\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
uickitti & & 63.59 \% & 70.70 \% & 56.15 \% & 1.5 s / GPU & \\
SubCNN & & 63.41 \% & 71.39 \% & 56.34 \% & 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.\\
Pose-RCNN & & 62.25 \% & 74.85 \% & 55.09 \% & 2 s / >8 cores & \\
DJML & & 59.76 \% & 69.26 \% & 52.94 \% & 2.4 s / GPU & \\
Deep3DBox & & 59.37 \% & 68.58 \% & 51.97 \% & 1.5 s / GPU & A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.\\
3DOP & st & 58.59 \% & 71.95 \% & 52.35 \% & 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 & & 55.62 \% & 67.49 \% & 48.85 \% & 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.\\
HM3D & & 55.12 \% & 67.32 \% & 48.86 \% & 0.35 s / GPU & \\
Mono3D & & 53.11 \% & 65.74 \% & 48.87 \% & 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.\\
FRCNN+Or & & 51.47 \% & 64.90 \% & 46.48 \% & 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.\\
VCTNet & & 43.79 \% & 48.73 \% & 38.05 \% & 0.18 s / GPU & \\
Allspark & & 42.27 \% & 48.01 \% & 36.27 \% & 0.7 s / GPU & \\
sensekitti & & 42.12 \% & 46.65 \% & 36.66 \% & 4.5 s / GPU & \\
maxFtr+ROI & & 38.29 \% & 42.96 \% & 34.28 \% & 0.25 s / 4 cores & W. Tian and M. Lauer: Detection and Orientation Estimation for Cyclists by Max Pooled Features. International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) 2017.\\
HSR2 & & 36.82 \% & 42.76 \% & 32.33 \% & 0.15 s / 1 core & \\
HM\_SSD\_RCNN & & 35.64 \% & 44.70 \% & 29.65 \% & 0.15 s / 1 core & \\
WRInception & & 34.02 \% & 41.88 \% & 29.37 \% & 0.06 s / GPU & \\
Re-3DOP & & 29.69 \% & 31.49 \% & 27.42 \% & 3 s / 1 core & \\
LPN & & 27.01 \% & 32.96 \% & 25.01 \% & 0.2 s / GPU & \\
ZGC & & 26.53 \% & 36.55 \% & 22.25 \% & 0.12 s / 1 core & \\
FD2 & & 24.65 \% & 35.58 \% & 21.97 \% & 0.01 s / GPU & \\
ANM & & 24.05 \% & 31.01 \% & 21.12 \% & 0.05 s / GPU & \\
QHY & & 23.90 \% & 33.67 \% & 22.75 \% & 0.1 s / 1 core & \\
NMRDO & & 23.53 \% & 32.68 \% & 19.81 \% & 0.1 s / GPU & \\
DPM-VOC+VP & & 23.22 \% & 31.24 \% & 21.62 \% & 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.\\
ACF\_M & & 23.14 \% & 28.89 \% & 22.28 \% & 0.1 s / 1 core & \\
LSVM-MDPM-sv & & 23.14 \% & 28.89 \% & 22.28 \% & 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.\\
ANM & & 22.82 \% & 30.83 \% & 20.15 \% & 0.05 s / GPU & \\
YOLOv2 & & 22.36 \% & 28.97 \% & 19.45 \% & 0.03 s / GPU & \\
FD & & 21.60 \% & 30.76 \% & 18.56 \% & 0.01 s / GPU & \\
HL & & 21.41 \% & 30.22 \% & 17.64 \% & 0.16 s / 1 core & \\
DPM-C8B1 & st & 19.25 \% & 27.16 \% & 17.95 \% & 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.\\
NMF-CNN & & 16.78 \% & 22.03 \% & 15.10 \% & 0.1 s / GPU &
\end{tabular}