\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
VMVS \cite{ku2018joint} & 67.66 \% & 78.57 \% & 63.83 \% & 0.25 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 66.28 \% & 78.33 \% & 61.37 \% & 2 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 64.32 \% & 72.73 \% & 59.07 \% & 0.47 s / GPU \\
Pose-RCNN \cite{braun2016pose} & 59.89 \% & 74.10 \% & 54.21 \% & 2 s / >8 cores \\
3DOP \cite{Chen2015NIPS} & 59.79 \% & 73.46 \% & 57.04 \% & 3s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 59.28 \% & 73.37 \% & 56.87 \% & 3.4 s / GPU \\
Mono3D \cite{Chen2016CVPR} & 58.12 \% & 68.58 \% & 54.94 \% & 4.2 s / GPU \\
MonoPSR \cite{ku2019monopsr} & 56.30 \% & 70.56 \% & 49.84 \% & 0.2 s / GPU \\
FRCNN+Or \cite{GuindelITSM} & 52.62 \% & 66.84 \% & 48.72 \% & 0.09 s / \\
PointPillars \cite{lang2018pointpillars} & 49.66 \% & 58.05 \% & 47.88 \% & 16 ms / \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 48.98 \% & 57.49 \% & 46.48 \% & 0.1 s / GPU \\
Shift R-CNN (mono) \cite{shiftrcnn} & 48.81 \% & 65.39 \% & 41.05 \% & 0.25 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 44.92 \% & 53.36 \% & 43.77 \% & 0.1 s / \\
SECOND \cite{yan2018second} & 43.51 \% & 51.56 \% & 38.78 \% & 38 ms / \\
SS3D \cite{DBLPjournalscorrabs190608070} & 43.45 \% & 52.70 \% & 37.20 \% & 48 ms / \\
AB3DMOT \cite{Weng2019} & 42.30 \% & 51.71 \% & 38.96 \% & 0.0047s / 1 core \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 39.83 \% & 53.66 \% & 35.73 \% & 8 s / 1 core \\
sensekitti \cite{binyang16craft} & 37.50 \% & 43.55 \% & 35.08 \% & 4.5 s / GPU \\
AVOD \cite{ku2018joint} & 36.38 \% & 44.12 \% & 31.81 \% & 0.08 s / \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 35.49 \% & 47.00 \% & 32.42 \% & 10 s / 4 cores \\
M3D-RPN \cite{brazil2019m3drpn} & 35.06 \% & 46.19 \% & 29.90 \% & 0.16 s / GPU \\
SubCat \cite{OhnBar2014CVPRWORK} & 34.18 \% & 43.95 \% & 30.76 \% & 1.2 s / 6 cores \\
RPN+BF \cite{Zhang2016ECCV} & 32.55 \% & 40.97 \% & 29.52 \% & 0.6 s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 31.80 \% & 37.80 \% & 31.26 \% & 0.06 s / GPU \\
ODES \cite{ERROR: Wrong syntax in BIBTEX file.} & 31.43 \% & 36.84 \% & 29.00 \% & 0.02 s / GPU \\
ACF \cite{Dollar2014PAMI} & 28.46 \% & 35.69 \% & 26.18 \% & 1 s / 1 core \\
multi-task CNN \cite{Oeljeklaus18} & 26.98 \% & 33.58 \% & 23.07 \% & 25.1 ms / GPU \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 23.37 \% & 31.08 \% & 20.72 \% & 15 s / 4 cores \\
ACF-MR \cite{Nattoji2016TITS} & 23.18 \% & 29.35 \% & 21.00 \% & 0.6 s / 1 core \\
BirdNet \cite{BirdNet2018} & 17.26 \% & 21.34 \% & 16.67 \% & 0.11 s /
\end{tabular}