\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
Deformable PV-RCNN \cite{bhattacharyya2020deformable} & 80.05 \% & 88.52 \% & 74.20 \% & 0.08 s / 1 core \\
MMLab PV-RCNN \cite{shi2020pv} & 79.70 \% & 86.43 \% & 72.96 \% & 0.08 s / 1 core \\
HotSpotNet \cite{chen2020object} & 78.31 \% & 85.79 \% & 71.24 \% & 0.04 s / 1 core \\
MMLab-PartA^2 \cite{shi2020part} & 77.52 \% & 88.70 \% & 70.41 \% & 0.08 s / GPU \\
PointPainting \cite{vora2019pointpainting} & 76.92 \% & 87.33 \% & 68.21 \% & 0.4 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 76.71 \% & 86.39 \% & 66.92 \% & 0.47 s / GPU \\
VOXEL\_FPN\_HR \cite{ERROR: Wrong syntax in BIBTEX file.} & 74.77 \% & 87.41 \% & 68.16 \% & 0.12 s / 8 cores \\
3DBN\_2 \cite{ERROR: Wrong syntax in BIBTEX file.} & 73.69 \% & 87.96 \% & 66.91 \% & 0.12 s / 1 core \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 72.81 \% & 85.94 \% & 65.84 \% & 0.1 s / GPU \\
AB3DMOT \cite{Weng2019} & 69.54 \% & 82.18 \% & 62.98 \% & 0.0047s / 1 core \\
ARPNET \cite{Ye2019} & 68.72 \% & 82.61 \% & 62.00 \% & 0.08 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 68.55 \% & 83.79 \% & 61.71 \% & 16 ms / \\
TANet \cite{liu2019tanet} & 66.37 \% & 81.15 \% & 60.10 \% & 0.035s / GPU \\
SubCNN \cite{xiang2017subcategory} & 63.36 \% & 71.97 \% & 55.42 \% & 2 s / GPU \\
Pose-RCNN \cite{braun2016pose} & 62.02 \% & 75.74 \% & 53.99 \% & 2 s / >8 cores \\
SCNet \cite{8813061} & 61.11 \% & 77.77 \% & 54.82 \% & 0.04 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 58.70 \% & 69.21 \% & 53.47 \% & 0.1 s / \\
Deep3DBox \cite{MousavianCVPR2017} & 58.56 \% & 68.31 \% & 50.30 \% & 1.5 s / GPU \\
3DOP \cite{Chen2015NIPS} & 58.45 \% & 72.24 \% & 51.91 \% & 3s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 58.28 \% & 65.41 \% & 54.27 \% & 0.06 s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 56.55 \% & 69.36 \% & 49.37 \% & 3.4 s / GPU \\
Mono3D \cite{Chen2016CVPR} & 53.96 \% & 67.33 \% & 47.91 \% & 4.2 s / GPU \\
AVOD \cite{ku2018joint} & 51.05 \% & 64.81 \% & 45.12 \% & 0.08 s / \\
BirdNet+ \cite{Barrera2020} & 50.94 \% & 69.92 \% & 47.01 \% & 0.1 s / \\
FRCNN+Or \cite{GuindelITSM} & 49.53 \% & 63.45 \% & 43.65 \% & 0.09 s / \\
MonoPSR \cite{ku2019monopsr} & 49.32 \% & 58.63 \% & 43.05 \% & 0.2 s / GPU \\
BirdNet \cite{BirdNet2018} & 45.03 \% & 62.69 \% & 41.88 \% & 0.11 s / \\
SparsePool \cite{wang2017fusing} & 43.50 \% & 59.77 \% & 39.36 \% & 0.13 s / 8 cores \\
sensekitti \cite{binyang16craft} & 41.14 \% & 47.48 \% & 35.07 \% & 4.5 s / GPU \\
CG-Stereo \cite{li2020confidence} & 40.64 \% & 60.24 \% & 35.55 \% & 0.57 s / \\
MonoPair \cite{chen2020cvpr} & 39.47 \% & 53.36 \% & 33.95 \% & 0.06 s / GPU \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 35.77 \% & 50.66 \% & 30.96 \% & 0.42 s / GPU \\
Disp R-CNN \cite{sun2020disprcnn} & 35.76 \% & 50.64 \% & 30.95 \% & 0.42 s / GPU \\
Shift R-CNN (mono) \cite{shiftrcnn} & 34.77 \% & 51.95 \% & 31.10 \% & 0.25 s / GPU \\
SparsePool \cite{wang2017fusing} & 34.56 \% & 43.33 \% & 31.09 \% & 0.13 s / 8 cores \\
Point-GNN \cite{shi2020pointgnn} & 32.37 \% & 36.29 \% & 29.81 \% & 0.6 s / GPU \\
D4LCN \cite{ding2019learning} & 31.70 \% & 48.03 \% & 26.99 \% & 0.2 s / GPU \\
M3D-RPN \cite{brazil2019m3drpn} & 31.09 \% & 48.11 \% & 26.10 \% & 0.16 s / GPU \\
SS3D \cite{DBLPjournalscorrabs190608070} & 27.79 \% & 42.95 \% & 24.26 \% & 48 ms / \\
DSGN \cite{Chen2020dsgn} & 20.28 \% & 29.76 \% & 19.13 \% & 0.67 s / \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 19.15 \% & 26.05 \% & 18.02 \% & 10 s / 4 cores \\
OC Stereo \cite{pon2020object} & 18.99 \% & 29.07 \% & 16.40 \% & 0.35 s / 1 core \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 18.92 \% & 27.97 \% & 17.43 \% & 8 s / 1 core \\
RefinedMPL \cite{vianney2019refinedmpl} & 16.02 \% & 26.54 \% & 13.20 \% & 0.15 s / GPU \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 14.64 \% & 23.93 \% & 13.09 \% & 15 s / 4 cores \\
RT3DStereo \cite{Koenigshof2019Objects} & 3.88 \% & 5.46 \% & 3.54 \% & 0.08 s / GPU
\end{tabular}