\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
Deformable PV-RCNN \cite{bhattacharyya2020deformable} & 68.54 \% & 82.19 \% & 61.33 \% & 0.08 s / 1 core \\
HotSpotNet \cite{chen2020object} & 65.95 \% & 82.59 \% & 59.00 \% & 0.04 s / 1 core \\
F-ConvNet \cite{wang2019frustum} & 65.07 \% & 81.98 \% & 56.54 \% & 0.47 s / GPU \\
3DSSD \cite{yang3DSSD20} & 64.10 \% & 82.48 \% & 56.90 \% & 0.04 s / GPU \\
PointPainting \cite{vora2019pointpainting} & 63.78 \% & 77.63 \% & 55.89 \% & 0.4 s / GPU \\
MMLab PV-RCNN \cite{shi2020pv} & 63.71 \% & 78.60 \% & 57.65 \% & 0.08 s / 1 core \\
MMLab-PartA^2 \cite{shi2020part} & 63.52 \% & 79.17 \% & 56.93 \% & 0.08 s / GPU \\
Point-GNN \cite{shi2020pointgnn} & 63.48 \% & 78.60 \% & 57.08 \% & 0.6 s / GPU \\
VOXEL\_FPN\_HR \cite{ERROR: Wrong syntax in BIBTEX file.} & 61.91 \% & 78.29 \% & 55.54 \% & 0.12 s / 8 cores \\
STD \cite{std2019yang} & 61.59 \% & 78.69 \% & 55.30 \% & 0.08 s / GPU \\
3DBN\_2 \cite{ERROR: Wrong syntax in BIBTEX file.} & 60.88 \% & 78.10 \% & 54.10 \% & 0.12 s / 1 core \\
AB3DMOT \cite{Weng2019} & 60.30 \% & 75.42 \% & 53.81 \% & 0.0047s / 1 core \\
TANet \cite{liu2019tanet} & 59.44 \% & 75.70 \% & 52.53 \% & 0.035s / GPU \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 58.82 \% & 74.96 \% & 52.53 \% & 0.1 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 58.65 \% & 77.10 \% & 51.92 \% & 16 ms / \\
ARPNET \cite{Ye2019} & 58.20 \% & 74.21 \% & 52.13 \% & 0.08 s / GPU \\
epBRM \cite{arxiv} & 56.13 \% & 72.08 \% & 49.91 \% & 0.10 s / 1 core \\
F-PointNet \cite{qi2017frustum} & 56.12 \% & 72.27 \% & 49.01 \% & 0.17 s / GPU \\
SCNet \cite{8813061} & 50.79 \% & 67.98 \% & 45.15 \% & 0.04 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 50.55 \% & 63.76 \% & 44.93 \% & 0.1 s / \\
MLOD \cite{deng2019mlod} & 49.43 \% & 68.81 \% & 42.84 \% & 0.12 s / GPU \\
BirdNet+ \cite{Barrera2020} & 47.72 \% & 67.38 \% & 42.89 \% & 0.1 s / \\
AVOD \cite{ku2018joint} & 42.08 \% & 57.19 \% & 38.29 \% & 0.08 s / \\
SparsePool \cite{wang2017fusing} & 37.33 \% & 52.61 \% & 33.39 \% & 0.13 s / 8 cores \\
SparsePool \cite{wang2017fusing} & 32.61 \% & 40.87 \% & 29.05 \% & 0.13 s / 8 cores \\
CG-Stereo \cite{li2020confidence} & 30.89 \% & 47.40 \% & 27.23 \% & 0.57 s / \\
BirdNet \cite{BirdNet2018} & 30.25 \% & 43.98 \% & 27.21 \% & 0.11 s / \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 23.75 \% & 39.72 \% & 20.47 \% & 0.42 s / GPU \\
Disp R-CNN \cite{sun2020disprcnn} & 23.75 \% & 39.72 \% & 20.47 \% & 0.42 s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 18.53 \% & 24.27 \% & 17.31 \% & 0.06 s / GPU \\
DSGN \cite{Chen2020dsgn} & 18.17 \% & 27.76 \% & 16.21 \% & 0.67 s / \\
OC Stereo \cite{pon2020object} & 16.63 \% & 29.40 \% & 14.72 \% & 0.35 s / 1 core \\
MonoPSR \cite{ku2019monopsr} & 4.74 \% & 8.37 \% & 3.68 \% & 0.2 s / GPU \\
TopNet-UncEst \cite{wirges2019capturing} & 4.54 \% & 7.13 \% & 3.81 \% & 0.09 s / \\
RT3DStereo \cite{Koenigshof2019Objects} & 3.37 \% & 5.29 \% & 2.57 \% & 0.08 s / GPU \\
MonoPair \cite{chen2020cvpr} & 2.12 \% & 3.79 \% & 1.83 \% & 0.06 s / GPU \\
RefinedMPL \cite{vianney2019refinedmpl} & 1.82 \% & 3.23 \% & 1.77 \% & 0.15 s / GPU \\
TopNet-HighRes \cite{8569433} & 1.67 \% & 2.49 \% & 1.88 \% & 101ms / \\
D4LCN \cite{ding2019learning} & 1.67 \% & 2.45 \% & 1.36 \% & 0.2 s / GPU \\
SS3D \cite{DBLPjournalscorrabs190608070} & 1.45 \% & 2.80 \% & 1.35 \% & 48 ms / \\
M3D-RPN \cite{brazil2019m3drpn} & 0.65 \% & 0.94 \% & 0.47 \% & 0.16 s / GPU \\
Shift R-CNN (mono) \cite{shiftrcnn} & 0.29 \% & 0.48 \% & 0.31 \% & 0.25 s / GPU \\
mBoW \cite{Behley2013IROS} & 0.00 \% & 0.00 \% & 0.00 \% & 10 s / 1 core
\end{tabular}