\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
MMLab PV-RCNN & la & 80.42 \% & 86.62 \% & 73.64 \% & 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. arXiv preprint arXiv:1912.13192 2019.\\
FichaDL & & 80.38 \% & 88.41 \% & 69.72 \% & 0.1 s / GPU & \\
Noah CV Lab - SSL & & 79.10 \% & 86.71 \% & 69.66 \% & 0.1 s / GPU & \\
OHS-Dense & & 78.42 \% & 85.79 \% & 71.80 \% & 0.03 s / 1 core & Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.\\
MMLab-PartA^2 & la & 78.29 \% & 88.90 \% & 71.19 \% & 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. arXiv preprint arXiv:1907.03670 2019.\\
F-ConvNet & la & 78.05 \% & 86.75 \% & 68.12 \% & 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.\\
PointPainting & la & 78.04 \% & 87.70 \% & 69.27 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. arXiv preprint arXiv:1911.10150 2019.\\
RRC & & 76.81 \% & 86.81 \% & 66.59 \% & 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.\\
HWFD & & 76.17 \% & 87.02 \% & 66.45 \% & 0.21 s / & \\
OHS-Direct & & 75.98 \% & 83.71 \% & 68.80 \% & 0.03 s / 1 core & Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.\\
Multi-3D & la & 75.77 \% & 84.71 \% & 65.95 \% & 0.15 s / 1 core & \\
MS-CNN & & 75.30 \% & 84.88 \% & 65.27 \% & 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.\\
VOXEL\_FPN\_HR & & 75.24 \% & 87.73 \% & 68.60 \% & 0.12 s / 8 cores & ERROR: Wrong syntax in BIBTEX file.\\
TuSimple & & 75.22 \% & 83.68 \% & 65.22 \% & 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.\\
Point-GNN & la & 75.08 \% & 85.75 \% & 68.69 \% & 0.6 s / GPU & \\
ExtAtt & & 75.08 \% & 86.09 \% & 65.30 \% & 1.2 s / GPU & \\
Deep3DBox & & 74.78 \% & 84.36 \% & 64.05 \% & 1.5 s / GPU & A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.\\
SDP+RPN & & 73.85 \% & 82.59 \% & 64.87 \% & 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.\\
sensekitti & & 73.48 \% & 82.90 \% & 64.03 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
MMLab-PointRCNN & la & 73.42 \% & 86.21 \% & 66.45 \% & 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.\\
F-PointNet & la & 73.16 \% & 86.86 \% & 65.21 \% & 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.\\
FOFNet & la & 72.96 \% & 87.12 \% & 66.37 \% & 0.04 s / GPU & \\
HR-SECOND & & 72.77 \% & 84.21 \% & 66.25 \% & 0.11 s / 1 core & \\
MonoPSR & & 72.08 \% & 82.06 \% & 62.43 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
ARPNET & & 71.95 \% & 84.96 \% & 65.21 \% & 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.\\
SubCNN & & 71.72 \% & 79.36 \% & 62.74 \% & 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.\\
STD & & 71.63 \% & 83.99 \% & 64.92 \% & 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.\\
Sogo\_MM & & 71.57 \% & 79.35 \% & 62.22 \% & 1.5 s / GPU & \\
PiP & & 71.52 \% & 82.97 \% & 65.52 \% & 0.05 s / 1 core & \\
Faster RCNN + Gr + A & & 70.78 \% & 83.99 \% & 63.36 \% & 1.29 s / GPU & \\
AB3DMOT & la on & 70.18 \% & 82.86 \% & 63.55 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
TridentNet & & 69.63 \% & 81.97 \% & 59.52 \% & 0.2 s / GPU & \\
MP & & 69.52 \% & 85.05 \% & 63.17 \% & 0.2 s / 1 core & \\
LDAM & & 69.31 \% & 80.20 \% & 63.85 \% & 0.05 s / GPU & \\
PointPillars & la & 68.98 \% & 83.97 \% & 62.17 \% & 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.\\
DGIST-CellBox & & 68.92 \% & 83.72 \% & 61.32 \% & 0.1 s / GPU & \\
Vote3Deep & la & 68.82 \% & 78.41 \% & 62.50 \% & 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.\\
3DOP & st & 68.71 \% & 80.52 \% & 61.07 \% & 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.\\
Pose-RCNN & & 68.40 \% & 81.53 \% & 59.43 \% & 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.\\
TANet & & 68.20 \% & 82.24 \% & 62.13 \% & 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.\\
Faster RCNN + G & & 68.09 \% & 83.51 \% & 60.60 \% & 1.1 s / GPU & \\
Faster RCNN + A & & 67.84 \% & 82.06 \% & 60.52 \% & 0.19 s / GPU & \\
Tencent\_ADlab\_Lidar & la & 67.82 \% & 82.74 \% & 61.06 \% & 0.1 s / GPU & \\
IVA & & 67.57 \% & 78.48 \% & 58.83 \% & 0.4 s / GPU & Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 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.\\
A-VoxelNet & & 67.37 \% & 81.32 \% & 60.27 \% & 0.029 s / GPU & \\
DeepStereoOP & & 67.22 \% & 79.35 \% & 58.60 \% & 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.\\
Faster RCNN + A & & 67.15 \% & 83.77 \% & 59.85 \% & 0.19 s / GPU & \\
SAANet & & 66.58 \% & 83.07 \% & 59.88 \% & 0.10 s / 1 core & \\
epBRM & la & 66.51 \% & 79.65 \% & 60.31 \% & 0.10 s / 1 core & K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.\\
Mono3D & & 65.15 \% & 77.19 \% & 57.88 \% & 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.\\
deprecated & & 63.34 \% & 83.91 \% & 53.78 \% & 0.05 s / GPU & \\
CentrNet-v1 & la & 62.99 \% & 78.90 \% & 56.46 \% & 0.03 s / GPU & \\
Faster R-CNN & & 62.86 \% & 72.40 \% & 54.97 \% & 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.\\
AtrousDet & & 62.50 \% & 79.02 \% & 53.87 \% & 0.05 s / & \\
SCNet & la & 62.50 \% & 78.48 \% & 56.34 \% & 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.\\
SCANet & & 62.31 \% & 76.50 \% & 56.06 \% & 0.17 s / >8 cores & \\
DDB & la & 61.41 \% & 78.04 \% & 55.37 \% & 0.05 s / GPU & \\
PP-3D & & 61.29 \% & 77.75 \% & 54.59 \% & 0.1 s / 1 core & \\
AVOD-FPN & la & 60.79 \% & 70.38 \% & 55.37 \% & 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.\\
SDP+CRC (ft) & & 60.72 \% & 75.63 \% & 53.00 \% & 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.\\
CFR & la & 60.04 \% & 76.63 \% & 53.40 \% & 0.06 s / 1 core & \\
Complexer-YOLO & la & 59.78 \% & 66.94 \% & 55.63 \% & 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.\\
PP\_v1.0 & & 59.48 \% & 77.50 \% & 52.86 \% & 0.02s / 1 core & \\
merge12-12 & & 59.48 \% & 77.66 \% & 51.41 \% & 0.2 s / 4 cores & \\
cas+res+soft & & 59.43 \% & 77.85 \% & 51.34 \% & 0.2 s / 4 cores & \\
YOLOv3.5 & & 58.57 \% & 79.16 \% & 51.74 \% & 0.05 s / GPU & \\
Regionlets & & 58.52 \% & 71.12 \% & 50.83 \% & 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.\\
cascadercnn & & 58.08 \% & 77.24 \% & 51.13 \% & 0.36 s / 4 cores & \\
GA\_rpn500 & & 57.82 \% & 76.06 \% & 49.00 \% & 1 s / 1 core & \\
GA2500 & & 57.82 \% & 76.06 \% & 48.99 \% & 0.2 s / 1 core & \\
bin & & 57.62 \% & 64.36 \% & 50.70 \% & 15ms s / GPU & \\
dgist\_multiDetNet & & 57.44 \% & 78.42 \% & 49.84 \% & 0.05 s / 1 core & \\
GA\_FULLDATA & & 57.20 \% & 75.50 \% & 50.26 \% & 1 s / 4 cores & \\
cas\_retina & & 57.14 \% & 73.97 \% & 50.32 \% & 0.2 s / 4 cores & \\
FRCNN+Or & & 57.01 \% & 70.99 \% & 50.14 \% & 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.\\
CSFADet & & 56.88 \% & 73.82 \% & 50.22 \% & 0.05 s / GPU & \\
cas\_retina\_1\_13 & & 56.39 \% & 72.80 \% & 49.71 \% & 0.03 s / 4 cores & \\
MonoPair & & 56.37 \% & 74.77 \% & 48.37 \% & 0.06 s / GPU & \\
GA\_BALANCE & & 56.07 \% & 78.33 \% & 49.02 \% & 1 s / 1 core & \\
MLOD & la & 56.04 \% & 75.35 \% & 49.11 \% & 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.\\
bigger\_ga & & 55.66 \% & 73.05 \% & 47.31 \% & 1 s / 1 core & \\
Multi-task DG & & 55.30 \% & 75.48 \% & 48.22 \% & 0.06 s / GPU & \\
ReSqueeze & & 54.50 \% & 69.64 \% & 48.24 \% & 0.03 s / GPU & \\
CRCNNA & & 53.41 \% & 69.81 \% & 46.29 \% & 0.1 s / 1 core & \\
AVOD & la & 52.60 \% & 66.45 \% & 46.39 \% & 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.\\
GAFM & & 51.40 \% & 73.43 \% & 44.61 \% & 0.5 s / 1 core & \\
JSU-NET & & 51.10 \% & 72.92 \% & 44.26 \% & 0.1 s / 1 core & \\
ZKNet & & 49.48 \% & 66.29 \% & 42.81 \% & 0.01 s / GPU & \\
anm & & 49.05 \% & 66.96 \% & 43.44 \% & 3 s / 1 core & \\
ga50 & & 49.02 \% & 70.25 \% & 42.52 \% & 1 s / 1 core & \\
NEUAV & & 48.65 \% & 69.50 \% & 42.64 \% & 0.06 s / GPU & \\
LPN & & 48.57 \% & 65.77 \% & 42.66 \% & 0.2 s / GPU & \\
mylsi-faster-rcnn & & 47.90 \% & 69.04 \% & 41.72 \% & 0.3 s / 1 core & \\
fasterrcnn & & 47.87 \% & 64.39 \% & 42.03 \% & 0.2 s / 4 cores & \\
BirdNet & la & 47.64 \% & 64.97 \% & 44.66 \% & 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.\\
yolo800 & & 47.31 \% & 63.22 \% & 42.28 \% & 0.13 s / 4 cores & \\
RTM3D & & 46.79 \% & 66.89 \% & 40.09 \% & 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.\\
RFCN & & 46.70 \% & 62.09 \% & 40.71 \% & 0.2 s / 4 cores & \\
NM & & 45.82 \% & 60.69 \% & 40.83 \% & 0.01 s / GPU & \\
SS3D\_HW & & 45.53 \% & 61.79 \% & 39.03 \% & 0.4 s / GPU & \\
FCY & la & 45.41 \% & 63.53 \% & 41.09 \% & 0.02 s / GPU & \\
PG-MonoNet & & 45.40 \% & 63.75 \% & 37.14 \% & 0.19 s / GPU & \\
RFCN\_RFB & & 45.28 \% & 60.06 \% & 39.66 \% & 0.2 s / 4 cores & \\
Cmerge & & 44.87 \% & 64.38 \% & 37.80 \% & 0.2 s / 4 cores & \\
SparsePool & & 44.57 \% & 60.53 \% & 40.37 \% & 0.13 s / 8 cores & Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.\\
Shift R-CNN (mono) & & 42.96 \% & 63.24 \% & 38.22 \% & 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.\\
myfaster-rcnn-v1.5 & & 42.89 \% & 59.60 \% & 38.07 \% & 0.1 s / 1 core & \\
D4LCN & & 42.86 \% & 65.29 \% & 36.29 \% & 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. arXiv preprint arXiv:1912.04799 2019.\\
cascade\_gw & & 42.84 \% & 63.58 \% & 36.94 \% & 0.2 s / 4 cores & \\
FD2 & & 42.67 \% & 62.54 \% & 38.41 \% & 0.01 s / GPU & \\
centernet & & 42.45 \% & 58.95 \% & 37.56 \% & 0.01 s / GPU & \\
M3D-RPN & & 41.54 \% & 61.54 \% & 35.23 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
MV-RGBD-RF & la & 40.94 \% & 51.10 \% & 34.83 \% & 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.\\
MTDP & & 40.46 \% & 53.83 \% & 35.74 \% & 0.15 s / GPU & \\
Int-YOLO & & 39.83 \% & 53.34 \% & 34.16 \% & 0.03 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
GNN & & 39.80 \% & 58.30 \% & 34.56 \% & 0.2 s / 1 core & \\
SparsePool & & 36.26 \% & 44.21 \% & 32.57 \% & 0.13 s / 8 cores & Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.\\
myfaster-rcnn & & 35.81 \% & 54.28 \% & 31.82 \% & 0.01 s / 1 core & \\
SS3D & & 35.48 \% & 52.97 \% & 31.07 \% & 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.\\
DSGN & st & 35.15 \% & 49.10 \% & 31.41 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. arXiv preprint arXiv:2001.03398 2020.\\
pAUCEnsT & & 34.90 \% & 50.51 \% & 30.35 \% & 60 s / 1 core & S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.\\
Retinanet100 & & 32.30 \% & 46.60 \% & 28.29 \% & 0.2 s / 4 cores & \\
TopNet-Retina & la & 31.98 \% & 47.51 \% & 29.84 \% & 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.\\
yolov3\_warp & & 29.48 \% & 44.46 \% & 25.84 \% & 0.5 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
OC Stereo & st & 28.76 \% & 43.18 \% & 24.80 \% & 0.35 s / 1 core & \\
MMRetina & st fl la & 28.00 \% & 43.71 \% & 24.62 \% & 0.38 s / GPU & \\
Vote3D & la & 27.99 \% & 39.81 \% & 25.19 \% & 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.\\
softyolo & & 27.90 \% & 41.90 \% & 24.74 \% & 0.16 s / 4 cores & \\
LSVM-MDPM-us & & 27.81 \% & 37.66 \% & 24.83 \% & 10 s / 4 cores & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.\\
DPM-VOC+VP & & 27.73 \% & 41.58 \% & 24.61 \% & 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.\\
100Frcnn & & 27.69 \% & 43.23 \% & 23.91 \% & 2 s / 4 cores & \\
RefinedMPL & & 27.17 \% & 44.47 \% & 22.84 \% & 0.1 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.\\
LSVM-MDPM-sv & & 26.05 \% & 35.70 \% & 23.56 \% & 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.\\
DPM-C8B1 & st & 25.57 \% & 41.47 \% & 21.93 \% & 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.\\
BdCost+DA+BB+MS & & 25.52 \% & 33.92 \% & 21.14 \% & TBD s / 4 cores & \\
R-CNN\_VGG & & 25.14 \% & 34.28 \% & 22.17 \% & 10 s / GPU & \\
Lidar\_ROI+Yolo(UJS) & & 24.42 \% & 36.43 \% & 21.78 \% & 0.1 s / 1 core & \\
BdCost+DA+BB & & 20.00 \% & 26.87 \% & 16.76 \% & TBD s / 4 cores & \\
mBoW & la & 17.63 \% & 26.66 \% & 16.02 \% & 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.\\
SA\_3D & & 14.38 \% & 19.40 \% & 12.50 \% & 0.3 s / GPU & \\
TopNet-HighRes & la & 13.98 \% & 22.86 \% & 14.52 \% & 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.\\
mymask-rcnn & & 13.58 \% & 18.03 \% & 12.42 \% & 0.3 s / 1 core & \\
RT3DStereo & st & 12.96 \% & 19.58 \% & 11.47 \% & 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.\\
KD53-20 & & 12.81 \% & 20.05 \% & 11.99 \% & 0.19 s / 4 cores & \\
TopNet-UncEst & la & 12.00 \% & 18.14 \% & 11.85 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
CBNet & & 0.39 \% & 0.24 \% & 0.44 \% & 1 s / 4 cores & \\
softretina & & 0.25 \% & 0.16 \% & 0.18 \% & 0.16 s / 4 cores & \\
YOLOv2 & & 0.06 \% & 0.15 \% & 0.07 \% & 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-DecayRate & la & 0.04 \% & 0.00 \% & 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.\\
JSyolo & & 0.03 \% & 0.02 \% & 0.04 \% & 0.16 s / 4 cores &
\end{tabular}