\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
EM-FPS & & 79.24 \% & 84.28 \% & 71.22 \% & 0.15 s / GPU & \\
FichaDL & & 78.56 \% & 86.23 \% & 68.99 \% & 0.1 s / GPU & \\
MMLab-PartA^2 & la & 77.48 \% & 85.54 \% & 70.35 \% & 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.\\
RRC & & 76.49 \% & 84.96 \% & 65.46 \% & 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.\\
F-ConvNet & la & 76.18 \% & 84.75 \% & 67.55 \% & 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.\\
VCTNet & & 75.91 \% & 83.20 \% & 67.81 \% & 0.02 s / GPU & \\
SAITv1 & & 75.83 \% & 83.99 \% & 66.45 \% & 0.15 s / GPU & \\
Multi-3D & la & 74.88 \% & 82.13 \% & 65.53 \% & 0.15 s / 1 core & \\
CLA & & 74.68 \% & 82.42 \% & 65.11 \% & 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.\\
MS-CNN & & 74.45 \% & 82.34 \% & 64.91 \% & 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.\\
TuSimple & & 74.26 \% & 81.38 \% & 64.88 \% & 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.\\
ExtAtt & & 74.25 \% & 84.04 \% & 65.03 \% & 1.2 s / GPU & \\
Deep3DBox & & 73.48 \% & 82.65 \% & 64.11 \% & 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 & & 73.08 \% & 81.05 \% & 64.88 \% & 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.\\
MMLab-PointRCNN & la & 72.94 \% & 83.64 \% & 66.07 \% & 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.\\
ECV-NET & & 72.73 \% & 82.62 \% & 62.82 \% & 0.4 s / GPU & \\
STD & & 72.63 \% & 82.18 \% & 65.16 \% & 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.\\
sensekitti & & 72.50 \% & 81.76 \% & 64.00 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
F-PointNet & la & 72.25 \% & 84.90 \% & 65.14 \% & 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.\\
BOE\_IOT\_AIBD & & 71.61 \% & 82.63 \% & 63.67 \% & 0.8 s / GPU & \\
FOFNet & la & 71.45 \% & 83.39 \% & 64.44 \% & 0.04 s / GPU & \\
ARPNET & & 71.22 \% & 82.93 \% & 64.81 \% & 0.08 s / GPU & \\
SubCNN & & 70.77 \% & 77.82 \% & 62.71 \% & 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.\\
Sogo\_MM & & 70.72 \% & 77.57 \% & 62.23 \% & 1.5 s / GPU & \\
ODES & & 69.80 \% & 78.51 \% & 61.32 \% & 0.02 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
AB3DMOT & la on & 69.46 \% & 81.27 \% & 62.82 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
TridentNet & & 69.06 \% & 80.64 \% & 60.06 \% & 0.2 s / GPU & \\
MonoPSR & & 68.99 \% & 79.80 \% & 60.19 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
DSS & & 68.98 \% & 78.43 \% & 63.52 \% & 0.03 s / GPU & \\
MDC & la & 68.84 \% & 79.81 \% & 60.24 \% & 0.17 s / GPU & \\
3DOP & st & 68.81 \% & 80.17 \% & 61.36 \% & 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.\\
PointPillars & la & 68.57 \% & 82.59 \% & 62.37 \% & 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.\\
Pose-RCNN & & 68.04 \% & 80.19 \% & 59.95 \% & 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.\\
Vote3Deep & la & 67.96 \% & 76.49 \% & 62.88 \% & 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.\\
Tencent\_ADlab\_Lidar & la & 67.37 \% & 81.56 \% & 61.28 \% & 0.1 s / GPU & \\
IVA & & 67.36 \% & 77.63 \% & 59.62 \% & 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 & & 67.13 \% & 80.77 \% & 60.37 \% & 0.029 s / GPU & \\
epBRM & la & 66.04 \% & 77.78 \% & 60.39 \% & 0.10 s / 1 core & \\
DeepStereoOP & & 65.72 \% & 77.00 \% & 57.74 \% & 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.\\
IPOD & & 65.28 \% & 82.90 \% & 57.63 \% & 0.2 s / GPU & \\
Mono3D & & 63.85 \% & 75.22 \% & 58.96 \% & 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.\\
CONV-BOX & la & 63.84 \% & 72.62 \% & 56.69 \% & 0.2 s / & \\
DA & & 63.58 \% & 79.36 \% & 56.80 \% & 0.08 s / 1 core & \\
SCANet & & 63.26 \% & 73.72 \% & 56.41 \% & 0.17 s / >8 cores & \\
AtrousDet & & 62.85 \% & 76.07 \% & 55.12 \% & 0.05 s / & \\
Faster R-CNN & & 62.81 \% & 71.41 \% & 55.44 \% & 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.\\
cas+res+soft & & 60.88 \% & 75.24 \% & 53.58 \% & 0.2 s / 4 cores & \\
SDP+CRC (ft) & & 60.87 \% & 74.31 \% & 53.95 \% & 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.\\
merge12-12 & & 60.83 \% & 75.12 \% & 53.69 \% & 0.2 s / 4 cores & \\
ELLIOT & la & 60.04 \% & 77.40 \% & 55.15 \% & 0.1 s / 1 core & \\
PP\_v1.0 & & 59.92 \% & 75.52 \% & 53.73 \% & 0.02s / 1 core & \\
AVOD-FPN & la & 59.32 \% & 68.65 \% & 55.82 \% & 0.1 s / & J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.\\
SECOND & & 58.94 \% & 81.96 \% & 57.20 \% & 38 ms / & Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.\\
Regionlets & & 58.69 \% & 70.09 \% & 51.81 \% & 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.\\
YOLOv3.5 & & 58.23 \% & 77.33 \% & 50.68 \% & 0.05 s / GPU & \\
CFR & la & 58.19 \% & 74.83 \% & 56.15 \% & 0.06 s / 1 core & \\
cascadercnn & & 58.09 \% & 75.56 \% & 50.19 \% & 0.36 s / 4 cores & \\
Complexer-YOLO & la & 57.53 \% & 65.82 \% & 57.47 \% & 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.\\
FRCNN+Or & & 57.37 \% & 70.05 \% & 51.00 \% & 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.\\
bin & & 57.13 \% & 63.05 \% & 50.64 \% & 15ms s / GPU & \\
cas\_retina & & 56.46 \% & 72.52 \% & 52.63 \% & 0.2 s / 4 cores & \\
AVOD & la & 56.01 \% & 65.72 \% & 48.89 \% & 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.\\
cas\_retina\_1\_13 & & 55.81 \% & 71.59 \% & 52.16 \% & 0.03 s / 4 cores & \\
Multi-task DG & & 55.38 \% & 73.83 \% & 47.82 \% & 0.06 s / GPU & \\
ReSqueeze & & 54.93 \% & 68.34 \% & 49.19 \% & 0.03 s / GPU & \\
anm & & 50.54 \% & 67.40 \% & 45.22 \% & 3 s / 1 core & \\
ZKNet & & 50.24 \% & 66.44 \% & 44.19 \% & 0.01 s / GPU & \\
LPN & & 50.02 \% & 65.33 \% & 44.85 \% & 0.2 s / GPU & \\
NEUAV & & 49.75 \% & 68.20 \% & 43.77 \% & 0.06 s / GPU & \\
yolo800 & & 49.15 \% & 64.64 \% & 43.58 \% & 0.13 s / 4 cores & \\
BirdNet & la & 49.04 \% & 64.88 \% & 46.61 \% & 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.\\
mylsi-faster-rcnn & & 48.85 \% & 67.93 \% & 43.77 \% & 0.3 s / 1 core & \\
fasterrcnn & & 48.81 \% & 64.40 \% & 42.74 \% & 0.2 s / 4 cores & \\
X\_MD & & 48.07 \% & 63.46 \% & 40.76 \% & 0.2 s / 1 core & \\
detectron & & 48.06 \% & 64.73 \% & 40.75 \% & 0.01 s / 1 core & \\
RFCN & & 47.61 \% & 62.17 \% & 43.74 \% & 0.2 s / 4 cores & \\
CLF3D & la & 47.53 \% & 65.31 \% & 40.23 \% & 0.13 s / GPU & \\
NM & & 47.20 \% & 60.64 \% & 42.96 \% & 0.01 s / GPU & \\
RFCN\_RFB & & 45.36 \% & 59.49 \% & 41.63 \% & 0.2 s / 4 cores & \\
cascade\_gw & & 45.00 \% & 63.14 \% & 38.81 \% & 0.2 s / 4 cores & \\
centernet & & 44.50 \% & 59.28 \% & 39.75 \% & 0.01 s / GPU & \\
myfaster-rcnn-v1.5 & & 44.32 \% & 59.99 \% & 39.88 \% & 0.1 s / 1 core & \\
FD2 & & 44.29 \% & 62.32 \% & 40.65 \% & 0.01 s / GPU & \\
SeRC & & 44.28 \% & 55.81 \% & 38.50 \% & 0.5 s / 1 core & \\
Cmerge & & 43.85 \% & 61.60 \% & 42.60 \% & 0.2 s / 4 cores & \\
Int-YOLO & & 43.30 \% & 52.88 \% & 36.57 \% & 0.03 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
MTDP & & 43.08 \% & 54.53 \% & 38.79 \% & 0.15 s / GPU & \\
GNN & & 42.65 \% & 59.43 \% & 37.72 \% & 0.2 s / 1 core & \\
MV-RGBD-RF & la & 42.61 \% & 51.46 \% & 37.42 \% & 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.\\
YOLOv3+d & & 42.60 \% & 59.08 \% & 40.77 \% & 0.04 s / GPU & \\
Shift R-CNN (mono) & & 42.30 \% & 65.56 \% & 41.40 \% & 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.\\
M3D-RPN & & 41.12 \% & 63.69 \% & 39.95 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
myfaster-rcnn & & 38.72 \% & 54.28 \% & 34.63 \% & 0.01 s / 1 core & \\
SS3D & & 37.90 \% & 53.79 \% & 35.12 \% & 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.\\
pAUCEnsT & & 37.88 \% & 52.28 \% & 33.38 \% & 60 s / 1 core & S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.\\
Retinanet100 & & 37.54 \% & 46.39 \% & 30.82 \% & 0.2 s / 4 cores & \\
TopNet-Retina & la & 35.20 \% & 50.28 \% & 34.11 \% & 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 & & 34.39 \% & 48.21 \% & 29.30 \% & 0.5 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
softyolo & & 31.30 \% & 45.16 \% & 27.38 \% & 0.16 s / 4 cores & \\
Vote3D & la & 31.24 \% & 41.45 \% & 28.60 \% & 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.\\
DPM-VOC+VP & & 31.16 \% & 43.65 \% & 28.29 \% & 8 s / 1 core & B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.\\
LSVM-MDPM-us & & 30.81 \% & 40.31 \% & 28.17 \% & 10 s / 4 cores & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.\\
a & & 30.10 \% & 44.38 \% & 29.08 \% & 0.35 s / 1 core & \\
100Frcnn & & 29.95 \% & 44.60 \% & 27.70 \% & 2 s / 4 cores & \\
LSVM-MDPM-sv & & 29.24 \% & 37.71 \% & 27.52 \% & 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 & 29.04 \% & 43.28 \% & 26.20 \% & 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.\\
R-CNN\_VGG & & 28.79 \% & 37.71 \% & 25.82 \% & 10 s / GPU & \\
rpn & & 28.65 \% & 37.40 \% & 23.50 \% & 0.01 s / 1 core & \\
Lidar\_ROI+Yolo(UJS) & & 27.21 \% & 39.41 \% & 26.12 \% & 0.1 s / 1 core & \\
mBoW & la & 21.62 \% & 28.19 \% & 20.93 \% & 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.\\
DT3D & & 20.65 \% & 31.29 \% & 20.73 \% & 0,21s / GPU & \\
mymask-rcnn & & 19.58 \% & 23.22 \% & 18.87 \% & 0.3 s / 1 core & \\
TopNet-HighRes & la & 19.15 \% & 29.34 \% & 19.69 \% & 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.\\
SA\_3D & & 18.51 \% & 22.79 \% & 15.33 \% & 0.3 s / GPU & \\
KD53-20 & & 17.71 \% & 23.15 \% & 17.30 \% & 0.19 s / 4 cores & \\
RT3DStereo & st & 17.66 \% & 23.73 \% & 11.94 \% & 0.08 s / GPU & \\
TopNet-UncEst & la & 16.21 \% & 19.18 \% & 15.99 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
YOLOv2 & & 4.55 \% & 4.55 \% & 4.55 \% & 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 & 1.01 \% & 0.04 \% & 1.01 \% & 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.\\
softretina & & 0.44 \% & 0.29 \% & 0.22 \% & 0.16 s / 4 cores & \\
JSyolo & & 0.22 \% & 0.22 \% & 0.22 \% & 0.16 s / 4 cores &
\end{tabular}