\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
Deformable PV-RCNN \cite{bhattacharyya2020deformable} & 80.57 \% & 88.65 \% & 74.81 \% & 0.08 s / 1 core \\
MMLab PV-RCNN \cite{shi2020pv} & 80.42 \% & 86.62 \% & 73.64 \% & 0.08 s / 1 core \\
HotSpotNet \cite{chen2020object} & 78.81 \% & 86.06 \% & 71.74 \% & 0.04 s / 1 core \\
MMLab-PartA^2 \cite{shi2020part} & 78.29 \% & 88.90 \% & 71.19 \% & 0.08 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 78.05 \% & 86.75 \% & 68.12 \% & 0.47 s / GPU \\
PointPainting \cite{vora2019pointpainting} & 78.04 \% & 87.70 \% & 69.27 \% & 0.4 s / GPU \\
RRC \cite{Ren17CVPR} & 76.81 \% & 86.81 \% & 66.59 \% & 3.6 s / GPU \\
MS-CNN \cite{Cai2016ECCV} & 75.30 \% & 84.88 \% & 65.27 \% & 0.4 s / GPU \\
VOXEL\_FPN\_HR \cite{ERROR: Wrong syntax in BIBTEX file.} & 75.24 \% & 87.73 \% & 68.60 \% & 0.12 s / 8 cores \\
TuSimple \cite{yang2016exploit} & 75.22 \% & 83.68 \% & 65.22 \% & 1.6 s / GPU \\
Point-GNN \cite{shi2020pointgnn} & 75.08 \% & 85.75 \% & 68.69 \% & 0.6 s / GPU \\
Deep3DBox \cite{MousavianCVPR2017} & 74.78 \% & 84.36 \% & 64.05 \% & 1.5 s / GPU \\
3DBN\_2 \cite{ERROR: Wrong syntax in BIBTEX file.} & 74.34 \% & 88.48 \% & 67.66 \% & 0.12 s / 1 core \\
3DSSD \cite{yang3DSSD20} & 74.12 \% & 87.09 \% & 67.67 \% & 0.04 s / GPU \\
SDP+RPN \cite{Yang2016CVPR} & 73.85 \% & 82.59 \% & 64.87 \% & 0.4 s / GPU \\
sensekitti \cite{binyang16craft} & 73.48 \% & 82.90 \% & 64.03 \% & 4.5 s / GPU \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 73.42 \% & 86.21 \% & 66.45 \% & 0.1 s / GPU \\
F-PointNet \cite{qi2017frustum} & 73.16 \% & 86.86 \% & 65.21 \% & 0.17 s / GPU \\
MonoPSR \cite{ku2019monopsr} & 72.08 \% & 82.06 \% & 62.43 \% & 0.2 s / GPU \\
ARPNET \cite{Ye2019} & 71.95 \% & 84.96 \% & 65.21 \% & 0.08 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 71.72 \% & 79.36 \% & 62.74 \% & 2 s / GPU \\
STD \cite{std2019yang} & 71.63 \% & 83.99 \% & 64.92 \% & 0.08 s / GPU \\
AB3DMOT \cite{Weng2019} & 70.18 \% & 82.86 \% & 63.55 \% & 0.0047s / 1 core \\
PointPillars \cite{lang2018pointpillars} & 68.98 \% & 83.97 \% & 62.17 \% & 16 ms / \\
Vote3Deep \cite{Engelcke2016ARXIV} & 68.82 \% & 78.41 \% & 62.50 \% & 1.5 s / 4 cores \\
3DOP \cite{Chen2015NIPS} & 68.71 \% & 80.52 \% & 61.07 \% & 3s / GPU \\
Pose-RCNN \cite{braun2016pose} & 68.40 \% & 81.53 \% & 59.43 \% & 2 s / >8 cores \\
TANet \cite{liu2019tanet} & 68.20 \% & 82.24 \% & 62.13 \% & 0.035s / GPU \\
IVA \cite{Zhu2016ACCV} & 67.57 \% & 78.48 \% & 58.83 \% & 0.4 s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 67.22 \% & 79.35 \% & 58.60 \% & 3.4 s / GPU \\
epBRM \cite{arxiv} & 66.51 \% & 79.65 \% & 60.31 \% & 0.10 s / 1 core \\
Mono3D \cite{Chen2016CVPR} & 65.15 \% & 77.19 \% & 57.88 \% & 4.2 s / GPU \\
Faster R-CNN \cite{Ren2015NIPS} & 62.86 \% & 72.40 \% & 54.97 \% & 2 s / GPU \\
SCNet \cite{8813061} & 62.50 \% & 78.48 \% & 56.34 \% & 0.04 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 60.79 \% & 70.38 \% & 55.37 \% & 0.1 s / \\
SDP+CRC (ft) \cite{Yang2016CVPR} & 60.72 \% & 75.63 \% & 53.00 \% & 0.6 s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 59.78 \% & 66.94 \% & 55.63 \% & 0.06 s / GPU \\
Regionlets \cite{Wang2015PAMI} & 58.52 \% & 71.12 \% & 50.83 \% & 1 s / >8 cores \\
FRCNN+Or \cite{GuindelITSM} & 57.01 \% & 70.99 \% & 50.14 \% & 0.09 s / \\
MonoPair \cite{chen2020cvpr} & 56.37 \% & 74.77 \% & 48.37 \% & 0.06 s / GPU \\
MLOD \cite{deng2019mlod} & 56.04 \% & 75.35 \% & 49.11 \% & 0.12 s / GPU \\
BirdNet+ \cite{Barrera2020} & 54.61 \% & 74.97 \% & 50.29 \% & 0.1 s / \\
AVOD \cite{ku2018joint} & 52.60 \% & 66.45 \% & 46.39 \% & 0.08 s / \\
CG-Stereo \cite{li2020confidence} & 48.46 \% & 69.98 \% & 42.41 \% & 0.57 s / \\
BirdNet \cite{BirdNet2018} & 47.64 \% & 64.91 \% & 44.59 \% & 0.11 s / \\
SparsePool \cite{wang2017fusing} & 44.57 \% & 60.53 \% & 40.37 \% & 0.13 s / 8 cores \\
Shift R-CNN (mono) \cite{shiftrcnn} & 42.96 \% & 63.24 \% & 38.22 \% & 0.25 s / GPU \\
D4LCN \cite{ding2019learning} & 42.86 \% & 65.29 \% & 36.29 \% & 0.2 s / GPU \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 42.25 \% & 58.27 \% & 36.90 \% & 0.42 s / GPU \\
Disp R-CNN \cite{sun2020disprcnn} & 42.23 \% & 58.26 \% & 36.88 \% & 0.42 s / GPU \\
M3D-RPN \cite{brazil2019m3drpn} & 41.54 \% & 61.54 \% & 35.23 \% & 0.16 s / GPU \\
MV-RGBD-RF \cite{Gonzalez2016TCYB} & 40.94 \% & 51.10 \% & 34.83 \% & 4 s / 4 cores \\
SparsePool \cite{wang2017fusing} & 36.26 \% & 44.21 \% & 32.57 \% & 0.13 s / 8 cores \\
SS3D \cite{DBLPjournalscorrabs190608070} & 35.48 \% & 52.97 \% & 31.07 \% & 48 ms / \\
DSGN \cite{Chen2020dsgn} & 35.15 \% & 49.10 \% & 31.41 \% & 0.67 s / \\
pAUCEnsT \cite{Paul2014ARXIV} & 34.90 \% & 50.51 \% & 30.35 \% & 60 s / 1 core \\
TopNet-Retina \cite{8569433} & 31.98 \% & 47.51 \% & 29.84 \% & 52ms / \\
OC Stereo \cite{pon2020object} & 28.76 \% & 43.18 \% & 24.80 \% & 0.35 s / 1 core \\
Vote3D \cite{Wang2015RSS} & 27.99 \% & 39.81 \% & 25.19 \% & 0.5 s / 4 cores \\
LSVM-MDPM-us \cite{Felzenszwalb2010PAMI} & 27.81 \% & 37.66 \% & 24.83 \% & 10 s / 4 cores \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 27.73 \% & 41.58 \% & 24.61 \% & 8 s / 1 core \\
RefinedMPL \cite{vianney2019refinedmpl} & 27.17 \% & 44.47 \% & 22.84 \% & 0.15 s / GPU \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 26.05 \% & 35.70 \% & 23.56 \% & 10 s / 4 cores \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 25.57 \% & 41.47 \% & 21.93 \% & 15 s / 4 cores \\
mBoW \cite{Behley2013IROS} & 17.63 \% & 26.66 \% & 16.02 \% & 10 s / 1 core \\
TopNet-HighRes \cite{8569433} & 13.98 \% & 22.86 \% & 14.52 \% & 101ms / \\
RT3DStereo \cite{Koenigshof2019Objects} & 12.96 \% & 19.58 \% & 11.47 \% & 0.08 s / GPU \\
yyyyolo \cite{ERROR: Wrong syntax in BIBTEX file.} & 12.52 \% & 16.29 \% & 11.07 \% & 0.01 s / 1 core \\
TopNet-UncEst \cite{wirges2019capturing} & 12.00 \% & 18.14 \% & 11.85 \% & 0.09 s / \\
YOLOv2 \cite{redmon2016you} & 0.06 \% & 0.15 \% & 0.07 \% & 0.02 s / GPU \\
TopNet-DecayRate \cite{8569433} & 0.04 \% & 0.00 \% & 0.04 \% & 92 ms /
\end{tabular}