\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
FichaDL & & 81.73 \% & 88.27 \% & 75.29 \% & 0.1 s / GPU & \\
Alibaba-CityBrain & & 80.90 \% & 88.13 \% & 74.08 \% & 1.5 s / GPU & \\
ExtAtt & & 79.63 \% & 87.95 \% & 74.78 \% & 1.2 s / GPU & \\
DH-ARI & & 78.29 \% & 87.43 \% & 69.91 \% & 3.6 s / GPU & \\
EM-FPS & & 77.61 \% & 84.93 \% & 72.52 \% & 0.15 s / GPU & \\
F-PointNet & la & 77.25 \% & 87.81 \% & 74.46 \% & 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.\\
TuSimple & & 77.04 \% & 86.78 \% & 72.40 \% & 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.\\
THICV-YDM & & 76.91 \% & 87.27 \% & 69.02 \% & 0.06 s / GPU & \\
Argus\_detection\_v1 & & 75.51 \% & 83.49 \% & 71.24 \% & 0.25 s / GPU & \\
RRC & & 75.33 \% & 84.16 \% & 70.39 \% & 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.\\
VCTNet & & 75.22 \% & 85.49 \% & 71.55 \% & 0.02 s / GPU & \\
MHN & & 74.60 \% & 85.81 \% & 68.94 \% & 0.39 s / GPU & J. Cao, Y. Pang and X. Li: Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection. arXiv:1804.00872 2018.\\
Aston-EAS & & 74.52 \% & 85.12 \% & 69.35 \% & 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.\\
ECP Faster R-CNN & & 74.27 \% & 84.12 \% & 70.06 \% & 0.25 s / GPU & M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.\\
SJTU-HW & & 74.24 \% & 85.42 \% & 69.34 \% & 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.\\
CLA & & 74.03 \% & 84.26 \% & 68.45 \% & 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.\\
Multi-3D & la & 74.00 \% & 84.01 \% & 68.74 \% & 0.15 s / 1 core & \\
ECV-NET & & 73.74 \% & 84.58 \% & 66.35 \% & 0.4 s / GPU & \\
BOE\_IOT\_AIBD & & 73.73 \% & 84.67 \% & 68.71 \% & 0.8 s / GPU & \\
MS-CNN & & 73.62 \% & 83.70 \% & 68.28 \% & 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.\\
SAITv1 & & 72.61 \% & 84.79 \% & 67.94 \% & 0.15 s / GPU & \\
F-ConvNet & la & 72.37 \% & 79.98 \% & 66.61 \% & 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.\\
Sogo\_MM & & 71.84 \% & 83.45 \% & 67.00 \% & 1.5 s / GPU & \\
GN & & 71.55 \% & 80.73 \% & 64.82 \% & 1 s / GPU & S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.\\
SubCNN & & 71.34 \% & 83.17 \% & 66.36 \% & 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.\\
VMVS & la & 70.89 \% & 81.11 \% & 67.23 \% & 0.25 s / GPU & J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.\\
IVA & & 70.63 \% & 83.03 \% & 64.68 \% & 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.\\
SDP+RPN & & 70.20 \% & 79.98 \% & 64.84 \% & 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.\\
MM-MRFC & fl la & 69.96 \% & 82.37 \% & 64.76 \% & 0.05 s / GPU & A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.\\
TridentNet & & 69.58 \% & 81.27 \% & 64.18 \% & 0.2 s / GPU & \\
MDC & la & 69.58 \% & 86.37 \% & 68.44 \% & 0.17 s / GPU & \\
MonoPSR & & 68.91 \% & 85.93 \% & 60.83 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
YOLOv3.5 & & 68.33 \% & 79.61 \% & 60.85 \% & 0.05 s / GPU & \\
HBA-RCNN & & 68.26 \% & 77.76 \% & 62.86 \% & 0.4 s / 1 core & \\
DA & & 67.89 \% & 79.91 \% & 64.83 \% & 0.08 s / 1 core & \\
3DOP & st & 67.46 \% & 82.36 \% & 64.71 \% & 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.\\
DeepStereoOP & & 67.32 \% & 82.50 \% & 65.14 \% & 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.\\
sensekitti & & 67.28 \% & 80.12 \% & 62.25 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
ODES & & 67.25 \% & 77.95 \% & 62.28 \% & 0.02 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
Mono3D & & 66.66 \% & 77.30 \% & 63.44 \% & 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.\\
Faster R-CNN & & 65.91 \% & 78.35 \% & 61.19 \% & 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.\\
AtrousDet & & 65.18 \% & 77.19 \% & 58.14 \% & 0.05 s / & \\
SDP+CRC (ft) & & 64.25 \% & 77.81 \% & 59.31 \% & 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.\\
PCN & & 63.48 \% & 77.88 \% & 58.59 \% & 0.6 s / & \\
Pose-RCNN & & 63.38 \% & 77.69 \% & 57.42 \% & 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.\\
CFM & & 63.26 \% & 74.21 \% & 56.44 \% & & Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.\\
IPOD & & 63.07 \% & 73.28 \% & 56.71 \% & 0.2 s / GPU & \\
ALV303 & & 61.77 \% & 69.13 \% & 54.54 \% & 0.2 s / GPU & \\
RPN+BF & & 61.29 \% & 75.58 \% & 56.08 \% & 0.6 s / GPU & L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.\\
ReSqueeze & & 61.25 \% & 72.78 \% & 57.43 \% & 0.03 s / GPU & \\
Regionlets & & 61.16 \% & 72.96 \% & 55.22 \% & 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.\\
merge12-12 & & 60.66 \% & 78.15 \% & 58.67 \% & 0.2 s / 4 cores & \\
cascadercnn & & 60.64 \% & 77.88 \% & 52.69 \% & 0.36 s / 4 cores & \\
cas+res+soft & & 60.60 \% & 77.96 \% & 58.56 \% & 0.2 s / 4 cores & \\
bin & & 60.54 \% & 70.13 \% & 56.55 \% & 15ms s / GPU & \\
cas\_retina & & 60.30 \% & 77.71 \% & 58.34 \% & 0.2 s / 4 cores & \\
A-VoxelNet & & 59.98 \% & 69.26 \% & 58.48 \% & 0.029 s / GPU & \\
cas\_retina\_1\_13 & & 59.87 \% & 77.11 \% & 57.81 \% & 0.03 s / 4 cores & \\
anm & & 59.21 \% & 75.51 \% & 56.49 \% & 3 s / 1 core & \\
CompACT-Deep & & 58.73 \% & 69.70 \% & 52.69 \% & 1 s / 1 core & Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.\\
DeepParts & & 58.68 \% & 70.46 \% & 52.73 \% & ~1 s / GPU & Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.\\
AVOD-FPN & la & 58.42 \% & 67.32 \% & 57.44 \% & 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.\\
ARPNET & & 58.23 \% & 68.51 \% & 53.34 \% & 0.08 s / GPU & \\
LPN & & 58.18 \% & 70.54 \% & 54.18 \% & 0.2 s / GPU & \\
SA\_3D & & 57.85 \% & 68.55 \% & 50.45 \% & 0.3 s / GPU & \\
mymask-rcnn & & 57.79 \% & 73.18 \% & 55.11 \% & 0.3 s / 1 core & \\
Tencent\_ADlab\_Lidar & la & 57.23 \% & 66.08 \% & 55.10 \% & 0.1 s / GPU & \\
FilteredICF & & 57.12 \% & 69.05 \% & 51.46 \% & ~ 2 s / >8 cores & S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.\\
ZKNet & & 57.11 \% & 70.20 \% & 52.15 \% & 0.01 s / GPU & \\
DSS & & 57.10 \% & 64.61 \% & 54.99 \% & 0.03 s / GPU & \\
RFCN & & 56.91 \% & 71.17 \% & 50.06 \% & 0.2 s / 4 cores & \\
FRCNN+Or & & 56.78 \% & 71.18 \% & 52.86 \% & 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.\\
FD2 & & 56.68 \% & 71.09 \% & 51.65 \% & 0.01 s / GPU & \\
MV-RGBD-RF & la & 56.59 \% & 73.05 \% & 49.63 \% & 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.\\
CHTTL MMF & & 56.01 \% & 73.22 \% & 50.26 \% & 0.1 s / GPU & \\
RFCN\_RFB & & 55.86 \% & 69.32 \% & 49.18 \% & 0.2 s / 4 cores & \\
SECOND & & 55.74 \% & 65.73 \% & 49.08 \% & 38 ms / & Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.\\
PointPillars & la & 55.68 \% & 64.66 \% & 53.93 \% & 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.\\
yolo800 & & 55.49 \% & 71.11 \% & 53.92 \% & 0.13 s / 4 cores & \\
STD & & 55.44 \% & 69.09 \% & 53.46 \% & 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.\\
Vote3Deep & la & 55.38 \% & 67.94 \% & 52.62 \% & 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.\\
CONV-BOX & la & 55.23 \% & 63.98 \% & 54.18 \% & 0.2 s / & \\
epBRM & la & 54.62 \% & 62.46 \% & 53.30 \% & 0.10 s / 1 core & \\
TAFT & & 54.59 \% & 67.07 \% & 48.48 \% & 0.2 s / 1 core & J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.\\
pAUCEnsT & & 54.58 \% & 66.11 \% & 48.49 \% & 60 s / 1 core & S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.\\
SCANet & & 54.02 \% & 64.57 \% & 48.05 \% & 0.17 s / >8 cores & \\
NM & & 53.98 \% & 69.06 \% & 50.76 \% & 0.01 s / GPU & \\
fasterrcnn & & 53.80 \% & 69.00 \% & 51.35 \% & 0.2 s / 4 cores & \\
NEUAV & & 53.75 \% & 68.86 \% & 48.04 \% & 0.06 s / GPU & \\
PDV2 & & 53.74 \% & 65.71 \% & 49.47 \% & 3.7 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.\\
PP\_v1.0 & & 53.59 \% & 62.16 \% & 51.51 \% & 0.02s / 1 core & \\
Shift R-CNN (mono) & & 53.33 \% & 71.11 \% & 44.71 \% & 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.\\
MTDP & & 52.97 \% & 66.97 \% & 47.64 \% & 0.15 s / GPU & \\
FOFNet & la & 52.94 \% & 62.34 \% & 47.54 \% & 0.04 s / GPU & \\
detectron & & 52.42 \% & 69.89 \% & 51.70 \% & 0.01 s / 1 core & \\
centernet & & 51.75 \% & 68.65 \% & 46.89 \% & 0.01 s / GPU & \\
Multi-task DG & & 51.34 \% & 68.07 \% & 50.39 \% & 0.06 s / GPU & \\
cascade\_gw & & 51.10 \% & 67.58 \% & 43.34 \% & 0.2 s / 4 cores & \\
YOLOv3+d & & 51.03 \% & 67.23 \% & 48.87 \% & 0.04 s / GPU & \\
ACFD & la & 50.91 \% & 61.59 \% & 45.51 \% & 0.2 s / 4 cores & M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.\\
MMLab-PointRCNN & la & 50.88 \% & 59.05 \% & 48.46 \% & 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.\\
myfaster-rcnn-v1.5 & & 50.62 \% & 66.91 \% & 48.16 \% & 0.1 s / 1 core & \\
CLF3D & la & 50.25 \% & 66.10 \% & 48.66 \% & 0.13 s / GPU & \\
R-CNN & & 50.20 \% & 62.05 \% & 44.85 \% & 4 s / GPU & J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.\\
SS3D & & 49.81 \% & 59.46 \% & 42.44 \% & 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.\\
SeRC & & 49.81 \% & 65.31 \% & 42.40 \% & 0.5 s / 1 core & \\
Resnet101Faster rcnn & & 49.64 \% & 64.97 \% & 48.47 \% & 1 s / 1 core & \\
mylsi-faster-rcnn & & 49.31 \% & 64.64 \% & 45.96 \% & 0.3 s / 1 core & \\
Int-YOLO & & 48.93 \% & 64.40 \% & 48.02 \% & 0.03 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
ELLIOT & la & 48.26 \% & 59.05 \% & 45.52 \% & 0.1 s / 1 core & \\
CFR & la & 48.16 \% & 63.07 \% & 47.51 \% & 0.06 s / 1 core & \\
ACF & & 47.29 \% & 60.11 \% & 42.90 \% & 1 s / 1 core & P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.\\
Fusion-DPM & la & 46.67 \% & 59.38 \% & 42.05 \% & ~ 30 s / 1 core & C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.\\
ACF-MR & & 46.23 \% & 58.85 \% & 42.10 \% & 0.6 s / 1 core & R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.\\
AB3DMOT & la on & 46.06 \% & 55.63 \% & 42.60 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
M3D-RPN & & 46.02 \% & 59.82 \% & 39.31 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
HA-SSVM & & 45.51 \% & 58.91 \% & 41.08 \% & 21 s / 1 core & J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.\\
DPM-VOC+VP & & 44.86 \% & 59.60 \% & 40.37 \% & 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.\\
Cmerge & & 44.81 \% & 62.62 \% & 44.53 \% & 0.2 s / 4 cores & \\
ACF-SC & & 44.77 \% & 54.20 \% & 39.57 \% & & 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.\\
SquaresICF & & 44.42 \% & 57.47 \% & 40.08 \% & 1 s / GPU & R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.\\
AVOD & la & 43.49 \% & 51.64 \% & 37.79 \% & 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.\\
Retinanet100 & & 42.83 \% & 52.43 \% & 35.02 \% & 0.2 s / 4 cores & \\
GNN & & 42.56 \% & 58.22 \% & 40.53 \% & 0.2 s / 1 core & \\
SubCat & & 42.34 \% & 54.06 \% & 37.95 \% & 1.2 s / 6 cores & E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.\\
myfaster-rcnn & & 41.91 \% & 57.22 \% & 39.62 \% & 0.01 s / 1 core & \\
yolov3\_warp & & 41.07 \% & 56.07 \% & 39.08 \% & 0.5 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
softyolo & & 40.78 \% & 55.95 \% & 39.57 \% & 0.16 s / 4 cores & \\
ACF & & 40.62 \% & 49.08 \% & 36.66 \% & 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). .\\
multi-task CNN & & 40.34 \% & 51.38 \% & 34.98 \% & 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.\\
KD53-20 & & 39.90 \% & 47.15 \% & 35.32 \% & 0.19 s / 4 cores & \\
LSVM-MDPM-sv & & 39.36 \% & 51.75 \% & 35.95 \% & 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.\\
pedestrian\_cnn & & 39.07 \% & 53.60 \% & 37.91 \% & 1 s / 1 core & \\
Lidar\_ROI+Yolo(UJS) & & 38.76 \% & 47.11 \% & 32.33 \% & 0.1 s / 1 core & \\
LSVM-MDPM-us & & 38.35 \% & 50.01 \% & 34.78 \% & 10 s / 4 cores & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.\\
& & 37.45 \% & 45.89 \% & 35.08 \% & / & \\
X\_MD & & 37.38 \% & 50.17 \% & 36.49 \% & 0.2 s / 1 core & \\
anonymous & la & 36.65 \% & 49.15 \% & 36.18 \% & 0.75 s / GPU & \\
Complexer-YOLO & la & 36.10 \% & 42.63 \% & 35.57 \% & 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.\\
Vote3D & la & 35.74 \% & 44.47 \% & 33.72 \% & 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.\\
a & & 33.13 \% & 47.80 \% & 32.53 \% & 0.35 s / 1 core & \\
rpn & & 32.79 \% & 46.95 \% & 31.70 \% & 0.01 s / 1 core & \\
RT3DStereo & st & 32.01 \% & 44.54 \% & 31.50 \% & 0.08 s / GPU & \\
mBoW & la & 31.37 \% & 44.36 \% & 30.62 \% & 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.\\
BirdNet & la & 30.90 \% & 36.83 \% & 29.93 \% & 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.\\
DPM-C8B1 & st & 29.03 \% & 38.96 \% & 25.61 \% & 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.\\
100Frcnn & & 26.73 \% & 35.65 \% & 26.46 \% & 2 s / 4 cores & \\
R-CNN\_VGG & & 23.16 \% & 28.95 \% & 22.17 \% & 10 s / GPU & \\
TopNet-Retina & la & 19.67 \% & 25.17 \% & 16.33 \% & 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.\\
DT3D & & 19.19 \% & 27.02 \% & 18.98 \% & 0,21s / GPU & \\
TopNet-HighRes & la & 17.57 \% & 22.98 \% & 17.35 \% & 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.\\
YOLOv2 & & 16.19 \% & 20.80 \% & 15.43 \% & 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.\\
BIP-HETERO & & 13.38 \% & 14.85 \% & 13.25 \% & ~2 s / 1 core & A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.\\
TopNet-UncEst & la & 10.91 \% & 15.55 \% & 10.05 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
softretina & & 0.93 \% & 0.68 \% & 0.95 \% & 0.16 s / 4 cores & \\
JSyolo & & 0.44 \% & 0.35 \% & 0.45 \% & 0.16 s / 4 cores & \\
TopNet-DecayRate & la & 0.04 \% & 0.02 \% & 0.05 \% & 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.\\
SN-net & & 0.00 \% & 0.00 \% & 0.00 \% & 0.8 s / GPU &
\end{tabular}