\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
Deep MANTA \cite{deepmantacvpr17} & 89.86 \% & 97.19 \% & 80.39 \% & 0.7 s / GPU \\
Deep3DBox \cite{MousavianCVPR2017} & 88.56 \% & 90.39 \% & 77.17 \% & 1.5 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 88.43 \% & 90.61 \% & 78.63 \% & 2 s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 86.57 \% & 89.01 \% & 77.13 \% & 3.4 s / GPU \\
Mono3D \cite{Chen2016CVPR} & 85.83 \% & 89.00 \% & 76.00 \% & 4.2 s / GPU \\
3DOP \cite{Chen2015NIPS} & 85.81 \% & 88.56 \% & 76.21 \% & 3s / GPU \\
FRCNN+Or \cite{GuindelICVES} & 77.80 \% & 88.93 \% & 67.87 \% & 0.1 s / GPU \\
3D FCN \cite{li2017iros} & 75.71 \% & 85.46 \% & 68.19 \% & >5 s / 1 core \\
3DVP \cite{Xiang2015CVPR} & 74.59 \% & 81.02 \% & 64.11 \% & 40 s / 8 cores \\
SubCat \cite{OhnBar2015TITS} & 74.42 \% & 80.74 \% & 58.83 \% & 0.7 s / 6 cores \\
OC-DPM \cite{Pepik2013CVPR} & 64.88 \% & 74.66 \% & 52.24 \% & 10 s / 8 cores \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 63.27 \% & 77.51 \% & 47.57 \% & 8 s / 1 core \\
AOG-View \cite{Li2014ECCV} & 62.25 \% & 77.37 \% & 50.44 \% & 3 s / 1 core \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 56.69 \% & 70.86 \% & 45.91 \% & 10 s / 4 cores \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 50.32 \% & 59.53 \% & 39.22 \% & 15 s / 4 cores \\
AOG \cite{Wu2016PAMI} & 30.81 \% & 34.05 \% & 24.86 \% & 3 s / 4 cores \\
CSoR \cite{Plotkin2015} & 25.38 \% & 34.43 \% & 21.95 \% & 3.5 s / 4 cores
\end{tabular}