\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
RRC \cite{Ren17CVPR} & 90.22 \% & 90.61 \% & 87.44 \% & 3.6 s / GPU \\
Deep MANTA \cite{deepmantacvpr17} & 90.03 \% & 97.25 \% & 80.62 \% & 0.7 s / GPU \\
SDP+RPN \cite{Yang2016CVPR} & 89.42 \% & 89.90 \% & 78.54 \% & 0.4 s / GPU \\
MV3D \cite{Chen2017CVPR} & 88.90 \% & 90.37 \% & 79.81 \% & 0.36 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 88.86 \% & 90.75 \% & 79.24 \% & 2 s / GPU \\
Deep3DBox \cite{MousavianCVPR2017} & 88.86 \% & 90.47 \% & 77.60 \% & 1.5 s / GPU \\
MS-CNN \cite{Cai2016ECCV} & 88.83 \% & 90.46 \% & 74.76 \% & 0.4 s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 88.75 \% & 90.34 \% & 79.39 \% & 3.4 s / GPU \\
3DOP \cite{Chen2015NIPS} & 88.34 \% & 90.09 \% & 78.79 \% & 3s / GPU \\
Mono3D \cite{Chen2016CVPR} & 87.86 \% & 90.27 \% & 78.09 \% & 4.2 s / GPU \\
SDP+CRC (ft) \cite{Yang2016CVPR} & 81.33 \% & 90.39 \% & 70.33 \% & 0.6 s / GPU \\
MV3D (LIDAR) \cite{Chen2017CVPR} & 79.76 \% & 89.80 \% & 78.61 \% & 0.24 s / GPU \\
RefineNet \cite{Rajaram2016ITSC} & 79.21 \% & 90.16 \% & 65.71 \% & 0.20 s / GPU \\
Faster R-CNN \cite{Ren2015NIPS} & 79.11 \% & 87.90 \% & 70.19 \% & 2 s / GPU \\
FRCNN+Or \cite{GuindelICVES} & 78.59 \% & 89.60 \% & 68.69 \% & 0.1 s / GPU \\
spLBP \cite{Hu2016TITS} & 77.39 \% & 80.16 \% & 60.59 \% & 1.5 s / 8 cores \\
Reinspect \cite{Stewart2016CVPR} & 76.65 \% & 88.36 \% & 66.56 \% & 2s / 1 core \\
Regionlets \cite{Wang2015PAMI} & 76.56 \% & 86.50 \% & 59.82 \% & 1 s / >8 cores \\
AOG \cite{Wu2016PAMI} & 75.97 \% & 85.58 \% & 60.96 \% & 3 s / 4 cores \\
3D FCN \cite{li2017iros} & 75.83 \% & 85.54 \% & 68.30 \% & >5 s / 1 core \\
3DVP \cite{Xiang2015CVPR} & 75.77 \% & 81.46 \% & 65.38 \% & 40 s / 8 cores \\
SubCat \cite{OhnBar2015TITS} & 75.46 \% & 81.45 \% & 59.71 \% & 0.7 s / 6 cores \\
MV-RGBD-RF \cite{Gonzalez2016TCYB} & 69.92 \% & 76.49 \% & 57.47 \% & 4 s / 4 cores \\
AOG-View \cite{Li2014ECCV} & 69.89 \% & 84.29 \% & 57.25 \% & 3 s / 1 core \\
Vote3Deep \cite{Engelcke2016ARXIV} & 68.39 \% & 76.95 \% & 63.22 \% & 1.5 s / 4 cores \\
OC-DPM \cite{Pepik2013CVPR} & 66.45 \% & 76.16 \% & 53.70 \% & 10 s / 8 cores \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 66.25 \% & 80.45 \% & 49.86 \% & 8 s / 1 core \\
MDPM-un-BB \cite{Felzenszwalb10} & 64.20 \% & 77.32 \% & 50.18 \% & 60 s / 4 core \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 60.99 \% & 74.95 \% & 47.16 \% & 15 s / 4 cores \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 57.44 \% & 71.70 \% & 46.58 \% & 10 s / 4 cores \\
LSVM-MDPM-us \cite{Felzenszwalb2010PAMI} & 56.10 \% & 70.52 \% & 42.87 \% & 10 s / 4 cores \\
ACF-SC \cite{Cadena2015ICRA} & 55.76 \% & 69.76 \% & 46.27 \% & \\
ACF \cite{Dollar2014PAMI} & 52.81 \% & 62.82 \% & 43.89 \% & 0.2 s / 1 core \\
Vote3D \cite{Wang2015RSS} & 48.05 \% & 56.66 \% & 42.64 \% & 0.5 s / 4 cores \\
CSoR \cite{Plotkin2015} & 26.13 \% & 35.24 \% & 22.69 \% & 3.5 s / 4 cores \\
mBoW \cite{Behley2013IROS} & 23.76 \% & 37.63 \% & 18.44 \% & 10 s / 1 core \\
YOLOv2 \cite{redmon2016you} & 19.31 \% & 28.37 \% & 15.94 \% & 0.02 s / GPU
\end{tabular}