\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
Deformable PV-RCNN \cite{bhattacharyya2020deformable} & 81.46 \% & 88.25 \% & 76.96 \% & 0.08 s / 1 core \\
MMLab PV-RCNN \cite{shi2020pv} & 81.43 \% & 90.25 \% & 76.82 \% & 0.08 s / 1 core \\
RangeRCNN \cite{liang2020rangercnn} & 81.33 \% & 88.47 \% & 77.09 \% & 0.06 s / GPU \\
CLOCs\_PVCas \cite{pang2020CLOCs} & 80.67 \% & 88.94 \% & 77.15 \% & 0.1 s / 1 core \\
3D-CVF at SPA \cite{3DCVF} & 80.05 \% & 89.20 \% & 73.11 \% & 0.06 s / 1 core \\
SA-SSD \cite{he2020sassd} & 79.79 \% & 88.75 \% & 74.16 \% & 0.04 s / 1 core \\
STD \cite{std2019yang} & 79.71 \% & 87.95 \% & 75.09 \% & 0.08 s / GPU \\
3DSSD \cite{yang3DSSD20} & 79.57 \% & 88.36 \% & 74.55 \% & 0.04 s / GPU \\
Point-GNN \cite{shi2020pointgnn} & 79.47 \% & 88.33 \% & 72.29 \% & 0.6 s / GPU \\
EPNet \cite{huang2020epnet} & 79.28 \% & 89.81 \% & 74.59 \% & 0.1 s / 1 core \\
3D IoU-Net \cite{Li20203DIoUNet} & 79.03 \% & 87.96 \% & 72.78 \% & 0.1 s / 1 core \\
SERCNN \cite{zhou2020joint} & 78.96 \% & 87.74 \% & 74.30 \% & 0.1 s / 1 core \\
MMLab-PartA^2 \cite{shi2020part} & 78.49 \% & 87.81 \% & 73.51 \% & 0.08 s / GPU \\
CLOCs\_SecCas \cite{pang2020CLOCs} & 78.45 \% & 86.38 \% & 72.45 \% & 0.1 s / 1 core \\
Patches - EMP \cite{lehner2019patch} & 78.41 \% & 89.84 \% & 73.15 \% & 0.5 s / GPU \\
HotSpotNet \cite{chen2020object} & 78.31 \% & 87.60 \% & 73.34 \% & 0.04 s / 1 core \\
CenterNet3DV1.5 \cite{wang2020centernet3dan} & 78.08 \% & 86.22 \% & 73.21 \% & 0.04 s / 1 core \\
CenterNet3D \cite{2007.07214} & 77.90 \% & 86.20 \% & 73.03 \% & 0.04 s / GPU \\
UberATG-MMF \cite{Liang2019CVPR} & 77.43 \% & 88.40 \% & 70.22 \% & 0.08 s / GPU \\
Associate-3Ddet \cite{Du2020CVPR} & 77.40 \% & 85.99 \% & 70.53 \% & 0.05 s / 1 core \\
Fast Point R-CNN \cite{Chen2019fastpointrcnn} & 77.40 \% & 85.29 \% & 70.24 \% & 0.06 s / GPU \\
Patches \cite{lehner2019patch} & 77.20 \% & 88.67 \% & 71.82 \% & 0.15 s / GPU \\
HRI-VoxelFPN \cite{Kuang2020voxelFPN} & 76.70 \% & 85.64 \% & 69.44 \% & 0.02 s / GPU \\
SARPNET \cite{ye2019sarpnet} & 76.64 \% & 85.63 \% & 71.31 \% & 0.05 s / 1 core \\
3D IoU Loss \cite{zhou2019} & 76.50 \% & 86.16 \% & 71.39 \% & 0.08 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 76.39 \% & 87.36 \% & 66.69 \% & 0.47 s / GPU \\
SegVoxelNet \cite{yi2020SegVoxelNet} & 76.13 \% & 86.04 \% & 70.76 \% & 0.04 s / 1 core \\
TANet \cite{liu2019tanet} & 75.94 \% & 84.39 \% & 68.82 \% & 0.035s / GPU \\
PointRGCN \cite{Zarzar2019PointRGCNGC} & 75.73 \% & 85.97 \% & 70.60 \% & 0.26 s / GPU \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 75.64 \% & 86.96 \% & 70.70 \% & 0.1 s / GPU \\
AB3DMOT \cite{Weng2019} & 75.43 \% & 86.10 \% & 68.88 \% & 0.0047s / 1 core \\
R-GCN \cite{Zarzar2019PointRGCNGC} & 75.26 \% & 83.42 \% & 68.73 \% & 0.16 s / GPU \\
epBRM \cite{arxiv} & 75.15 \% & 85.00 \% & 69.84 \% & 0.1 s / GPU \\
3DBN\_2 \cite{ERROR: Wrong syntax in BIBTEX file.} & 75.06 \% & 84.90 \% & 72.10 \% & 0.12 s / 1 core \\
MAFF-Net(DAF-Pillar) \cite{zhang2020maffnet} & 75.04 \% & 85.52 \% & 67.61 \% & 0.04 s / 1 core \\
PI-RCNN \cite{xie2020pi} & 74.82 \% & 84.37 \% & 70.03 \% & 0.1 s / 1 core \\
PointPillars \cite{lang2018pointpillars} & 74.31 \% & 82.58 \% & 68.99 \% & 16 ms / \\
ARPNET \cite{Ye2019} & 74.04 \% & 84.69 \% & 68.64 \% & 0.08 s / GPU \\
VOXEL\_FPN\_HR \cite{ERROR: Wrong syntax in BIBTEX file.} & 73.98 \% & 85.33 \% & 68.67 \% & 0.12 s / 8 cores \\
PC-CNN-V2 \cite{8461232} & 73.79 \% & 85.57 \% & 65.65 \% & 0.5 s / GPU \\
C-GCN \cite{Zarzar2019PointRGCNGC} & 73.62 \% & 83.49 \% & 67.01 \% & 0.147 s / GPU \\
3DBN \cite{DBLPjournalscorrabs190108373} & 73.53 \% & 83.77 \% & 66.23 \% & 0.13s / \\
SCNet \cite{8813061} & 73.17 \% & 83.34 \% & 67.93 \% & 0.04 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 71.76 \% & 83.07 \% & 65.73 \% & 0.1 s / \\
PointPainting \cite{vora2019pointpainting} & 71.70 \% & 82.11 \% & 67.08 \% & 0.4 s / GPU \\
WS3D \cite{meng2020eccv} & 70.59 \% & 80.99 \% & 64.23 \% & 0.1 s / GPU \\
F-PointNet \cite{qi2017frustum} & 69.79 \% & 82.19 \% & 60.59 \% & 0.17 s / GPU \\
UberATG-ContFuse \cite{Liang2018ECCV} & 68.78 \% & 83.68 \% & 61.67 \% & 0.06 s / GPU \\
MLOD \cite{deng2019mlod} & 67.76 \% & 77.24 \% & 62.05 \% & 0.12 s / GPU \\
AVOD \cite{ku2018joint} & 66.47 \% & 76.39 \% & 60.23 \% & 0.08 s / \\
MV3D \cite{Chen2017CVPR} & 63.63 \% & 74.97 \% & 54.00 \% & 0.36 s / GPU \\
A3DODWTDA \cite{erino397fregu856master2018} & 56.82 \% & 62.84 \% & 48.12 \% & 0.08 s / GPU \\
PL++ (SDN+GDC) \cite{you2020pseudolidar} & 54.88 \% & 68.38 \% & 49.16 \% & 0.6 s / GPU \\
MV3D (LIDAR) \cite{Chen2017CVPR} & 54.54 \% & 68.35 \% & 49.16 \% & 0.24 s / GPU \\
CG-Stereo \cite{li2020confidence} & 53.58 \% & 74.39 \% & 46.50 \% & 0.57 s / \\
DSGN \cite{Chen2020dsgn} & 52.18 \% & 73.50 \% & 45.14 \% & 0.67 s / \\
SF \cite{ERROR: Wrong syntax in BIBTEX file.} & 51.92 \% & 58.88 \% & 44.59 \% & 0.5 s / 1 core \\
BirdNet+ \cite{Barrera2020} & 51.85 \% & 70.14 \% & 50.03 \% & 0.1 s / \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 47.34 \% & 55.93 \% & 42.60 \% & 0.06 s / GPU \\
Pseudo-LiDAR++ \cite{you2020pseudolidar} & 42.43 \% & 61.11 \% & 36.99 \% & 0.4 s / GPU \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 39.36 \% & 59.61 \% & 32.01 \% & 0.42 s / GPU \\
ZoomNet \cite{xu2020zoomnet} & 38.64 \% & 55.98 \% & 30.97 \% & 0.3 s / 1 core \\
Disp R-CNN \cite{sun2020disprcnn} & 37.93 \% & 58.55 \% & 31.95 \% & 0.42 s / GPU \\
OC Stereo \cite{pon2020object} & 37.60 \% & 55.15 \% & 30.25 \% & 0.35 s / 1 core \\
Pseudo-Lidar \cite{Wang2019CVPR} & 34.05 \% & 54.53 \% & 28.25 \% & 0.4 s / GPU \\
Stereo R-CNN \cite{licvpr2019} & 30.23 \% & 47.58 \% & 23.72 \% & 0.3 s / GPU \\
BirdNet \cite{BirdNet2018} & 27.26 \% & 40.99 \% & 25.32 \% & 0.11 s / \\
RT3DStereo \cite{Koenigshof2019Objects} & 23.28 \% & 29.90 \% & 18.96 \% & 0.08 s / GPU \\
RT3D \cite{8403277} & 19.14 \% & 23.74 \% & 18.86 \% & 0.09 s / GPU \\
StereoFENet \cite{monofenet} & 18.41 \% & 29.14 \% & 14.20 \% & 0.15 s / 1 core \\
Kinematic3D \cite{brazil2020kinematic} & 12.72 \% & 19.07 \% & 9.17 \% & 0.12 s / 1 core \\
D4LCN \cite{ding2019learning} & 11.72 \% & 16.65 \% & 9.51 \% & 0.2 s / GPU \\
RefinedMPL \cite{vianney2019refinedmpl} & 11.14 \% & 18.09 \% & 8.94 \% & 0.15 s / GPU \\
PatchNet \cite{Ma2020ECCV} & 11.12 \% & 15.68 \% & 10.17 \% & 0.4 s / 1 core \\
AM3D \cite{ma2019accurate} & 10.74 \% & 16.50 \% & 9.52 \% & 0.4 s / GPU \\
RTM3D \cite{li2020rtm3d} & 10.34 \% & 14.41 \% & 8.77 \% & 0.05 s / GPU \\
MonoPair \cite{chen2020cvpr} & 9.99 \% & 13.04 \% & 8.65 \% & 0.06 s / GPU \\
SMOKE \cite{liu2020smoke} & 9.76 \% & 14.03 \% & 7.84 \% & 0.03 s / GPU \\
M3D-RPN \cite{brazil2019m3drpn} & 9.71 \% & 14.76 \% & 7.42 \% & 0.16 s / GPU \\
TopNet-HighRes \cite{8569433} & 9.28 \% & 12.67 \% & 7.95 \% & 101ms / \\
SS3D \cite{DBLPjournalscorrabs190608070} & 7.68 \% & 10.78 \% & 6.51 \% & 48 ms / \\
Mono3D\_PLiDAR \cite{Weng2019} & 7.50 \% & 10.76 \% & 6.10 \% & 0.1 s / \\
MonoPSR \cite{ku2019monopsr} & 7.25 \% & 10.76 \% & 5.85 \% & 0.2 s / GPU \\
Decoupled-3D \cite{cai2020monocular} & 7.02 \% & 11.08 \% & 5.63 \% & 0.08 s / GPU \\
VoxelJones \cite{motro2019vehicular} & 6.35 \% & 7.39 \% & 5.80 \% & .18 s / 1 core \\
MonoGRNet \cite{qin2019monogrnet} & 5.74 \% & 9.61 \% & 4.25 \% & 0.04s / \\
A3DODWTDA (image) \cite{erino397fregu856master2018} & 5.27 \% & 6.88 \% & 4.45 \% & 0.8 s / GPU \\
MonoFENet \cite{monofenet} & 5.14 \% & 8.35 \% & 4.10 \% & 0.15 s / 1 core \\
TLNet (Stereo) \cite{qin2019tlnet} & 4.37 \% & 7.64 \% & 3.74 \% & 0.1 s / 1 core \\
CSoR \cite{Plotkin2015} & 4.06 \% & 5.61 \% & 3.17 \% & 3.5 s / 4 cores \\
Shift R-CNN (mono) \cite{shiftrcnn} & 3.87 \% & 6.88 \% & 2.83 \% & 0.25 s / GPU \\
MVRA + I-FRCNN+ \cite{Choi2019ICCV} & 3.27 \% & 5.19 \% & 2.49 \% & 0.18 s / GPU \\
TopNet-UncEst \cite{wirges2019capturing} & 3.02 \% & 3.24 \% & 2.26 \% & 0.09 s / \\
GS3D \cite{li2019gs3d} & 2.90 \% & 4.47 \% & 2.47 \% & 2 s / 1 core \\
3D-GCK \cite{gahlert2020single} & 2.52 \% & 3.27 \% & 2.11 \% & 24 ms / \\
ROI-10D \cite{manhardt2018roi10d} & 2.02 \% & 4.32 \% & 1.46 \% & 0.2 s / GPU \\
FQNet \cite{liu2019deep} & 1.51 \% & 2.77 \% & 1.01 \% & 0.5 s / 1 core \\
3D-SSMFCNN \cite{novakmaster2017} & 1.41 \% & 1.88 \% & 1.11 \% & 0.1 s / GPU \\
mBoW \cite{Behley2013IROS} & 0.00 \% & 0.00 \% & 0.00 \% & 10 s / 1 core
\end{tabular}