\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
SA-SSD \cite{he2020sassd} & 91.03 \% & 95.03 \% & 85.96 \% & 0.04 s / 1 core \\
MMLab PV-RCNN \cite{shi2020pv} & 90.65 \% & 94.98 \% & 86.14 \% & 0.08 s / 1 core \\
Deformable PV-RCNN \cite{bhattacharyya2020deformable} & 90.13 \% & 92.42 \% & 85.93 \% & 0.08 s / 1 core \\
CLOCs\_PVCas \cite{pang2020CLOCs} & 89.80 \% & 93.05 \% & 86.57 \% & 0.1 s / 1 core \\
3D-CVF at SPA \cite{3DCVF} & 89.56 \% & 93.52 \% & 82.45 \% & 0.06 s / 1 core \\
scssd-normal(0.3) \cite{scssd} & 89.54 \% & 95.26 \% & 82.31 \% & 0.05 s / GPU \\
scssd-normal(0.4) \cite{scssd} & 89.38 \% & 94.91 \% & 84.29 \% & 0.05 s / 1 core \\
STD \cite{std2019yang} & 89.19 \% & 94.74 \% & 86.42 \% & 0.08 s / GPU \\
Point-GNN \cite{shi2020pointgnn} & 89.17 \% & 93.11 \% & 83.90 \% & 0.6 s / GPU \\
3DSSD \cite{yang3DSSD20} & 89.02 \% & 92.66 \% & 85.86 \% & 0.04 s / GPU \\
HVNet \cite{ye2020hvnet} & 88.82 \% & 92.83 \% & 83.38 \% & 0.03 s / GPU \\
CenterNet3DV1.5 \cite{wang2020centernet3dan} & 88.51 \% & 91.78 \% & 85.50 \% & 0.04 s / 1 core \\
EPNet \cite{huang2020epnet} & 88.47 \% & 94.22 \% & 83.69 \% & 0.1 s / 1 core \\
CenterNet3D \cite{2007.07214} & 88.46 \% & 91.80 \% & 83.62 \% & 0.04 s / GPU \\
RangeRCNN \cite{liang2020rangercnn} & 88.40 \% & 92.15 \% & 85.74 \% & 0.06 s / GPU \\
Patches \cite{lehner2019patch} & 88.39 \% & 92.72 \% & 83.19 \% & 0.15 s / GPU \\
3D IoU-Net \cite{Li20203DIoUNet} & 88.38 \% & 94.76 \% & 81.93 \% & 0.1 s / 1 core \\
CLOCs\_SecCas \cite{pang2020CLOCs} & 88.23 \% & 91.16 \% & 82.63 \% & 0.1 s / 1 core \\
UberATG-MMF \cite{Liang2019CVPR} & 88.21 \% & 93.67 \% & 81.99 \% & 0.08 s / GPU \\
Patches - EMP \cite{lehner2019patch} & 88.17 \% & 94.49 \% & 84.75 \% & 0.5 s / GPU \\
PointPainting \cite{vora2019pointpainting} & 88.11 \% & 92.45 \% & 83.36 \% & 0.4 s / GPU \\
SERCNN \cite{zhou2020joint} & 88.10 \% & 94.11 \% & 83.43 \% & 0.1 s / 1 core \\
Associate-3Ddet \cite{Du2020CVPR} & 88.09 \% & 91.40 \% & 82.96 \% & 0.05 s / 1 core \\
HotSpotNet \cite{chen2020object} & 88.09 \% & 94.06 \% & 83.24 \% & 0.04 s / 1 core \\
UberATG-HDNET \cite{Yang2018CoRL} & 87.98 \% & 93.13 \% & 81.23 \% & 0.05 s / GPU \\
Fast Point R-CNN \cite{Chen2019fastpointrcnn} & 87.84 \% & 90.87 \% & 80.52 \% & 0.06 s / GPU \\
MMLab-PartA^2 \cite{shi2020part} & 87.79 \% & 91.70 \% & 84.61 \% & 0.08 s / GPU \\
MODet \cite{zhang2019accurate} & 87.56 \% & 90.80 \% & 82.69 \% & 0.05 s / \\
AB3DMOT \cite{Weng2019} & 87.53 \% & 91.99 \% & 81.03 \% & 0.0047s / 1 core \\
PointRGCN \cite{Zarzar2019PointRGCNGC} & 87.49 \% & 91.63 \% & 80.73 \% & 0.26 s / GPU \\
PC-CNN-V2 \cite{8461232} & 87.40 \% & 91.19 \% & 79.35 \% & 0.5 s / GPU \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 87.39 \% & 92.13 \% & 82.72 \% & 0.1 s / GPU \\
MAFF-Net(DAF-Pillar) \cite{zhang2020maffnet} & 87.34 \% & 90.79 \% & 77.66 \% & 0.04 s / 1 core \\
HRI-VoxelFPN \cite{Kuang2020voxelFPN} & 87.21 \% & 92.75 \% & 79.82 \% & 0.02 s / GPU \\
epBRM \cite{arxiv} & 87.13 \% & 90.70 \% & 81.92 \% & 0.1 s / GPU \\
SARPNET \cite{ye2019sarpnet} & 86.92 \% & 92.21 \% & 81.68 \% & 0.05 s / 1 core \\
ARPNET \cite{Ye2019} & 86.81 \% & 90.06 \% & 79.41 \% & 0.08 s / GPU \\
C-GCN \cite{Zarzar2019PointRGCNGC} & 86.78 \% & 91.11 \% & 80.09 \% & 0.147 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 86.56 \% & 90.07 \% & 82.81 \% & 16 ms / \\
TANet \cite{liu2019tanet} & 86.54 \% & 91.58 \% & 81.19 \% & 0.035s / GPU \\
SCNet \cite{8813061} & 86.48 \% & 90.07 \% & 81.30 \% & 0.04 s / GPU \\
SegVoxelNet \cite{yi2020SegVoxelNet} & 86.37 \% & 91.62 \% & 83.04 \% & 0.04 s / 1 core \\
VOXEL\_FPN\_HR \cite{ERROR: Wrong syntax in BIBTEX file.} & 86.36 \% & 90.28 \% & 81.20 \% & 0.12 s / 8 cores \\
3D IoU Loss \cite{zhou2019} & 86.22 \% & 91.36 \% & 81.20 \% & 0.08 s / GPU \\
R-GCN \cite{Zarzar2019PointRGCNGC} & 86.05 \% & 91.91 \% & 81.05 \% & 0.16 s / GPU \\
UberATG-PIXOR++ \cite{Yang2018CoRL} & 86.01 \% & 93.28 \% & 80.11 \% & 0.035 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 85.84 \% & 91.51 \% & 76.11 \% & 0.47 s / GPU \\
PI-RCNN \cite{xie2020pi} & 85.81 \% & 91.44 \% & 81.00 \% & 0.1 s / 1 core \\
UberATG-ContFuse \cite{Liang2018ECCV} & 85.35 \% & 94.07 \% & 75.88 \% & 0.06 s / GPU \\
3DBN\_2 \cite{ERROR: Wrong syntax in BIBTEX file.} & 85.30 \% & 91.37 \% & 82.57 \% & 0.12 s / 1 core \\
AVOD \cite{ku2018joint} & 84.95 \% & 89.75 \% & 78.32 \% & 0.08 s / \\
WS3D \cite{meng2020eccv} & 84.93 \% & 90.96 \% & 77.96 \% & 0.1 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 84.82 \% & 90.99 \% & 79.62 \% & 0.1 s / \\
F-PointNet \cite{qi2017frustum} & 84.67 \% & 91.17 \% & 74.77 \% & 0.17 s / GPU \\
3DBN \cite{DBLPjournalscorrabs190108373} & 83.94 \% & 89.66 \% & 76.50 \% & 0.13s / \\
MLOD \cite{deng2019mlod} & 82.68 \% & 90.25 \% & 77.97 \% & 0.12 s / GPU \\
UberATG-PIXOR \cite{Yang2018CVPR} & 80.01 \% & 83.97 \% & 74.31 \% & 0.035 s / \\
MV3D (LIDAR) \cite{Chen2017CVPR} & 78.98 \% & 86.49 \% & 72.23 \% & 0.24 s / GPU \\
MV3D \cite{Chen2017CVPR} & 78.93 \% & 86.62 \% & 69.80 \% & 0.36 s / GPU \\
LaserNet \cite{lasernet} & 74.52 \% & 79.19 \% & 68.45 \% & 12 ms / GPU \\
PL++ (SDN+GDC) \cite{you2020pseudolidar} & 73.80 \% & 84.61 \% & 65.59 \% & 0.6 s / GPU \\
A3DODWTDA \cite{erino397fregu856master2018} & 73.26 \% & 79.58 \% & 62.77 \% & 0.08 s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 68.96 \% & 77.24 \% & 64.95 \% & 0.06 s / GPU \\
TopNet-Retina \cite{8569433} & 68.16 \% & 80.16 \% & 63.43 \% & 52ms / \\
CG-Stereo \cite{li2020confidence} & 66.44 \% & 85.29 \% & 58.95 \% & 0.57 s / \\
SF \cite{ERROR: Wrong syntax in BIBTEX file.} & 65.74 \% & 74.20 \% & 58.35 \% & 0.5 s / 1 core \\
DSGN \cite{Chen2020dsgn} & 65.05 \% & 82.90 \% & 56.60 \% & 0.67 s / \\
TopNet-DecayRate \cite{8569433} & 64.60 \% & 79.74 \% & 58.04 \% & 92 ms / \\
BirdNet+ \cite{Barrera2020} & 63.33 \% & 84.80 \% & 61.23 \% & 0.1 s / \\
3D FCN \cite{li2017iros} & 61.67 \% & 70.62 \% & 55.61 \% & >5 s / 1 core \\
BirdNet \cite{BirdNet2018} & 59.83 \% & 84.17 \% & 57.35 \% & 0.11 s / \\
TopNet-UncEst \cite{wirges2019capturing} & 59.67 \% & 72.05 \% & 51.67 \% & 0.09 s / \\
Pseudo-LiDAR++ \cite{you2020pseudolidar} & 58.01 \% & 78.31 \% & 51.25 \% & 0.4 s / GPU \\
ZoomNet \cite{xu2020zoomnet} & 54.91 \% & 72.94 \% & 44.14 \% & 0.3 s / 1 core \\
VoxelJones \cite{motro2019vehicular} & 53.96 \% & 66.21 \% & 47.66 \% & .18 s / 1 core \\
TopNet-HighRes \cite{8569433} & 53.05 \% & 67.84 \% & 46.99 \% & 101ms / \\
Disp R-CNN \cite{sun2020disprcnn} & 52.37 \% & 73.87 \% & 43.67 \% & 0.42 s / GPU \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 52.37 \% & 74.12 \% & 43.79 \% & 0.42 s / GPU \\
OC Stereo \cite{pon2020object} & 51.47 \% & 68.89 \% & 42.97 \% & 0.35 s / 1 core \\
RT3DStereo \cite{Koenigshof2019Objects} & 46.82 \% & 58.81 \% & 38.38 \% & 0.08 s / GPU \\
Pseudo-Lidar \cite{Wang2019CVPR} & 45.00 \% & 67.30 \% & 38.40 \% & 0.4 s / GPU \\
RT3D \cite{8403277} & 44.00 \% & 56.44 \% & 42.34 \% & 0.09 s / GPU \\
Stereo R-CNN \cite{licvpr2019} & 41.31 \% & 61.92 \% & 33.42 \% & 0.3 s / GPU \\
StereoFENet \cite{monofenet} & 32.96 \% & 49.29 \% & 25.90 \% & 0.15 s / 1 core \\
RefinedMPL \cite{vianney2019refinedmpl} & 17.60 \% & 28.08 \% & 13.95 \% & 0.15 s / GPU \\
Kinematic3D \cite{brazil2020kinematic} & 17.52 \% & 26.69 \% & 13.10 \% & 0.12 s / 1 core \\
AM3D \cite{ma2019accurate} & 17.32 \% & 25.03 \% & 14.91 \% & 0.4 s / GPU \\
PatchNet \cite{Ma2020ECCV} & 16.86 \% & 22.97 \% & 14.97 \% & 0.4 s / 1 core \\
D4LCN \cite{ding2019learning} & 16.02 \% & 22.51 \% & 12.55 \% & 0.2 s / GPU \\
MonoPair \cite{chen2020cvpr} & 14.83 \% & 19.28 \% & 12.89 \% & 0.06 s / GPU \\
Decoupled-3D \cite{cai2020monocular} & 14.82 \% & 23.16 \% & 11.25 \% & 0.08 s / GPU \\
SMOKE \cite{liu2020smoke} & 14.49 \% & 20.83 \% & 12.75 \% & 0.03 s / GPU \\
RTM3D \cite{li2020rtm3d} & 14.20 \% & 19.17 \% & 11.99 \% & 0.05 s / GPU \\
Mono3D\_PLiDAR \cite{Weng2019} & 13.92 \% & 21.27 \% & 11.25 \% & 0.1 s / \\
M3D-RPN \cite{brazil2019m3drpn} & 13.67 \% & 21.02 \% & 10.23 \% & 0.16 s / GPU \\
CSoR \cite{Plotkin2015} & 13.07 \% & 18.67 \% & 10.34 \% & 3.5 s / 4 cores \\
MonoPSR \cite{ku2019monopsr} & 12.58 \% & 18.33 \% & 9.91 \% & 0.2 s / GPU \\
SS3D \cite{DBLPjournalscorrabs190608070} & 11.52 \% & 16.33 \% & 9.93 \% & 48 ms / \\
MonoGRNet \cite{qin2019monogrnet} & 11.17 \% & 18.19 \% & 8.73 \% & 0.04s / \\
MonoFENet \cite{monofenet} & 11.03 \% & 17.03 \% & 9.05 \% & 0.15 s / 1 core \\
A3DODWTDA (image) \cite{erino397fregu856master2018} & 8.66 \% & 10.37 \% & 7.06 \% & 0.8 s / GPU \\
TLNet (Stereo) \cite{qin2019tlnet} & 7.69 \% & 13.71 \% & 6.73 \% & 0.1 s / 1 core \\
Shift R-CNN (mono) \cite{shiftrcnn} & 6.82 \% & 11.84 \% & 5.27 \% & 0.25 s / GPU \\
GS3D \cite{li2019gs3d} & 6.08 \% & 8.41 \% & 4.94 \% & 2 s / 1 core \\
MVRA + I-FRCNN+ \cite{Choi2019ICCV} & 5.84 \% & 9.05 \% & 4.50 \% & 0.18 s / GPU \\
ROI-10D \cite{manhardt2018roi10d} & 4.91 \% & 9.78 \% & 3.74 \% & 0.2 s / GPU \\
3D-GCK \cite{gahlert2020single} & 4.57 \% & 5.79 \% & 3.64 \% & 24 ms / \\
FQNet \cite{liu2019deep} & 3.23 \% & 5.40 \% & 2.46 \% & 0.5 s / 1 core \\
3D-SSMFCNN \cite{novakmaster2017} & 2.63 \% & 3.20 \% & 2.40 \% & 0.1 s / GPU \\
multi-task CNN \cite{Oeljeklaus18} & 0.00 \% & 0.00 \% & 0.00 \% & 25.1 ms / GPU \\
mBoW \cite{Behley2013IROS} & 0.00 \% & 0.00 \% & 0.00 \% & 10 s / 1 core
\end{tabular}