\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
RRC \cite{Ren17CVPR} & 76.47 \% & 84.96 \% & 65.46 \% & 3.6 s / GPU \\
MS-CNN \cite{Cai2016ECCV} & 74.45 \% & 82.34 \% & 64.91 \% & 0.4 s / GPU \\
Deep3DBox \cite{MousavianCVPR2017} & 73.48 \% & 82.65 \% & 64.11 \% & 1.5 s / GPU \\
SDP+RPN \cite{Yang2016CVPR} & 73.08 \% & 81.05 \% & 64.88 \% & 0.4 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 70.77 \% & 77.82 \% & 62.71 \% & 2 s / GPU \\
3DOP \cite{Chen2015NIPS} & 68.81 \% & 80.17 \% & 61.36 \% & 3s / GPU \\
Vote3Deep \cite{Engelcke2016ARXIV} & 67.96 \% & 76.49 \% & 62.88 \% & 1.5 s / 4 cores \\
IVA \cite{Zhu2016ACCV} & 67.36 \% & 77.63 \% & 59.62 \% & 0.4 s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 65.72 \% & 77.00 \% & 57.74 \% & 3.4 s / GPU \\
Mono3D \cite{Chen2016CVPR} & 63.85 \% & 75.22 \% & 58.96 \% & 4.2 s / GPU \\
Faster R-CNN \cite{Ren2015NIPS} & 62.81 \% & 71.41 \% & 55.44 \% & 2 s / GPU \\
SDP+CRC (ft) \cite{Yang2016CVPR} & 60.87 \% & 74.31 \% & 53.95 \% & 0.6 s / GPU \\
Regionlets \cite{Wang2015PAMI} & 58.69 \% & 70.09 \% & 51.81 \% & 1 s / >8 cores \\
maxFtr+ROI \cite{Tian2017VISAPP} & 43.59 \% & 49.65 \% & 38.74 \% & 0.25 s / 4 cores \\
MV-RGBD-RF \cite{Gonzalez2016TCYB} & 42.61 \% & 51.46 \% & 37.42 \% & 4 s / 4 cores \\
pAUCEnsT \cite{Paul2014ARXIV} & 37.88 \% & 52.28 \% & 33.38 \% & 60 s / 1 core \\
Vote3D \cite{Wang2015RSS} & 31.24 \% & 41.45 \% & 28.60 \% & 0.5 s / 4 cores \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 31.16 \% & 43.65 \% & 28.29 \% & 8 s / 1 core \\
LSVM-MDPM-us \cite{Felzenszwalb2010PAMI} & 30.81 \% & 40.31 \% & 28.17 \% & 10 s / 4 cores \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 29.24 \% & 37.71 \% & 27.52 \% & 10 s / 4 cores \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 29.04 \% & 43.28 \% & 26.20 \% & 15 s / 4 cores \\
mBoW \cite{Behley2013IROS} & 21.62 \% & 28.19 \% & 20.93 \% & 10 s / 1 core \\
YOLOv2 \cite{redmon2016you} & 4.55 \% & 4.55 \% & 4.55 \% & 0.02 s / GPU
\end{tabular}