\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
MMLab-PartA^2 \cite{shi2019part} & 77.48 \% & 85.54 \% & 70.35 \% & 0.08 s / GPU \\
RRC \cite{Ren17CVPR} & 76.49 \% & 84.96 \% & 65.46 \% & 3.6 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 76.18 \% & 84.75 \% & 67.55 \% & 0.47 s / GPU \\
CLA \cite{Zhang2019CVPR} & 74.68 \% & 82.42 \% & 65.11 \% & 0.3 s / GPU \\
MS-CNN \cite{Cai2016ECCV} & 74.45 \% & 82.34 \% & 64.91 \% & 0.4 s / GPU \\
TuSimple \cite{yang2016exploit} & 74.26 \% & 81.38 \% & 64.88 \% & 1.6 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 \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 72.94 \% & 83.64 \% & 66.07 \% & 0.1 s / GPU \\
STD \cite{std2019yang} & 72.63 \% & 82.18 \% & 65.16 \% & 0.08 s / GPU \\
sensekitti \cite{binyang16craft} & 72.50 \% & 81.76 \% & 64.00 \% & 4.5 s / GPU \\
F-PointNet \cite{qi2017frustum} & 72.25 \% & 84.90 \% & 65.14 \% & 0.17 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 70.77 \% & 77.82 \% & 62.71 \% & 2 s / GPU \\
ODES \cite{ERROR: Wrong syntax in BIBTEX file.} & 69.80 \% & 78.51 \% & 61.32 \% & 0.02 s / GPU \\
AB3DMOT \cite{Weng2019} & 69.46 \% & 81.27 \% & 62.82 \% & 0.0047s / 1 core \\
MonoPSR \cite{ku2019monopsr} & 68.99 \% & 79.80 \% & 60.19 \% & 0.2 s / GPU \\
3DOP \cite{Chen2015NIPS} & 68.81 \% & 80.17 \% & 61.36 \% & 3s / GPU \\
PointPillars \cite{lang2018pointpillars} & 68.57 \% & 82.59 \% & 62.37 \% & 16 ms / \\
Pose-RCNN \cite{braun2016pose} & 68.04 \% & 80.19 \% & 59.95 \% & 2 s / >8 cores \\
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 \\
AVOD-FPN \cite{ku2018joint} & 59.32 \% & 68.65 \% & 55.82 \% & 0.1 s / \\
SECOND \cite{yan2018second} & 58.94 \% & 81.96 \% & 57.20 \% & 38 ms / \\
Regionlets \cite{Wang2015PAMI} & 58.69 \% & 70.09 \% & 51.81 \% & 1 s / >8 cores \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 57.53 \% & 65.82 \% & 57.47 \% & 0.06 s / GPU \\
FRCNN+Or \cite{GuindelITSM} & 57.37 \% & 70.05 \% & 51.00 \% & 0.09 s / \\
AVOD \cite{ku2018joint} & 56.01 \% & 65.72 \% & 48.89 \% & 0.08 s / \\
BirdNet \cite{BirdNet2018} & 49.04 \% & 64.88 \% & 46.61 \% & 0.11 s / \\
Int-YOLO \cite{ERROR: Wrong syntax in BIBTEX file.} & 43.30 \% & 52.88 \% & 36.57 \% & 0.03 s / 1 core \\
MV-RGBD-RF \cite{Gonzalez2016TCYB} & 42.61 \% & 51.46 \% & 37.42 \% & 4 s / 4 cores \\
Shift R-CNN (mono) \cite{shiftrcnn} & 42.30 \% & 65.56 \% & 41.40 \% & 0.25 s / GPU \\
M3D-RPN \cite{brazil2019m3drpn} & 41.12 \% & 63.69 \% & 39.95 \% & 0.16 s / GPU \\
SS3D \cite{DBLPjournalscorrabs190608070} & 37.90 \% & 53.79 \% & 35.12 \% & 48 ms / \\
pAUCEnsT \cite{Paul2014ARXIV} & 37.88 \% & 52.28 \% & 33.38 \% & 60 s / 1 core \\
TopNet-Retina \cite{8569433} & 35.20 \% & 50.28 \% & 34.11 \% & 52ms / \\
yolov3\_warp \cite{ERROR: Wrong syntax in BIBTEX file.} & 34.39 \% & 48.21 \% & 29.30 \% & 0.5 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 \\
TopNet-HighRes \cite{8569433} & 19.15 \% & 29.34 \% & 19.69 \% & 101ms / \\
TopNet-UncEst \cite{wirges2019capturing} & 16.21 \% & 19.18 \% & 15.99 \% & 0.09 s / \\
YOLOv2 \cite{redmon2016you} & 4.55 \% & 4.55 \% & 4.55 \% & 0.02 s / GPU \\
TopNet-DecayRate \cite{8569433} & 1.01 \% & 0.04 \% & 1.01 \% & 92 ms /
\end{tabular}