\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
HotSpotNet \cite{chen2020object} & 45.37 \% & 53.10 \% & 41.47 \% & 0.04 s / 1 core \\
TANet \cite{liu2019tanet} & 44.34 \% & 53.72 \% & 40.49 \% & 0.035s / GPU \\
3DSSD \cite{yang3DSSD20} & 44.27 \% & 54.64 \% & 40.23 \% & 0.04 s / GPU \\
Point-GNN \cite{shi2020pointgnn} & 43.77 \% & 51.92 \% & 40.14 \% & 0.6 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 43.38 \% & 52.16 \% & 38.80 \% & 0.47 s / GPU \\
MMLab-PartA^2 \cite{shi2020part} & 43.35 \% & 53.10 \% & 40.06 \% & 0.08 s / GPU \\
MMLab PV-RCNN \cite{shi2020pv} & 43.29 \% & 52.17 \% & 40.29 \% & 0.08 s / 1 core \\
VMVS \cite{ku2018joint} & 43.27 \% & 53.44 \% & 39.51 \% & 0.25 s / GPU \\
STD \cite{std2019yang} & 42.47 \% & 53.29 \% & 38.35 \% & 0.08 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 42.27 \% & 50.46 \% & 39.04 \% & 0.1 s / \\
SemanticVoxels \cite{fei2020semanticvoxels} & 42.19 \% & 50.90 \% & 39.52 \% & 0.04 s / GPU \\
F-PointNet \cite{qi2017frustum} & 42.15 \% & 50.53 \% & 38.08 \% & 0.17 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 41.92 \% & 51.45 \% & 38.89 \% & 16 ms / \\
epBRM \cite{arxiv} & 41.52 \% & 49.17 \% & 39.08 \% & 0.10 s / 1 core \\
PointPainting \cite{vora2019pointpainting} & 40.97 \% & 50.32 \% & 37.87 \% & 0.4 s / GPU \\
Deformable PV-RCNN \cite{bhattacharyya2020deformable} & 40.89 \% & 46.97 \% & 38.80 \% & 0.08 s / 1 core \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 39.37 \% & 47.98 \% & 36.01 \% & 0.1 s / GPU \\
ARPNET \cite{Ye2019} & 39.31 \% & 48.32 \% & 35.93 \% & 0.08 s / GPU \\
SCNet \cite{8813061} & 38.66 \% & 47.83 \% & 35.70 \% & 0.04 s / GPU \\
3DBN\_2 \cite{ERROR: Wrong syntax in BIBTEX file.} & 38.23 \% & 46.79 \% & 35.57 \% & 0.12 s / 1 core \\
MLOD \cite{deng2019mlod} & 37.47 \% & 47.58 \% & 35.07 \% & 0.12 s / GPU \\
VOXEL\_FPN\_HR \cite{ERROR: Wrong syntax in BIBTEX file.} & 37.01 \% & 46.32 \% & 34.67 \% & 0.12 s / 8 cores \\
AB3DMOT \cite{Weng2019} & 34.59 \% & 42.27 \% & 31.37 \% & 0.0047s / 1 core \\
BirdNet+ \cite{Barrera2020} & 31.46 \% & 37.99 \% & 29.46 \% & 0.1 s / \\
SparsePool \cite{wang2017fusing} & 30.38 \% & 37.84 \% & 26.94 \% & 0.13 s / 8 cores \\
SparsePool \cite{wang2017fusing} & 27.92 \% & 35.52 \% & 25.87 \% & 0.13 s / 8 cores \\
AVOD \cite{ku2018joint} & 27.86 \% & 36.10 \% & 25.76 \% & 0.08 s / \\
CSW3D \cite{hu2019csw3d} & 26.64 \% & 33.75 \% & 23.34 \% & 0.03 s / 4 cores \\
SF \cite{ERROR: Wrong syntax in BIBTEX file.} & 24.84 \% & 31.61 \% & 21.96 \% & 0.5 s / 1 core \\
CG-Stereo \cite{li2020confidence} & 24.31 \% & 33.22 \% & 20.95 \% & 0.57 s / \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 21.98 \% & 30.98 \% & 18.68 \% & 0.42 s / GPU \\
Disp R-CNN \cite{sun2020disprcnn} & 21.98 \% & 31.05 \% & 18.67 \% & 0.42 s / GPU \\
OC Stereo \cite{pon2020object} & 17.58 \% & 24.48 \% & 15.60 \% & 0.35 s / 1 core \\
BirdNet \cite{BirdNet2018} & 17.08 \% & 22.04 \% & 15.82 \% & 0.11 s / \\
DSGN \cite{Chen2020dsgn} & 15.55 \% & 20.53 \% & 14.15 \% & 0.67 s / \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 13.96 \% & 17.60 \% & 12.70 \% & 0.06 s / GPU \\
RefinedMPL \cite{vianney2019refinedmpl} & 7.18 \% & 11.14 \% & 5.84 \% & 0.15 s / GPU \\
TopNet-HighRes \cite{8569433} & 6.92 \% & 10.40 \% & 6.63 \% & 101ms / \\
MonoPair \cite{chen2020cvpr} & 6.68 \% & 10.02 \% & 5.53 \% & 0.06 s / GPU \\
Shift R-CNN (mono) \cite{shiftrcnn} & 4.66 \% & 7.95 \% & 4.16 \% & 0.25 s / GPU \\
MonoPSR \cite{ku2019monopsr} & 4.00 \% & 6.12 \% & 3.30 \% & 0.2 s / GPU \\
M3D-RPN \cite{brazil2019m3drpn} & 3.48 \% & 4.92 \% & 2.94 \% & 0.16 s / GPU \\
D4LCN \cite{ding2019learning} & 3.42 \% & 4.55 \% & 2.83 \% & 0.2 s / GPU \\
RT3DStereo \cite{Koenigshof2019Objects} & 2.45 \% & 3.28 \% & 2.35 \% & 0.08 s / GPU \\
TopNet-UncEst \cite{wirges2019capturing} & 1.87 \% & 3.42 \% & 1.73 \% & 0.09 s / \\
SS3D \cite{DBLPjournalscorrabs190608070} & 1.78 \% & 2.31 \% & 1.48 \% & 48 ms / \\
mBoW \cite{Behley2013IROS} & 0.00 \% & 0.00 \% & 0.00 \% & 10 s / 1 core
\end{tabular}