\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
Deformable PV-RCNN \cite{bhattacharyya2020deformable} & 72.61 \% & 83.93 \% & 65.82 \% & 0.08 s / 1 core \\
PointPainting \cite{vora2019pointpainting} & 71.54 \% & 83.91 \% & 62.97 \% & 0.4 s / GPU \\
HVNet \cite{ye2020hvnet} & 71.17 \% & 83.97 \% & 63.65 \% & 0.03 s / GPU \\
MMLab PV-RCNN \cite{shi2020pv} & 68.89 \% & 82.49 \% & 62.41 \% & 0.08 s / 1 core \\
F-ConvNet \cite{wang2019frustum} & 68.88 \% & 84.16 \% & 60.05 \% & 0.47 s / GPU \\
MMLab-PartA^2 \cite{shi2020part} & 68.73 \% & 83.43 \% & 61.85 \% & 0.08 s / GPU \\
HotSpotNet \cite{chen2020object} & 68.51 \% & 83.29 \% & 61.84 \% & 0.04 s / 1 core \\
3DSSD \cite{yang3DSSD20} & 67.62 \% & 85.04 \% & 61.14 \% & 0.04 s / GPU \\
Point-GNN \cite{shi2020pointgnn} & 67.28 \% & 81.17 \% & 59.67 \% & 0.6 s / GPU \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 67.24 \% & 82.56 \% & 60.28 \% & 0.1 s / GPU \\
STD \cite{std2019yang} & 67.23 \% & 81.36 \% & 59.35 \% & 0.08 s / GPU \\
ARPNET \cite{Ye2019} & 66.39 \% & 82.32 \% & 58.80 \% & 0.08 s / GPU \\
AB3DMOT \cite{Weng2019} & 65.85 \% & 80.00 \% & 58.69 \% & 0.0047s / 1 core \\
VOXEL\_FPN\_HR \cite{ERROR: Wrong syntax in BIBTEX file.} & 65.02 \% & 81.07 \% & 58.44 \% & 0.12 s / 8 cores \\
3DBN\_2 \cite{ERROR: Wrong syntax in BIBTEX file.} & 64.28 \% & 81.06 \% & 57.55 \% & 0.12 s / 1 core \\
TANet \cite{liu2019tanet} & 63.77 \% & 79.16 \% & 56.21 \% & 0.035s / GPU \\
PointPillars \cite{lang2018pointpillars} & 62.73 \% & 79.90 \% & 55.58 \% & 16 ms / \\
F-PointNet \cite{qi2017frustum} & 61.37 \% & 77.26 \% & 53.78 \% & 0.17 s / GPU \\
epBRM \cite{arxiv} & 59.79 \% & 75.13 \% & 53.36 \% & 0.10 s / 1 core \\
AVOD-FPN \cite{ku2018joint} & 57.12 \% & 69.39 \% & 51.09 \% & 0.1 s / \\
SCNet \cite{8813061} & 56.39 \% & 73.73 \% & 49.99 \% & 0.04 s / GPU \\
MLOD \cite{deng2019mlod} & 55.06 \% & 73.03 \% & 48.21 \% & 0.12 s / GPU \\
BirdNet+ \cite{Barrera2020} & 52.15 \% & 72.45 \% & 46.57 \% & 0.1 s / \\
AVOD \cite{ku2018joint} & 48.15 \% & 64.11 \% & 42.37 \% & 0.08 s / \\
BirdNet \cite{BirdNet2018} & 41.56 \% & 58.64 \% & 36.94 \% & 0.11 s / \\
SparsePool \cite{wang2017fusing} & 40.74 \% & 56.52 \% & 36.68 \% & 0.13 s / 8 cores \\
TopNet-Retina \cite{8569433} & 36.83 \% & 47.48 \% & 33.58 \% & 52ms / \\
CG-Stereo \cite{li2020confidence} & 36.25 \% & 55.33 \% & 32.17 \% & 0.57 s / \\
SparsePool \cite{wang2017fusing} & 35.24 \% & 43.55 \% & 30.15 \% & 0.13 s / 8 cores \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 26.46 \% & 43.41 \% & 22.46 \% & 0.42 s / GPU \\
Disp R-CNN \cite{sun2020disprcnn} & 26.46 \% & 43.41 \% & 22.46 \% & 0.42 s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 25.43 \% & 32.00 \% & 22.88 \% & 0.06 s / GPU \\
DSGN \cite{Chen2020dsgn} & 21.04 \% & 31.23 \% & 18.93 \% & 0.67 s / \\
OC Stereo \cite{pon2020object} & 19.23 \% & 32.47 \% & 17.11 \% & 0.35 s / 1 core \\
TopNet-DecayRate \cite{8569433} & 16.00 \% & 23.02 \% & 13.24 \% & 92 ms / \\
TopNet-UncEst \cite{wirges2019capturing} & 9.18 \% & 12.31 \% & 8.14 \% & 0.09 s / \\
TopNet-HighRes \cite{8569433} & 6.48 \% & 9.99 \% & 6.76 \% & 101ms / \\
MonoPSR \cite{ku2019monopsr} & 5.78 \% & 9.87 \% & 4.57 \% & 0.2 s / GPU \\
RT3DStereo \cite{Koenigshof2019Objects} & 4.10 \% & 7.03 \% & 3.88 \% & 0.08 s / GPU \\
MonoPair \cite{chen2020cvpr} & 2.87 \% & 4.76 \% & 2.42 \% & 0.06 s / GPU \\
RefinedMPL \cite{vianney2019refinedmpl} & 2.42 \% & 4.23 \% & 2.14 \% & 0.15 s / GPU \\
SS3D \cite{DBLPjournalscorrabs190608070} & 1.89 \% & 3.45 \% & 1.44 \% & 48 ms / \\
D4LCN \cite{ding2019learning} & 1.82 \% & 2.72 \% & 1.79 \% & 0.2 s / GPU \\
M3D-RPN \cite{brazil2019m3drpn} & 0.81 \% & 1.25 \% & 0.78 \% & 0.16 s / GPU \\
Shift R-CNN (mono) \cite{shiftrcnn} & 0.38 \% & 0.76 \% & 0.41 \% & 0.25 s / GPU \\
mBoW \cite{Behley2013IROS} & 0.00 \% & 0.00 \% & 0.00 \% & 10 s / 1 core
\end{tabular}