\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
F-PointNet \cite{qi2017frustum} & 80.13 \% & 89.83 \% & 75.05 \% & 0.17 s / GPU \\
TuSimple \cite{yang2016exploit} & 78.40 \% & 88.87 \% & 73.66 \% & 1.6 s / GPU \\
RRC \cite{Ren17CVPR} & 76.61 \% & 85.98 \% & 71.47 \% & 3.6 s / GPU \\
ECP Faster R-CNN \cite{DBLPjournalscorrabs180507193} & 76.25 \% & 85.96 \% & 70.55 \% & 0.25 s / GPU \\
Aston-EAS \cite{wei2019enhanced} & 76.07 \% & 86.71 \% & 70.02 \% & 0.24 s / GPU \\
MHN \cite{jiale2018arXiv} & 75.99 \% & 87.21 \% & 69.50 \% & 0.39 s / GPU \\
FFNet \cite{zhao2019monocular} & 75.81 \% & 87.17 \% & 69.86 \% & 1.07 s / GPU \\
SJTU-HW \cite{zsq2018icip} & 75.81 \% & 87.17 \% & 69.86 \% & 0.85s / GPU \\
MS-CNN \cite{Cai2016ECCV} & 74.89 \% & 85.71 \% & 68.99 \% & 0.4 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 72.91 \% & 83.63 \% & 67.18 \% & 0.47 s / GPU \\
GN \cite{JUNG201743} & 72.29 \% & 82.93 \% & 65.56 \% & 1 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 72.27 \% & 84.88 \% & 66.82 \% & 2 s / GPU \\
VMVS \cite{ku2018joint} & 71.82 \% & 82.80 \% & 66.85 \% & 0.25 s / GPU \\
IVA \cite{Zhu2016ACCV} & 71.37 \% & 84.61 \% & 64.90 \% & 0.4 s / GPU \\
MM-MRFC \cite{Costea2017CVPR} & 70.76 \% & 83.79 \% & 64.81 \% & 0.05 s / GPU \\
SDP+RPN \cite{Yang2016CVPR} & 70.42 \% & 82.07 \% & 65.09 \% & 0.4 s / GPU \\
3DOP \cite{Chen2015NIPS} & 69.57 \% & 83.17 \% & 63.48 \% & 3s / GPU \\
MonoPSR \cite{ku2019monopsr} & 68.56 \% & 85.60 \% & 63.34 \% & 0.2 s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 68.46 \% & 83.00 \% & 63.35 \% & 3.4 s / GPU \\
sensekitti \cite{binyang16craft} & 68.41 \% & 82.72 \% & 62.72 \% & 4.5 s / GPU \\
Mono3D \cite{Chen2016CVPR} & 67.29 \% & 80.30 \% & 62.23 \% & 4.2 s / GPU \\
Faster R-CNN \cite{Ren2015NIPS} & 66.24 \% & 79.97 \% & 61.09 \% & 2 s / GPU \\
SDP+CRC (ft) \cite{Yang2016CVPR} & 64.36 \% & 79.22 \% & 59.16 \% & 0.6 s / GPU \\
Pose-RCNN \cite{braun2016pose} & 63.54 \% & 80.07 \% & 57.02 \% & 2 s / >8 cores \\
CFM \cite{7807316} & 62.84 \% & 74.76 \% & 56.06 \% & \\
RPN+BF \cite{Zhang2016ECCV} & 61.22 \% & 77.06 \% & 55.22 \% & 0.6 s / GPU \\
Regionlets \cite{Wang2015PAMI} & 60.83 \% & 73.79 \% & 54.72 \% & 1 s / >8 cores \\
OHS-Direct \cite{chen2019object} & 60.63 \% & 69.37 \% & 57.64 \% & 0.03 s / 1 core \\
TANet \cite{liu2019tanet} & 59.07 \% & 69.90 \% & 56.44 \% & 0.035s / GPU \\
OHS-Dense \cite{chen2019object} & 58.70 \% & 68.18 \% & 54.68 \% & 0.03 s / 1 core \\
DeepParts \cite{Tian2015ICCV} & 58.15 \% & 71.47 \% & 51.92 \% & ~1 s / GPU \\
CompACT-Deep \cite{Cai2015ICCV} & 58.14 \% & 70.93 \% & 52.29 \% & 1 s / 1 core \\
MMLab-PartA^2 \cite{shi2019part} & 57.96 \% & 68.78 \% & 54.01 \% & 0.08 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 57.87 \% & 67.95 \% & 55.23 \% & 0.1 s / \\
FRCNN+Or \cite{GuindelITSM} & 56.68 \% & 71.64 \% & 51.53 \% & 0.09 s / \\
FilteredICF \cite{Zhang2015CVPR} & 56.53 \% & 69.79 \% & 50.32 \% & ~ 2 s / >8 cores \\
ARPNET \cite{Ye2019} & 56.42 \% & 69.08 \% & 52.69 \% & 0.08 s / GPU \\
MV-RGBD-RF \cite{Gonzalez2016TCYB} & 56.18 \% & 72.99 \% & 49.72 \% & 4 s / 4 cores \\
MLOD \cite{deng2019mlod} & 55.62 \% & 68.42 \% & 51.45 \% & 0.12 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 55.10 \% & 65.29 \% & 52.39 \% & 16 ms / \\
STD \cite{std2019yang} & 55.04 \% & 68.33 \% & 50.85 \% & 0.08 s / GPU \\
Vote3Deep \cite{Engelcke2016ARXIV} & 54.80 \% & 67.99 \% & 51.17 \% & 1.5 s / 4 cores \\
epBRM \cite{arxiv} & 54.13 \% & 62.90 \% & 51.95 \% & 0.10 s / 1 core \\
PointPainting \cite{vora2019pointpainting} & 53.76 \% & 61.86 \% & 50.61 \% & 0.4 s / GPU \\
PDV2 \cite{Shen2017PR} & 53.54 \% & 65.59 \% & 47.65 \% & 3.7 s / 1 core \\
TAFT \cite{Shen2018 TITS} & 53.15 \% & 67.62 \% & 47.08 \% & 0.2 s / 1 core \\
pAUCEnsT \cite{Paul2014ARXIV} & 52.88 \% & 65.84 \% & 46.97 \% & 60 s / 1 core \\
Shift R-CNN (mono) \cite{shiftrcnn} & 51.30 \% & 70.86 \% & 46.37 \% & 0.25 s / GPU \\
SCNet \cite{8813061} & 49.61 \% & 60.95 \% & 46.91 \% & 0.04 s / GPU \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 49.41 \% & 58.93 \% & 46.44 \% & 0.1 s / GPU \\
VOXEL\_FPN\_HR \cite{ERROR: Wrong syntax in BIBTEX file.} & 49.09 \% & 60.28 \% & 45.47 \% & 0.12 s / 8 cores \\
Int-YOLO \cite{ERROR: Wrong syntax in BIBTEX file.} & 48.76 \% & 64.09 \% & 44.31 \% & 0.03 s / 1 core \\
ACFD \cite{DBLPconfivsDimitrievskiVP17} & 48.63 \% & 61.62 \% & 44.15 \% & 0.2 s / 4 cores \\
R-CNN \cite{Hosang2015DnnForPedestrians} & 48.57 \% & 62.88 \% & 43.05 \% & 4 s / GPU \\
SS3D \cite{DBLPjournalscorrabs190608070} & 45.79 \% & 61.58 \% & 41.14 \% & 48 ms / \\
ACF \cite{Dollar2014PAMI} & 45.67 \% & 59.81 \% & 40.88 \% & 1 s / 1 core \\
Fusion-DPM \cite{Premebida2014IROS} & 44.99 \% & 58.93 \% & 40.19 \% & ~ 30 s / 1 core \\
ACF-MR \cite{Nattoji2016TITS} & 44.79 \% & 58.29 \% & 39.94 \% & 0.6 s / 1 core \\
HA-SSVM \cite{Xu2016IJCV} & 43.87 \% & 58.76 \% & 38.81 \% & 21 s / 1 core \\
AB3DMOT \cite{Weng2019} & 43.86 \% & 54.55 \% & 40.99 \% & 0.0047s / 1 core \\
D4LCN \cite{ding2019learning} & 43.50 \% & 59.55 \% & 37.12 \% & 0.2 s / GPU \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 43.26 \% & 59.21 \% & 38.12 \% & 8 s / 1 core \\
ACF-SC \cite{Cadena2015ICRA} & 42.97 \% & 53.30 \% & 38.12 \% & \\
SquaresICF \cite{Benenson2013Cvpr} & 42.61 \% & 57.08 \% & 37.85 \% & 1 s / GPU \\
CSW3D \cite{hu2019csw3d} & 41.50 \% & 53.76 \% & 37.25 \% & 0.03 s / 4 cores \\
M3D-RPN \cite{brazil2019m3drpn} & 41.46 \% & 56.64 \% & 37.31 \% & 0.16 s / GPU \\
RTM3D \cite{li2020rtm3d} & 41.43 \% & 58.14 \% & 37.09 \% & 0.05 s / GPU \\
yolov3\_warp \cite{ERROR: Wrong syntax in BIBTEX file.} & 40.64 \% & 55.04 \% & 36.33 \% & 0.5 s / 1 core \\
SubCat \cite{OhnBar2014CVPRWORK} & 40.50 \% & 53.75 \% & 35.66 \% & 1.2 s / 6 cores \\
DSGN \cite{Chen2020dsgn} & 39.93 \% & 49.28 \% & 38.13 \% & 0.67 s / \\
SparsePool \cite{wang2017fusing} & 39.59 \% & 50.81 \% & 35.91 \% & 0.13 s / 8 cores \\
SparsePool \cite{wang2017fusing} & 39.43 \% & 50.94 \% & 35.78 \% & 0.13 s / 8 cores \\
AVOD \cite{ku2018joint} & 39.43 \% & 50.90 \% & 35.75 \% & 0.08 s / \\
ACF \cite{Dollar2014PAMI} & 39.12 \% & 48.42 \% & 35.03 \% & 0.2 s / 1 core \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 37.26 \% & 50.74 \% & 33.13 \% & 10 s / 4 cores \\
multi-task CNN \cite{Oeljeklaus18} & 37.00 \% & 49.38 \% & 33.46 \% & 25.1 ms / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 36.45 \% & 42.16 \% & 32.91 \% & 0.06 s / GPU \\
LSVM-MDPM-us \cite{Felzenszwalb2010PAMI} & 35.92 \% & 48.73 \% & 31.70 \% & 10 s / 4 cores \\
Vote3D \cite{Wang2015RSS} & 33.04 \% & 42.66 \% & 30.59 \% & 0.5 s / 4 cores \\
mBoW \cite{Behley2013IROS} & 30.26 \% & 41.52 \% & 26.34 \% & 10 s / 1 core \\
BirdNet \cite{BirdNet2018} & 29.58 \% & 36.62 \% & 28.25 \% & 0.11 s / \\
RT3DStereo \cite{Koenigshof2019Objects} & 29.30 \% & 41.12 \% & 25.25 \% & 0.08 s / GPU \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 25.34 \% & 36.40 \% & 22.00 \% & 15 s / 4 cores \\
RefinedMPL \cite{vianney2019refinedmpl} & 20.81 \% & 30.41 \% & 18.72 \% & 0.1 s / GPU \\
TopNet-Retina \cite{8569433} & 16.45 \% & 22.37 \% & 15.43 \% & 52ms / \\
TopNet-HighRes \cite{8569433} & 15.28 \% & 21.22 \% & 13.89 \% & 101ms / \\
YOLOv2 \cite{redmon2016you} & 11.46 \% & 15.37 \% & 9.67 \% & 0.02 s / GPU \\
TopNet-UncEst \cite{wirges2019capturing} & 8.58 \% & 13.00 \% & 7.38 \% & 0.09 s / \\
BIP-HETERO \cite{Mekonnen2014ICPR} & 7.05 \% & 8.51 \% & 6.30 \% & ~2 s / 1 core \\
TopNet-DecayRate \cite{8569433} & 0.01 \% & 0.01 \% & 0.01 \% & 92 ms /
\end{tabular}