\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
MVRA + I-FRCNN+ & & 89.93 \% & 90.60 \% & 79.78 \% & 0.18 s / GPU & \\
Deep MANTA & & 89.86 \% & 97.19 \% & 80.39 \% & 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.\\
THICV-YDM & & 89.66 \% & 90.22 \% & 79.89 \% & 0.06 s / GPU & \\
F-ConvNet & la & 89.60 \% & 90.41 \% & 80.39 \% & 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.\\
RGB3D & la & 89.60 \% & 90.74 \% & 87.98 \% & 0.39 s / GPU & \\
PointRCNN-deprecated & la & 89.55 \% & 90.76 \% & 80.76 \% & 0.1 s / GPU & \\
Patches & la & 89.48 \% & 90.73 \% & 87.18 \% & 0.15 s / GPU & \\
HRI-FusionRCNN & & 89.48 \% & 90.49 \% & 80.40 \% & 0.1 s / 1 core & \\
Patches - EMP & la & 89.43 \% & 94.61 \% & 87.81 \% & 0.5 s / GPU & \\
HRI-VoxelFPN & & 89.27 \% & 90.43 \% & 80.31 \% & 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.\\
MMLab-PointRCNN & la & 89.22 \% & 90.73 \% & 85.53 \% & 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.\\
Alibaba-AILabsX & la & 89.20 \% & 90.25 \% & 80.36 \% & 0.05 s / 1 core & \\
AB3DMOT & la on & 89.16 \% & 90.66 \% & 86.24 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
SRF & & 89.00 \% & 90.33 \% & 80.36 \% & 0.05 s / GPU & \\
MMLab-PartA^2 & la & 88.98 \% & 90.41 \% & 87.08 \% & 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.\\
SegVoxelNet & & 88.88 \% & 90.50 \% & 87.34 \% & 0.04 s / 1 core & \\
PFPN & & 88.83 \% & 90.30 \% & 79.99 \% & 0.02 s / 4 cores & \\
PointPillars & la & 88.76 \% & 90.19 \% & 86.38 \% & 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 & & 88.74 \% & 90.38 \% & 79.99 \% & 0.4 s / GPU & \\
3D IoU Loss & la & 88.72 \% & 90.08 \% & 80.06 \% & 0.08 s / GPU & D. Zhou: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.\\
Sogo\_MM & & 88.72 \% & 90.67 \% & 78.95 \% & 1.5 s / GPU & \\
MVSLN & & 88.62 \% & 90.52 \% & 80.08 \% & 0.1s s / 1 core & \\
TBA & & 88.62 \% & 90.03 \% & 86.29 \% & 0.07 s / 1 core & \\
Deep3DBox & & 88.56 \% & 90.39 \% & 77.17 \% & 1.5 s / GPU & A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.\\
PTS & la & 88.55 \% & 90.11 \% & 79.90 \% & 0.01 s / 1 core & \\
ARPNET & & 88.52 \% & 90.00 \% & 79.85 \% & 0.08 s / GPU & \\
NU-optim & & 88.51 \% & 89.82 \% & 87.12 \% & 0.04 s / GPU & \\
SubCNN & & 88.43 \% & 90.61 \% & 78.63 \% & 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.\\
FOFNet & la & 88.37 \% & 90.46 \% & 79.84 \% & 0.04 s / GPU & \\
A-VoxelNet & & 88.36 \% & 89.72 \% & 79.61 \% & 0.029 s / GPU & \\
Tencent\_ADlab\_Lidar & la & 88.33 \% & 90.05 \% & 84.80 \% & 0.1 s / GPU & \\
SECOND-V1.5 & la & 88.33 \% & 90.15 \% & 79.65 \% & 0.04 s / GPU & \\
MPNet & la & 88.27 \% & 90.27 \% & 85.06 \% & 0.02 s / GPU & \\
GPP & & 87.96 \% & 90.35 \% & 78.57 \% & 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.\\
3DBN & la & 87.95 \% & 89.93 \% & 79.32 \% & 0.13s / & X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.\\
Shift R-CNN (mono) & & 87.91 \% & 90.27 \% & 78.72 \% & 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.\\
MonoPSR & & 87.83 \% & 89.88 \% & 70.48 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
PAD & & 87.73 \% & 90.06 \% & 82.30 \% & 0.15 s / 1 core & \\
CFR & la & 87.67 \% & 90.26 \% & 79.02 \% & 0.06 s / 1 core & \\
PP\_v1.0 & & 87.61 \% & 89.97 \% & 83.02 \% & 0.02s / 1 core & \\
AVOD & la & 87.46 \% & 89.59 \% & 79.54 \% & 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.\\
SeoulRobotics-HFD & la & 87.34 \% & 89.88 \% & 79.32 \% & 0.035 s / & \\
AVOD-FPN & la & 87.13 \% & 89.95 \% & 79.74 \% & 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.\\
DFD & & 87.01 \% & 89.72 \% & 78.98 \% & 0.05 s / GPU & \\
SECA & & 86.80 \% & 89.42 \% & 78.81 \% & 0.09 s / GPU & \\
SCANet & & 86.65 \% & 89.06 \% & 78.67 \% & 0.09s / GPU & \\
DeepStereoOP & & 86.57 \% & 89.01 \% & 77.13 \% & 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.\\
SCANet & & 86.39 \% & 89.25 \% & 78.65 \% & 0.17 s / >8 cores & \\
FQNet & & 86.29 \% & 89.48 \% & 74.40 \% & 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.\\
RAR-Net & & 86.28 \% & 89.48 \% & 74.39 \% & 0.5 s / 1 core & \\
ELLIOT & la & 86.13 \% & 89.69 \% & 80.50 \% & 0.1 s / 1 core & \\
Mono3D & & 85.83 \% & 89.00 \% & 76.00 \% & 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.\\
3DOP & st & 85.81 \% & 88.56 \% & 76.21 \% & 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.\\
MBR-SSD & & 85.03 \% & 88.10 \% & 75.92 \% & 4.0 s / GPU & \\
PL V2 (SDN+GDC) & st la & 83.40 \% & 90.13 \% & 75.92 \% & 0.6 s / GPU & \\
StereoFENet & st & 83.13 \% & 88.83 \% & 76.33 \% & 0.15 s / 1 core & W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. 2019.\\
MonoFENet & & 82.05 \% & 88.86 \% & 75.63 \% & 0.15 s / 1 core & W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. 2019.\\
M3D-RPN & & 81.66 \% & 83.80 \% & 65.94 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
SECOND & & 81.31 \% & 87.84 \% & 71.95 \% & 38 ms / & Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.\\
X\_MD & & 80.28 \% & 89.53 \% & 79.14 \% & 0.2 s / 1 core & \\
FNV1\_Fusion & & 80.12 \% & 89.25 \% & 78.58 \% & 0.11 s / GPU & \\
FNV1\_RPN & & 80.10 \% & 89.27 \% & 78.66 \% & 0.12 s / 1 core & \\
SS3D & & 79.70 \% & 89.02 \% & 69.91 \% & 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 & & 79.56 \% & 89.11 \% & 78.14 \% & 1 s / GPU & \\
VSE & & 79.56 \% & 89.11 \% & 78.14 \% & 0.15 s / GPU & \\
Complexer-YOLO & la & 79.08 \% & 87.97 \% & 78.75 \% & 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 & & 78.97 \% & 88.40 \% & 76.70 \% & 0.11 s / GPU & \\
BS3D & & 78.68 \% & 89.28 \% & 68.52 \% & 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.\\
Pseudo-LiDAR V2 & st & 78.35 \% & 89.36 \% & 74.79 \% & 0.4 s / GPU & \\
FRCNN+Or & & 77.61 \% & 88.52 \% & 67.69 \% & 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.\\
Manhnet & & 77.21 \% & 85.58 \% & 60.50 \% & 26 ms / 1 core & \\
CLF3D & la & 76.50 \% & 84.35 \% & 67.12 \% & 0.13 s / GPU & \\
avodC & & 76.30 \% & 86.31 \% & 68.71 \% & 0.1 s / GPU & \\
3D FCN & la & 75.71 \% & 85.46 \% & 68.19 \% & >5 s / 1 core & B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.\\
3D-SSMFCNN & & 75.42 \% & 75.44 \% & 67.27 \% & 0.1 s / GPU & L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.\\
Pose-RCNN & & 75.35 \% & 88.78 \% & 61.47 \% & 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.\\
GS3D & & 75.16 \% & 83.52 \% & 59.59 \% & 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.\\
3DVP & & 74.59 \% & 81.02 \% & 64.11 \% & 40 s / 8 cores & Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.\\
SubCat & & 74.42 \% & 80.74 \% & 58.83 \% & 0.7 s / 6 cores & E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.\\
BdCost+DA+MS & & 73.15 \% & 82.12 \% & 58.29 \% & TBD s / 4 cores & \\
a & & 69.87 \% & 86.40 \% & 60.71 \% & 0.35 s / 1 core & \\
ROI-10D & & 67.85 \% & 74.24 \% & 59.28 \% & 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.\\
MF3D & & 67.68 \% & 87.79 \% & 57.57 \% & 0.03 s / GPU & \\
multi-task CNN & & 66.19 \% & 76.69 \% & 58.11 \% & 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.\\
BdCost48LDCF & & 66.01 \% & 77.10 \% & 50.35 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
BdCost48-25C & & 65.25 \% & 77.59 \% & 50.68 \% & 4 s / 1 core & \\
OC-DPM & & 64.88 \% & 74.66 \% & 52.24 \% & 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.\\
3DVSSD & & 64.72 \% & 77.22 \% & 57.56 \% & 0.06 s / 1 core & \\
DPM-VOC+VP & & 63.27 \% & 77.51 \% & 47.57 \% & 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.\\
AOG-View & & 62.25 \% & 77.37 \% & 50.44 \% & 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.\\
monoref3d & & 57.65 \% & 73.74 \% & 48.91 \% & 0.1 s / 1 core & \\
ref3D & & 57.65 \% & 73.74 \% & 48.91 \% & 0.1 s / 1 core & \\
SAMME48LDCF & & 57.49 \% & 75.12 \% & 46.64 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
LSVM-MDPM-sv & & 56.69 \% & 70.86 \% & 45.91 \% & 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 & & 56.54 \% & 74.41 \% & 47.15 \% & 0.1 s / 1 core & \\
RCN-resnet101 & & 53.93 \% & 56.36 \% & 48.32 \% & 0.3 s / GPU & \\
SAG-Net & & 53.29 \% & 57.92 \% & 47.73 \% & 0.2 s / GPU & \\
VeloFCN & la & 52.70 \% & 70.21 \% & 46.11 \% & 1 s / GPU & B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .\\
Mono3D\_PLiDAR & & 50.76 \% & 76.57 \% & 43.30 \% & 0.1 s / & X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.\\
DPM-C8B1 & st & 50.32 \% & 59.53 \% & 39.22 \% & 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.\\
VAT-Net & & 49.91 \% & 52.74 \% & 45.16 \% & 1 s / GPU & \\
InNet & & 49.55 \% & 52.32 \% & 44.79 \% & 0.16 s / GPU & \\
ODES & & 48.06 \% & 46.22 \% & 42.43 \% & 0.02 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
ReSqueeze & & 45.40 \% & 47.38 \% & 41.68 \% & 0.03 s / GPU & \\
sensekitti & & 44.56 \% & 47.06 \% & 41.50 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
VCTNet & & 43.41 \% & 46.53 \% & 39.42 \% & 0.02 s / GPU & \\
Resnet101Faster rcnn & & 42.62 \% & 49.41 \% & 38.21 \% & 1 s / 1 core & \\
FD2 & & 39.44 \% & 47.56 \% & 35.20 \% & 0.01 s / GPU & \\
bin & & 37.23 \% & 41.94 \% & 32.65 \% & 15ms s / GPU & \\
IPOD & & 37.01 \% & 36.95 \% & 36.96 \% & 0.2 s / GPU & \\
cas+res+soft & & 36.06 \% & 36.91 \% & 31.97 \% & 0.2 s / 4 cores & \\
cas\_retina & & 36.05 \% & 38.05 \% & 31.81 \% & 0.2 s / 4 cores & \\
merge12-12 & & 36.02 \% & 36.92 \% & 31.88 \% & 0.2 s / 4 cores & \\
BirdNet & la & 35.81 \% & 50.85 \% & 34.90 \% & 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.\\
AtrousDet & & 35.80 \% & 36.56 \% & 32.32 \% & 0.05 s / & \\
cas\_retina\_1\_13 & & 35.68 \% & 38.39 \% & 31.89 \% & 0.03 s / 4 cores & \\
cascadercnn & & 35.01 \% & 34.13 \% & 28.55 \% & 0.36 s / 4 cores & \\
centernet & & 34.18 \% & 37.83 \% & 30.75 \% & 0.01 s / GPU & \\
IoU\_DCRCNN & & 33.60 \% & 38.00 \% & 31.21 \% & 0.66 s / GPU & \\
cascade\_gw & & 33.49 \% & 32.78 \% & 28.18 \% & 0.2 s / 4 cores & \\
Cmerge & & 32.95 \% & 36.87 \% & 29.14 \% & 0.2 s / 4 cores & \\
ZKNet & & 32.91 \% & 37.16 \% & 28.94 \% & 0.01 s / GPU & \\
softretina & & 32.90 \% & 37.63 \% & 28.73 \% & 0.16 s / 4 cores & \\
Fast-SSD & & 32.90 \% & 40.88 \% & 29.21 \% & 0.06 s / & \\
Retinanet100 & & 32.87 \% & 37.54 \% & 28.69 \% & 0.2 s / 4 cores & \\
LPN & & 32.41 \% & 33.97 \% & 29.15 \% & 0.2 s / GPU & \\
RTL3D & & 32.26 \% & 32.32 \% & 29.71 \% & 0.02 s / GPU & \\
NM & & 32.25 \% & 36.53 \% & 28.20 \% & 0.01 s / GPU & \\
Multi-task DG & & 32.16 \% & 37.01 \% & 29.18 \% & 0.06 s / GPU & \\
SceneNet & & 32.02 \% & 36.62 \% & 28.46 \% & 0.03 s / GPU & \\
detectron & & 31.71 \% & 35.58 \% & 28.18 \% & 0.01 s / 1 core & \\
FailNet-Fusion & la & 31.58 \% & 29.02 \% & 30.32 \% & 0.1 s / 1 core & \\
RFCN\_RFB & & 31.47 \% & 33.70 \% & 27.02 \% & 0.2 s / 4 cores & \\
FailNet-LIDAR & la & 31.05 \% & 29.38 \% & 29.66 \% & 0.1 s / 1 core & \\
MTDP & & 31.04 \% & 34.12 \% & 27.50 \% & 0.15 s / GPU & \\
AOG & & 30.81 \% & 34.05 \% & 24.86 \% & 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.\\
yolo800 & & 30.47 \% & 31.53 \% & 27.01 \% & 0.13 s / 4 cores & \\
VoxelNet(Unofficial) & & 30.29 \% & 33.54 \% & 27.36 \% & 0.5 s / GPU & \\
RFCN & & 30.29 \% & 33.30 \% & 25.44 \% & 0.2 s / 4 cores & \\
fasterrcnn & & 28.13 \% & 29.83 \% & 24.76 \% & 0.2 s / 4 cores & \\
E-VoxelNet & & 28.03 \% & 30.85 \% & 25.39 \% & 0.1 s / GPU & \\
SubCat48LDCF & & 26.78 \% & 34.43 \% & 19.46 \% & 0.5 s / 8 cores & A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.\\
RFBnet & & 26.39 \% & 32.45 \% & 23.97 \% & 0.2 s / 4 cores & \\
Lidar\_ROI+Yolo(UJS) & & 25.40 \% & 28.93 \% & 22.51 \% & 0.1 s / 1 core & \\
CSoR & la & 25.38 \% & 34.43 \% & 21.95 \% & 3.5 s / 4 cores & L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.\\
100Frcnn & & 25.26 \% & 34.82 \% & 21.73 \% & 2 s / 4 cores & \\
RT3DStereo & st & 22.24 \% & 25.54 \% & 19.33 \% & 0.08 s / GPU & \\
DLMB & la on & 21.69 \% & 25.31 \% & 18.75 \% & 0.03 s / 8 cores & \\
FailNet-Mono & & 20.02 \% & 24.41 \% & 17.85 \% & 0.1 s / 1 core & \\
RT3D & la & 18.98 \% & 24.23 \% & 20.56 \% & 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.\\
softyolo & & 18.22 \% & 25.50 \% & 15.97 \% & 0.16 s / 4 cores & \\
rpn & & 17.04 \% & 25.68 \% & 13.96 \% & 0.01 s / 1 core & \\
Licar & la & 15.58 \% & 18.24 \% & 16.15 \% & 0.09 s / GPU & \\
KD53-20 & & 14.27 \% & 20.79 \% & 12.61 \% & 0.19 s / 4 cores & \\
DLnet & & 8.48 \% & 9.09 \% & 7.39 \% & 0.3 s / 4 cores & \\
FCPP & & 0.19 \% & 0.02 \% & 0.20 \% & 0.02 s / 1 core & \\
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}