\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
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.\\
RaC & & 89.25 \% & 89.98 \% & 80.07 \% & 1s s / GPU & \\
uickitti & & 88.72 \% & 90.67 \% & 78.95 \% & 1.5 s / GPU & \\
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.\\
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.\\
DJML & & 88.27 \% & 89.90 \% & 78.29 \% & 2.4 s / GPU & \\
HM3D & & 87.29 \% & 89.41 \% & 77.08 \% & 0.35 s / 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.\\
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.\\
FRCNN+Or & & 77.80 \% & 88.93 \% & 67.87 \% & 0.1 s / GPU & 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.\\
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.\\
Pose-RCNN & & 75.35 \% & 88.78 \% & 61.47 \% & 2 s / >8 cores & \\
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.\\
BdCost48LDCF & & 66.00 \% & 77.10 \% & 50.35 \% & 5 s / 1 core & \\
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.\\
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.\\
NMRDO & & 59.55 \% & 77.38 \% & 51.91 \% & 0.1 s / GPU & \\
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.\\
GVPL & & 54.32 \% & 61.33 \% & 46.24 \% & 1 s / 8 cores & \\
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 .\\
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.\\
HSR2 & & 45.46 \% & 47.03 \% & 40.60 \% & 0.15 s / 1 core & \\
Allspark & & 45.30 \% & 47.19 \% & 39.95 \% & 0.7 s / GPU & \\
WRInception & & 45.07 \% & 47.05 \% & 40.52 \% & 0.06 s / GPU & \\
sensekitti & & 44.56 \% & 47.06 \% & 41.50 \% & 4.5 s / GPU & \\
HM\_SSD\_RCNN & & 44.44 \% & 46.40 \% & 39.98 \% & 0.15 s / 1 core & \\
VCTNet & & 42.05 \% & 45.66 \% & 37.49 \% & 0.18 s / GPU & \\
DuEye & & 40.99 \% & 39.62 \% & 38.97 \% & 4 s / GPU & \\
FD & & 40.40 \% & 46.30 \% & 34.01 \% & 0.01 s / GPU & \\
FD2 & & 39.44 \% & 47.56 \% & 35.20 \% & 0.01 s / GPU & \\
CPCD & & 38.93 \% & 36.51 \% & 34.15 \% & 3 s / 1 core & \\
Re-3DOP & & 38.35 \% & 36.67 \% & 33.74 \% & 3 s / 1 core & \\
UI & & 38.14 \% & 39.13 \% & 31.41 \% & 0.4 s / GPU & \\
Direwolf & & 36.92 \% & 37.35 \% & 33.37 \% & 0.5 s / GPU & \\
ZGC & & 36.69 \% & 45.54 \% & 32.23 \% & 0.12 s / 1 core & \\
QHY & & 36.31 \% & 46.05 \% & 31.72 \% & 0.1 s / 1 core & \\
SYVO & & 36.17 \% & 36.85 \% & 29.14 \% & 0.13 s / GPU & \\
HL & & 35.06 \% & 41.56 \% & 27.94 \% & 0.16 s / 1 core & \\
ANM & & 34.79 \% & 35.30 \% & 31.75 \% & 0.05 s / GPU & \\
ANM & & 32.72 \% & 34.26 \% & 28.06 \% & 0.05 s / GPU & \\
LPN & & 32.41 \% & 33.97 \% & 29.15 \% & 0.2 s / GPU & \\
SceneNet & & 32.02 \% & 36.62 \% & 28.46 \% & 0.03 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.\\
FCNN & & 28.85 \% & 35.35 \% & 25.25 \% & 0.1 s / 1 core & \\
YOLOv2 & & 26.98 \% & 34.61 \% & 23.42 \% & 0.03 s / GPU & \\
NMF-CNN & & 26.11 \% & 32.01 \% & 19.11 \% & 0.1 s / GPU & \\
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.\\
SubCat48LDCF & & 24.27 \% & 28.56 \% & 19.02 \% & 5 s / 1 core & \\
frd & & 22.18 \% & 27.99 \% & 19.59 \% & 2 s / 1 core &
\end{tabular}