\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
F-PointNet \cite{qi2017frustum} & 77.25 \% & 87.81 \% & 74.46 \% & 0.17 s / GPU \\
TuSimple \cite{yang2016exploit} & 77.04 \% & 86.78 \% & 72.40 \% & 1.6 s / GPU \\
RRC \cite{Ren17CVPR} & 75.33 \% & 84.16 \% & 70.39 \% & 3.6 s / GPU \\
MHN \cite{jiale2018arXiv} & 74.60 \% & 85.81 \% & 68.94 \% & 0.39 s / GPU \\
Aston-EAS \cite{wei2019enhanced} & 74.52 \% & 85.12 \% & 69.35 \% & 0.24 s / GPU \\
ECP Faster R-CNN \cite{DBLPjournalscorrabs180507193} & 74.27 \% & 84.12 \% & 70.06 \% & 0.25 s / GPU \\
SJTU-HW \cite{zsq2018icip} & 74.24 \% & 85.42 \% & 69.34 \% & 0.85s / GPU \\
CLA \cite{Zhang2019CVPR} & 74.03 \% & 84.26 \% & 68.45 \% & 0.3 s / GPU \\
MS-CNN \cite{Cai2016ECCV} & 73.62 \% & 83.70 \% & 68.28 \% & 0.4 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 72.37 \% & 79.98 \% & 66.61 \% & 0.47 s / GPU \\
GN \cite{JUNG201743} & 71.55 \% & 80.73 \% & 64.82 \% & 1 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 71.34 \% & 83.17 \% & 66.36 \% & 2 s / GPU \\
VMVS \cite{ku2018joint} & 70.89 \% & 81.11 \% & 67.23 \% & 0.25 s / GPU \\
IVA \cite{Zhu2016ACCV} & 70.63 \% & 83.03 \% & 64.68 \% & 0.4 s / GPU \\
SDP+RPN \cite{Yang2016CVPR} & 70.20 \% & 79.98 \% & 64.84 \% & 0.4 s / GPU \\
MM-MRFC \cite{Costea2017CVPR} & 69.96 \% & 82.37 \% & 64.76 \% & 0.05 s / GPU \\
MonoPSR \cite{ku2019monopsr} & 68.91 \% & 85.93 \% & 60.83 \% & 0.2 s / GPU \\
3DOP \cite{Chen2015NIPS} & 67.46 \% & 82.36 \% & 64.71 \% & 3s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 67.32 \% & 82.50 \% & 65.14 \% & 3.4 s / GPU \\
sensekitti \cite{binyang16craft} & 67.28 \% & 80.12 \% & 62.25 \% & 4.5 s / GPU \\
ODES \cite{ERROR: Wrong syntax in BIBTEX file.} & 67.25 \% & 77.95 \% & 62.28 \% & 0.02 s / GPU \\
Mono3D \cite{Chen2016CVPR} & 66.66 \% & 77.30 \% & 63.44 \% & 4.2 s / GPU \\
Faster R-CNN \cite{Ren2015NIPS} & 65.91 \% & 78.35 \% & 61.19 \% & 2 s / GPU \\
SDP+CRC (ft) \cite{Yang2016CVPR} & 64.25 \% & 77.81 \% & 59.31 \% & 0.6 s / GPU \\
Pose-RCNN \cite{braun2016pose} & 63.38 \% & 77.69 \% & 57.42 \% & 2 s / >8 cores \\
CFM \cite{7807316} & 63.26 \% & 74.21 \% & 56.44 \% & \\
RPN+BF \cite{Zhang2016ECCV} & 61.29 \% & 75.58 \% & 56.08 \% & 0.6 s / GPU \\
Regionlets \cite{Wang2015PAMI} & 61.16 \% & 72.96 \% & 55.22 \% & 1 s / >8 cores \\
CompACT-Deep \cite{Cai2015ICCV} & 58.73 \% & 69.70 \% & 52.69 \% & 1 s / 1 core \\
DeepParts \cite{Tian2015ICCV} & 58.68 \% & 70.46 \% & 52.73 \% & ~1 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 58.42 \% & 67.32 \% & 57.44 \% & 0.1 s / \\
FilteredICF \cite{Zhang2015CVPR} & 57.12 \% & 69.05 \% & 51.46 \% & ~ 2 s / >8 cores \\
FRCNN+Or \cite{GuindelITSM} & 56.78 \% & 71.18 \% & 52.86 \% & 0.09 s / \\
MV-RGBD-RF \cite{Gonzalez2016TCYB} & 56.59 \% & 73.05 \% & 49.63 \% & 4 s / 4 cores \\
SECOND \cite{yan2018second} & 55.74 \% & 65.73 \% & 49.08 \% & 38 ms / \\
PointPillars \cite{lang2018pointpillars} & 55.68 \% & 64.66 \% & 53.93 \% & 16 ms / \\
STD \cite{std2019yang} & 55.44 \% & 69.09 \% & 53.46 \% & 0.08 s / GPU \\
Vote3Deep \cite{Engelcke2016ARXIV} & 55.38 \% & 67.94 \% & 52.62 \% & 1.5 s / 4 cores \\
TAFT \cite{Shen2018 TITS} & 54.59 \% & 67.07 \% & 48.48 \% & 0.2 s / 1 core \\
pAUCEnsT \cite{Paul2014ARXIV} & 54.58 \% & 66.11 \% & 48.49 \% & 60 s / 1 core \\
PDV2 \cite{Shen2017PR} & 53.74 \% & 65.71 \% & 49.47 \% & 3.7 s / 1 core \\
Shift R-CNN (mono) \cite{shiftrcnn} & 53.33 \% & 71.11 \% & 44.71 \% & 0.25 s / GPU \\
ACFD \cite{DBLPconfivsDimitrievskiVP17} & 50.91 \% & 61.59 \% & 45.51 \% & 0.2 s / 4 cores \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 50.88 \% & 59.05 \% & 48.46 \% & 0.1 s / GPU \\
R-CNN \cite{Hosang2015DnnForPedestrians} & 50.20 \% & 62.05 \% & 44.85 \% & 4 s / GPU \\
SS3D \cite{DBLPjournalscorrabs190608070} & 49.81 \% & 59.46 \% & 42.44 \% & 48 ms / \\
Int-YOLO \cite{ERROR: Wrong syntax in BIBTEX file.} & 48.93 \% & 64.40 \% & 48.02 \% & 0.03 s / 1 core \\
ACF \cite{Dollar2014PAMI} & 47.29 \% & 60.11 \% & 42.90 \% & 1 s / 1 core \\
Fusion-DPM \cite{Premebida2014IROS} & 46.67 \% & 59.38 \% & 42.05 \% & ~ 30 s / 1 core \\
ACF-MR \cite{Nattoji2016TITS} & 46.23 \% & 58.85 \% & 42.10 \% & 0.6 s / 1 core \\
AB3DMOT \cite{Weng2019} & 46.06 \% & 55.63 \% & 42.60 \% & 0.0047s / 1 core \\
M3D-RPN \cite{brazil2019m3drpn} & 46.02 \% & 59.82 \% & 39.31 \% & 0.16 s / GPU \\
HA-SSVM \cite{Xu2016IJCV} & 45.51 \% & 58.91 \% & 41.08 \% & 21 s / 1 core \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 44.86 \% & 59.60 \% & 40.37 \% & 8 s / 1 core \\
ACF-SC \cite{Cadena2015ICRA} & 44.77 \% & 54.20 \% & 39.57 \% & \\
SquaresICF \cite{Benenson2013Cvpr} & 44.42 \% & 57.47 \% & 40.08 \% & 1 s / GPU \\
AVOD \cite{ku2018joint} & 43.49 \% & 51.64 \% & 37.79 \% & 0.08 s / \\
SubCat \cite{OhnBar2014CVPRWORK} & 42.34 \% & 54.06 \% & 37.95 \% & 1.2 s / 6 cores \\
yolov3\_warp \cite{ERROR: Wrong syntax in BIBTEX file.} & 41.07 \% & 56.07 \% & 39.08 \% & 0.5 s / 1 core \\
ACF \cite{Dollar2014PAMI} & 40.62 \% & 49.08 \% & 36.66 \% & 0.2 s / 1 core \\
multi-task CNN \cite{Oeljeklaus18} & 40.34 \% & 51.38 \% & 34.98 \% & 25.1 ms / GPU \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 39.36 \% & 51.75 \% & 35.95 \% & 10 s / 4 cores \\
LSVM-MDPM-us \cite{Felzenszwalb2010PAMI} & 38.35 \% & 50.01 \% & 34.78 \% & 10 s / 4 cores \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 36.10 \% & 42.63 \% & 35.57 \% & 0.06 s / GPU \\
Vote3D \cite{Wang2015RSS} & 35.74 \% & 44.47 \% & 33.72 \% & 0.5 s / 4 cores \\
mBoW \cite{Behley2013IROS} & 31.37 \% & 44.36 \% & 30.62 \% & 10 s / 1 core \\
BirdNet \cite{BirdNet2018} & 30.90 \% & 36.83 \% & 29.93 \% & 0.11 s / \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 29.03 \% & 38.96 \% & 25.61 \% & 15 s / 4 cores \\
TopNet-Retina \cite{8569433} & 19.67 \% & 25.17 \% & 16.33 \% & 52ms / \\
TopNet-HighRes \cite{8569433} & 17.57 \% & 22.98 \% & 17.35 \% & 101ms / \\
YOLOv2 \cite{redmon2016you} & 16.19 \% & 20.80 \% & 15.43 \% & 0.02 s / GPU \\
BIP-HETERO \cite{Mekonnen2014ICPR} & 13.38 \% & 14.85 \% & 13.25 \% & ~2 s / 1 core \\
TopNet-UncEst \cite{wirges2019capturing} & 10.91 \% & 15.55 \% & 10.05 \% & 0.09 s / \\
TopNet-DecayRate \cite{8569433} & 0.04 \% & 0.02 \% & 0.05 \% & 92 ms /
\end{tabular}