 s / GPU & \\
MonoDIS & & 87.58 \% & 90.31 \% & 76.85 \% & 0.1 s / 1 core & \\
TridentNet & & 87.49 \% & 88.38 \% & 78.97 \% & 0.2 s / GPU & \\
AVOD-FPN & la & 87.44 \% & 89.99 \% & 80.05 \% & 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.\\
SECA & & 87.42 \% & 89.57 \% & 79.43 \% & 0.09 s / GPU & \\
SCANet & & 87.31 \% & 89.34 \% & 79.30 \% & 0.09s / GPU & \\
SCANet & & 87.12 \% & 89.65 \% & 79.43 \% & 0.17 s / >8 cores & \\
ODES & & 87.10 \% & 86.82 \% & 78.32 \% & 0.02 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
ELLIOT & la & 86.98 \% & 90.20 \% & 81.41 \% & 0.1 s / 1 core & \\
cas\_retina & & 86.23 \% & 89.53 \% & 75.77 \% & 0.2 s / 4 cores & \\
cascadercnn & & 85.86 \% & 84.21 \% & 69.57 \% & 0.36 s / 4 cores & \\
ReSqueeze & & 85.74 \% & 87.12 \% & 77.02 \% & 0.03 s / GPU & \\
NLK & & 85.56 \% & 89.00 \% & 79.34 \% & 0.02 s / 1 core & \\
AM3D & & 85.42 \% & 87.33 \% & 77.43 \% & 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.\\
anm & & 85.33 \% & 90.11 \% & 76.55 \% & 3 s / 1 core & \\
IoU\_DCRCNN & & 84.48 \% & 87.68 \% & 76.70 \% & 0.66 s / GPU & \\
cas\_retina\_1\_13 & & 84.43 \% & 89.22 \% & 75.39 \% & 0.03 s / 4 cores & \\
YOLOv3+d & & 84.13 \% & 84.30 \% & 76.34 \% & 0.04 s / GPU & \\
PL V2 (SDN+GDC) & st la & 84.12 \% & 90.23 \% & 76.71 \% & 0.6 s / GPU & \\
M3D-RPN & & 83.78 \% & 84.34 \% & 67.85 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
StereoFENet & st & 83.65 \% & 89.01 \% & 77.12 \% & 0.15 s / 1 core & W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. 2019.\\
NEUAV & & 83.25 \% & 87.75 \% & 76.38 \% & 0.06 s / GPU & \\
MonoFENet & & 82.54 \% & 89.10 \% & 76.39 \% & 0.15 s / 1 core & W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. 2019.\\
cascade\_gw & & 82.24 \% & 81.09 \% & 67.95 \% & 0.2 s / 4 cores & \\
LPN & & 81.67 \% & 87.70 \% & 72.69 \% & 0.2 s / GPU & \\
A3DODWTDA (image) & & 81.54 \% & 76.21 \% & 66.85 \% & 0.8 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
SDP+CRC (ft) & & 81.33 \% & 90.39 \% & 70.33 \% & 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.\\
ResNet-RRC w/RGBD & & 81.09 \% & 89.91 \% & 71.78 \% & 0.057 s / GPU & \\
ResNet-RRC & & 81.00 \% & 89.89 \% & 71.56 \% & 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.\\
Stereo R-CNN & st & 80.80 \% & 90.23 \% & 71.42 \% & 0.3 s / GPU & P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.\\
X\_MD & & 80.65 \% & 89.81 \% & 79.66 \% & 0.2 s / 1 core & \\
FNV1\_Fusion & & 80.41 \% & 89.37 \% & 79.03 \% & 0.11 s / GPU & \\
FNV1\_RPN & & 80.41 \% & 89.44 \% & 79.14 \% & 0.12 s / 1 core & \\
Cmerge & & 80.25 \% & 89.83 \% & 70.76 \% & 0.2 s / 4 cores & \\
SS3D & & 80.11 \% & 89.15 \% & 70.52 \% & 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.\\
SECA & & 80.05 \% & 89.26 \% & 78.80 \% & 1 s / GPU & \\
VSE & & 80.05 \% & 89.26 \% & 78.80 \% & 0.15 s / GPU & \\
BS3D & & 80.02 \% & 89.85 \% & 70.14 \% & 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.\\
centernet & & 79.97 \% & 89.78 \% & 70.57 \% & 0.01 s / GPU & \\
MV3D (LIDAR) & la & 79.76 \% & 89.80 \% & 78.61 \% & 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.\\
ZKNet & & 79.62 \% & 89.89 \% & 70.03 \% & 0.01 s / GPU & \\
RFCN\_RFB & & 79.45 \% & 83.85 \% & 67.51 \% & 0.2 s / 4 cores & \\
Complexer-YOLO & la & 79.31 \% & 88.11 \% & 79.11 \% & 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.\\
FNV1 & & 79.28 \% & 88.45 \% & 77.14 \% & 0.11 s / GPU & \\
RefineNet & & 79.21 \% & 90.16 \% & 65.71 \% & 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.\\
Pseudo-LiDAR V2 & st & 79.20 \% & 89.62 \% & 75.93 \% & 0.4 s / GPU & \\
softretina & & 79.15 \% & 89.36 \% & 69.24 \% & 0.16 s / 4 cores & \\
Faster R-CNN & & 79.11 \% & 87.90 \% & 70.19 \% & 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.\\
detectron & & 78.96 \% & 88.14 \% & 69.74 \% & 0.01 s / 1 core & \\
FRCNN+Or & & 78.95 \% & 89.87 \% & 68.97 \% & 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.\\
Resnet101Faster rcnn & & 78.93 \% & 87.97 \% & 69.80 \% & 1 s / 1 core & \\
Retinanet100 & & 78.85 \% & 89.83 \% & 68.73 \% & 0.2 s / 4 cores & \\
NM & & 78.77 \% & 89.04 \% & 68.69 \% & 0.01 s / GPU & \\
SeRC & & 78.33 \% & 88.28 \% & 69.36 \% & 0.5 s / 1 core & \\
Manhnet & & 78.03 \% & 85.86 \% & 61.13 \% & 26 ms / 1 core & \\
RTL3D & & 77.63 \% & 76.95 \% & 71.17 \% & 0.02 s / GPU & \\
avodC & & 77.54 \% & 86.86 \% & 70.00 \% & 0.1 s / GPU & \\
MonoGRNet & & 77.46 \% & 87.23 \% & 61.12 \% & 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.\\
spLBP & & 77.39 \% & 80.16 \% & 60.59 \% & 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.\\
SceneNet & & 77.34 \% & 87.90 \% & 68.38 \% & 0.03 s / GPU & \\
FailNet-LIDAR & la & 77.03 \% & 71.93 \% & 71.79 \% & 0.1 s / 1 core & \\
CLF3D & la & 77.00 \% & 84.51 \% & 67.81 \% & 0.13 s / GPU & \\
MTDP & & 76.91 \% & 84.24 \% & 67.91 \% & 0.15 s / GPU & \\
yolov3\_warp & & 76.73 \% & 89.13 \% & 67.70 \% & 0.5 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
FailNet-Fusion & la & 76.69 \% & 70.11 \% & 71.40 \% & 0.1 s / 1 core & \\
Reinspect & & 76.65 \% & 88.36 \% & 66.56 \% & 2s / 1 core & R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.\\
Regionlets & & 76.56 \% & 86.50 \% & 59.82 \% & 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.\\
AOG & & 75.97 \% & 85.58 \% & 60.96 \% & 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.\\
GS3D & & 75.84 \% & 83.92 \% & 60.24 \% & 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.\\
3D FCN & la & 75.83 \% & 85.54 \% & 68.30 \% & >5 s / 1 core & B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.\\
3D-SSMFCNN & & 75.78 \% & 75.51 \% & 67.75 \% & 0.1 s / GPU & L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.\\
3DVP & & 75.77 \% & 81.46 \% & 65.38 \% & 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.\\
Pose-RCNN & & 75.74 \% & 88.89 \% & 61.86 \% & 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.\\
SubCat & & 75.46 \% & 81.45 \% & 59.71 \% & 0.7 s / 6 cores & E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.\\
myfaster-rcnn-v1.5 & & 75.29 \% & 88.35 \% & 60.91 \% & 0.1 s / 1 core & \\
multi-task CNN & & 75.21 \% & 83.45 \% & 66.89 \% & 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.\\
Multi-task DG & & 75.21 \% & 87.87 \% & 66.76 \% & 0.06 s / GPU & \\
A3DODWTDA & la & 74.71 \% & 78.21 \% & 66.70 \% & 0.08 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
FD2 & & 74.68 \% & 87.14 \% & 65.70 \% & 0.01 s / GPU & \\
BdCost+DA+MS & & 74.07 \% & 83.02 \% & 59.06 \% & TBD s / 4 cores & \\
RFCN & & 73.56 \% & 80.70 \% & 61.94 \% & 0.2 s / 4 cores & \\
VoxelNet(Unofficial) & & 73.39 \% & 79.27 \% & 65.61 \% & 0.5 s / GPU & \\
3DVSSD & & 73.39 \% & 84.39 \% & 65.64 \% & 0.06 s / 1 core & \\
bin & & 73.31 \% & 76.05 \% & 63.76 \% & 15ms s / GPU & \\
yolo800 & & 73.00 \% & 76.45 \% & 64.68 \% & 0.13 s / 4 cores & \\
ResNet-RRC (Noised) & & 71.81 \% & 78.97 \% & 63.57 \% & .057 s / GPU & \\
a & & 70.88 \% & 86.95 \% & 61.74 \% & 0.35 s / 1 core & \\
Int-YOLO & & 70.65 \% & 74.76 \% & 63.70 \% & 0.03 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
MV-RGBD-RF & la & 69.92 \% & 76.49 \% & 57.47 \% & 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.\\
AOG-View & & 69.89 \% & 84.29 \% & 57.25 \% & 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.\\
ROI-10D & & 69.64 \% & 75.33 \% & 61.18 \% & 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.\\
fasterrcnn & & 68.76 \% & 73.64 \% & 59.72 \% & 0.2 s / 4 cores & \\
MF3D & & 68.72 \% & 88.46 \% & 58.70 \% & 0.03 s / GPU & \\
myfaster-rcnn & & 68.69 \% & 89.59 \% & 58.39 \% & 0.01 s / 1 core & \\
Vote3Deep & la & 68.39 \% & 76.95 \% & 63.22 \% & 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.\\
Pseudo-LiDAR & st & 67.96 \% & 85.08 \% & 59.55 \% & 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. CVPR 2019.\\
GPVL & & 67.89 \% & 77.76 \% & 58.23 \% & 10 s / 1 core & \\
RFBnet & & 67.86 \% & 82.31 \% & 59.95 \% & 0.2 s / 4 cores & \\
Fast-SSD & & 67.17 \% & 83.89 \% & 59.09 \% & 0.06 s / & \\
BdCost48LDCF & & 67.08 \% & 77.93 \% & 51.15 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
SA\_3D & & 66.69 \% & 86.36 \% & 55.18 \% & 0.3 s / GPU & \\
OC-DPM & & 66.45 \% & 76.16 \% & 53.70 \% & 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.\\
mymask-rcnn & & 66.31 \% & 86.32 \% & 54.33 \% & 0.3 s / 1 core & \\
DPM-VOC+VP & & 66.25 \% & 80.45 \% & 49.86 \% & 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.\\
BdCost48-25C & & 65.95 \% & 78.21 \% & 51.23 \% & 4 s / 1 core & \\
& & 65.93 \% & 78.51 \% & 57.77 \% & / & \\
MDPM-un-BB & & 64.20 \% & 77.32 \% & 50.18 \% & 60 s / 4 core & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.\\
PDV-Subcat & & 63.15 \% & 77.33 \% & 49.75 \% & 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.\\
E-VoxelNet & & 63.00 \% & 70.24 \% & 55.94 \% & 0.1 s / GPU & \\
Lidar\_ROI+Yolo(UJS) & & 62.71 \% & 70.58 \% & 55.17 \% & 0.1 s / 1 core & \\
GNN & & 62.59 \% & 76.03 \% & 50.18 \% & 0.2 s / 1 core & \\
MODet & la & 61.84 \% & 67.21 \% & 61.57 \% & 0.05 s / & \\
yl\_net & & 61.01 \% & 66.08 \% & 61.29 \% & 0.03 s / GPU & \\
DPM-C8B1 & st & 60.99 \% & 74.95 \% & 47.16 \% & 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.\\
tiny\_rfdet & & 60.89 \% & 66.76 \% & 57.88 \% & 0.01 s / GPU & \\
SubCat48LDCF & & 60.53 \% & 78.16 \% & 43.66 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
SAMME48LDCF & & 58.50 \% & 76.22 \% & 47.50 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
monoref3d & & 58.27 \% & 74.15 \% & 49.71 \% & 0.1 s / 1 core & \\
ref3D & & 58.27 \% & 74.15 \% & 49.71 \% & 0.1 s / 1 core & \\
BirdNet & la & 57.47 \% & 78.18 \% & 56.66 \% & 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.\\
100Frcnn & & 57.47 \% & 81.09 \% & 48.37 \% & 2 s / 4 cores & \\
LSVM-MDPM-sv & & 57.44 \% & 71.70 \% & 46.58 \% & 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.\\
ref3D & & 57.14 \% & 74.81 \% & 47.95 \% & 0.1 s / 1 core & \\
mylsi-faster-rcnn & & 56.13 \% & 79.05 \% & 48.48 \% & 0.3 s / 1 core & \\
LSVM-MDPM-us & & 56.10 \% & 70.52 \% & 42.87 \% & 10 s / 4 cores & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.\\
ACF-SC & & 55.76 \% & 69.76 \% & 46.27 \% & & 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.\\
Mono3D\_PLiDAR & & 54.41 \% & 80.29 \% & 46.67 \% & 0.1 s / & X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.\\
VeloFCN & la & 53.45 \% & 70.68 \% & 46.90 \% & 1 s / GPU & B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .\\
ACF & & 52.81 \% & 62.82 \% & 43.89 \% & 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). .\\
RT3DStereo & st & 48.92 \% & 57.56 \% & 42.81 \% & 0.08 s / GPU & \\
FailNet-Mono & & 48.91 \% & 57.86 \% & 42.95 \% & 0.1 s / 1 core & \\
TopNet-HighRes & la & 48.87 \% & 59.77 \% & 43.15 \% & 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.\\
DLMB & la on & 48.76 \% & 59.32 \% & 43.19 \% & 0.03 s / 8 cores & \\
Vote3D & la & 48.05 \% & 56.66 \% & 42.64 \% & 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.\\
Multimodal Detection & la & 46.77 \% & 64.04 \% & 39.38 \% & 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.\\
softyolo & & 45.77 \% & 62.82 \% & 39.77 \% & 0.16 s / 4 cores & \\
rpn & & 43.99 \% & 65.47 \% & 36.33 \% & 0.01 s / 1 core & \\
RT3D & la & 39.71 \% & 49.96 \% & 41.47 \% & 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.\\
KD53-20 & & 37.82 \% & 52.30 \% & 32.71 \% & 0.19 s / 4 cores & \\
DT3D & & 35.98 \% & 49.23 \% & 31.78 \% & 0,21s / GPU & \\
Licar & la & 33.89 \% & 41.60 \% & 35.17 \% & 0.09 s / GPU & \\
CSoR & la & 26.13 \% & 35.24 \% & 22.69 \% & 3.5 s / 4 cores & L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.\\
R-CNN\_VGG & & 26.04 \% & 32.23 \% & 20.93 \% & 10 s / GPU & \\
FCN-Depth & & 25.66 \% & 50.55 \% & 24.95 \% & 1 s / GPU & \\
mBoW & la & 23.76 \% & 37.63 \% & 18.44 \% & 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 & 23.21 \% & 37.59 \% & 18.00 \% & 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.\\
DLnet & & 20.30 \% & 23.46 \% & 17.96 \% & 0.3 s / 4 cores & \\
YOLOv2 & & 19.31 \% & 28.37 \% & 15.94 \% & 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 & 13.77 \% & 10.35 \% & 13.49 \% & 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 & 6.36 \% & 7.79 \% & 6.31 \% & 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.\\
FCPP & & 0.20 \% & 0.02 \% & 0.22 \% & 0.02 s / 1 core & \\
TopNet-DecayRate & la & 0.04 \% & 0.04 \% & 0.04 \% & 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.\\
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.\\
SN-net & & 0.00 \% & 0.00 \% & 0.00 \% & 0.8 s / GPU & \\
JSyolo & & 0.00 \% & 0.00 \% & 0.00 \% & 0.16 s / 4 cores &
\end{tabular}