\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
CLOCs\_PVCas \cite{pang2020CLOCs} & 95.96 \% & 96.76 \% & 91.08 \% & 0.1 s / 1 core \\
PC-CNN-V2 \cite{8461232} & 95.20 \% & 96.06 \% & 89.37 \% & 0.5 s / GPU \\
F-PointNet \cite{qi2017frustum} & 95.17 \% & 95.85 \% & 85.42 \% & 0.17 s / GPU \\
SA-SSD \cite{he2020sassd} & 95.16 \% & 97.92 \% & 90.15 \% & 0.04 s / 1 core \\
3DSSD \cite{yang3DSSD20} & 95.10 \% & 97.69 \% & 92.18 \% & 0.04 s / GPU \\
MVRA + I-FRCNN+ \cite{Choi2019ICCV} & 94.98 \% & 95.87 \% & 82.52 \% & 0.18 s / GPU \\
MMLab PV-RCNN \cite{shi2020pv} & 94.70 \% & 98.17 \% & 92.04 \% & 0.08 s / 1 core \\
Deformable PV-RCNN \cite{bhattacharyya2020deformable} & 94.64 \% & 95.86 \% & 92.10 \% & 0.08 s / 1 core \\
TuSimple \cite{yang2016exploit} & 94.47 \% & 95.12 \% & 86.45 \% & 1.6 s / GPU \\
EPNet \cite{huang2020epnet} & 94.44 \% & 96.15 \% & 89.99 \% & 0.1 s / 1 core \\
SERCNN \cite{zhou2020joint} & 94.42 \% & 96.33 \% & 89.96 \% & 0.1 s / 1 core \\
UberATG-MMF \cite{Liang2019CVPR} & 94.25 \% & 97.41 \% & 89.87 \% & 0.08 s / GPU \\
RangeRCNN \cite{liang2020rangercnn} & 94.03 \% & 95.48 \% & 91.74 \% & 0.06 s / GPU \\
Patches - EMP \cite{lehner2019patch} & 93.75 \% & 97.91 \% & 90.56 \% & 0.5 s / GPU \\
MonoPair \cite{chen2020cvpr} & 93.55 \% & 96.61 \% & 83.55 \% & 0.06 s / GPU \\
Deep MANTA \cite{deepmantacvpr17} & 93.50 \% & 98.89 \% & 83.21 \% & 0.7 s / GPU \\
Point-GNN \cite{shi2020pointgnn} & 93.50 \% & 96.58 \% & 88.35 \% & 0.6 s / GPU \\
scssd-normal(0.3) \cite{scssd} & 93.45 \% & 96.72 \% & 88.31 \% & 0.05 s / GPU \\
RRC \cite{Ren17CVPR} & 93.40 \% & 95.68 \% & 87.37 \% & 3.6 s / GPU \\
3D-CVF at SPA \cite{3DCVF} & 93.36 \% & 96.78 \% & 86.11 \% & 0.06 s / 1 core \\
scssd-normal(0.4) \cite{scssd} & 93.31 \% & 96.59 \% & 88.23 \% & 0.05 s / 1 core \\
STD \cite{std2019yang} & 93.22 \% & 96.14 \% & 90.53 \% & 0.08 s / GPU \\
SARPNET \cite{ye2019sarpnet} & 93.21 \% & 96.07 \% & 88.09 \% & 0.05 s / 1 core \\
Fast Point R-CNN \cite{Chen2019fastpointrcnn} & 93.18 \% & 96.13 \% & 87.68 \% & 0.06 s / GPU \\
sensekitti \cite{binyang16craft} & 93.17 \% & 94.79 \% & 84.38 \% & 4.5 s / GPU \\
SJTU-HW \cite{zsq2018icip} & 93.11 \% & 96.30 \% & 82.21 \% & 0.85s / GPU \\
CLOCs\_SecCas \cite{pang2020CLOCs} & 92.95 \% & 95.43 \% & 89.21 \% & 0.1 s / 1 core \\
HotSpotNet \cite{chen2020object} & 92.81 \% & 96.21 \% & 89.80 \% & 0.04 s / 1 core \\
SegVoxelNet \cite{yi2020SegVoxelNet} & 92.73 \% & 96.00 \% & 87.60 \% & 0.04 s / 1 core \\
Patches \cite{lehner2019patch} & 92.72 \% & 96.34 \% & 87.63 \% & 0.15 s / GPU \\
CenterNet3D \cite{2007.07214} & 92.69 \% & 95.76 \% & 89.81 \% & 0.04 s / GPU \\
R-GCN \cite{Zarzar2019PointRGCNGC} & 92.67 \% & 96.19 \% & 87.66 \% & 0.16 s / GPU \\
PI-RCNN \cite{xie2020pi} & 92.66 \% & 96.17 \% & 87.68 \% & 0.1 s / 1 core \\
PointPainting \cite{vora2019pointpainting} & 92.58 \% & 98.39 \% & 89.71 \% & 0.4 s / GPU \\
3D IoU-Net \cite{Li20203DIoUNet} & 92.47 \% & 96.31 \% & 87.67 \% & 0.1 s / 1 core \\
Associate-3Ddet \cite{Du2020CVPR} & 92.45 \% & 95.61 \% & 87.32 \% & 0.05 s / 1 core \\
PointRGCN \cite{Zarzar2019PointRGCNGC} & 92.33 \% & 97.51 \% & 87.07 \% & 0.26 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 92.19 \% & 95.85 \% & 80.09 \% & 0.47 s / GPU \\
SDP+RPN \cite{Yang2016CVPR} & 92.03 \% & 95.16 \% & 79.16 \% & 0.4 s / GPU \\
AB3DMOT \cite{Weng2019} & 92.00 \% & 95.88 \% & 86.98 \% & 0.0047s / 1 core \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 91.90 \% & 95.92 \% & 87.11 \% & 0.1 s / GPU \\
MMLab-PartA^2 \cite{shi2020part} & 91.86 \% & 95.03 \% & 89.06 \% & 0.08 s / GPU \\
epBRM \cite{arxiv} & 91.77 \% & 94.59 \% & 88.45 \% & 0.1 s / GPU \\
3DBN\_2 \cite{ERROR: Wrong syntax in BIBTEX file.} & 91.75 \% & 95.34 \% & 89.12 \% & 0.12 s / 1 core \\
C-GCN \cite{Zarzar2019PointRGCNGC} & 91.73 \% & 95.64 \% & 86.37 \% & 0.147 s / GPU \\
ITVD \cite{liu2018learning} & 91.73 \% & 95.85 \% & 79.31 \% & 0.3 s / GPU \\
SINet+ \cite{hu2019sinet} & 91.67 \% & 94.17 \% & 78.60 \% & 0.3 s / \\
Cascade MS-CNN \cite{cai2019cascade} & 91.60 \% & 94.26 \% & 78.84 \% & 0.25 s / GPU \\
MAFF-Net(DAF-Pillar) \cite{zhang2020maffnet} & 91.46 \% & 94.38 \% & 83.89 \% & 0.04 s / 1 core \\
HRI-VoxelFPN \cite{Kuang2020voxelFPN} & 91.44 \% & 96.65 \% & 86.18 \% & 0.02 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 91.19 \% & 94.00 \% & 88.17 \% & 16 ms / \\
LTN \cite{8836625} & 91.18 \% & 94.68 \% & 81.51 \% & 0.4 s / GPU \\
WS3D \cite{meng2020eccv} & 91.15 \% & 95.13 \% & 86.52 \% & 0.1 s / GPU \\
Aston-EAS \cite{wei2019enhanced} & 91.02 \% & 93.91 \% & 77.93 \% & 0.24 s / GPU \\
ARPNET \cite{Ye2019} & 90.99 \% & 94.00 \% & 83.49 \% & 0.08 s / GPU \\
PatchNet \cite{Ma2020ECCV} & 90.87 \% & 93.82 \% & 79.62 \% & 0.4 s / 1 core \\
MV3D \cite{Chen2017CVPR} & 90.83 \% & 96.47 \% & 78.63 \% & 0.36 s / GPU \\
3D IoU Loss \cite{zhou2019} & 90.79 \% & 95.92 \% & 85.65 \% & 0.08 s / GPU \\
SINet\_VGG \cite{hu2019sinet} & 90.79 \% & 93.59 \% & 77.53 \% & 0.2 s / \\
TANet \cite{liu2019tanet} & 90.67 \% & 93.67 \% & 85.31 \% & 0.035s / GPU \\
VOXEL\_FPN\_HR \cite{ERROR: Wrong syntax in BIBTEX file.} & 90.55 \% & 93.76 \% & 85.42 \% & 0.12 s / 8 cores \\
CG-Stereo \cite{li2020confidence} & 90.38 \% & 96.31 \% & 82.80 \% & 0.57 s / \\
SCNet \cite{8813061} & 90.30 \% & 95.59 \% & 85.09 \% & 0.04 s / GPU \\
Deep3DBox \cite{MousavianCVPR2017} & 90.19 \% & 94.71 \% & 76.82 \% & 1.5 s / GPU \\
FQNet \cite{liu2019deep} & 90.17 \% & 94.72 \% & 76.78 \% & 0.5 s / 1 core \\
DeepStereoOP \cite{Pham2017SPIC} & 90.06 \% & 95.15 \% & 79.91 \% & 3.4 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 89.98 \% & 94.26 \% & 79.78 \% & 2 s / GPU \\
MLOD \cite{deng2019mlod} & 89.97 \% & 94.88 \% & 84.98 \% & 0.12 s / GPU \\
GPP \cite{rangesh2018ground} & 89.96 \% & 94.02 \% & 81.13 \% & 0.23 s / GPU \\
AVOD \cite{ku2018joint} & 89.88 \% & 95.17 \% & 82.83 \% & 0.08 s / \\
SINet\_PVA \cite{hu2019sinet} & 89.86 \% & 92.72 \% & 76.47 \% & 0.11 s / \\
3DOP \cite{Chen2015NIPS} & 89.55 \% & 92.96 \% & 79.38 \% & 3s / GPU \\
Mono3D \cite{Chen2016CVPR} & 89.37 \% & 94.52 \% & 79.15 \% & 4.2 s / GPU \\
4d-MSCNN \cite{ferraz2020three} & 89.37 \% & 92.40 \% & 77.00 \% & 0.3 min / GPU \\
AVOD-FPN \cite{ku2018joint} & 88.92 \% & 94.70 \% & 84.13 \% & 0.1 s / \\
AM3D \cite{ma2019accurate} & 88.71 \% & 92.55 \% & 77.78 \% & 0.4 s / GPU \\
MS-CNN \cite{Cai2016ECCV} & 88.68 \% & 93.87 \% & 76.11 \% & 0.4 s / GPU \\
MonoPSR \cite{ku2019monopsr} & 88.50 \% & 93.63 \% & 73.36 \% & 0.2 s / GPU \\
Shift R-CNN (mono) \cite{shiftrcnn} & 88.48 \% & 94.07 \% & 78.34 \% & 0.25 s / GPU \\
MM-MRFC \cite{Costea2017CVPR} & 88.46 \% & 95.54 \% & 78.14 \% & 0.05 s / GPU \\
3DBN \cite{DBLPjournalscorrabs190108373} & 88.29 \% & 93.74 \% & 80.74 \% & 0.13s / \\
SMOKE \cite{liu2020smoke} & 87.51 \% & 93.21 \% & 77.66 \% & 0.03 s / GPU \\
CDN \cite{garg2020wasserstein} & 87.19 \% & 95.85 \% & 79.43 \% & 0.6 s / GPU \\
RTM3D \cite{li2020rtm3d} & 86.93 \% & 91.82 \% & 77.41 \% & 0.05 s / GPU \\
DSGN \cite{Chen2020dsgn} & 86.43 \% & 95.53 \% & 78.75 \% & 0.67 s / \\
Stereo R-CNN \cite{licvpr2019} & 85.98 \% & 93.98 \% & 71.25 \% & 0.3 s / GPU \\
StereoFENet \cite{monofenet} & 85.70 \% & 91.48 \% & 77.62 \% & 0.15 s / 1 core \\
ResNet-RRC\_Car \cite{rrcresnet} & 85.33 \% & 91.45 \% & 74.27 \% & 0.06 s / GPU \\
PL++ (SDN+GDC) \cite{you2020pseudolidar} & 85.15 \% & 94.95 \% & 77.78 \% & 0.6 s / GPU \\
M3D-RPN \cite{brazil2019m3drpn} & 85.08 \% & 89.04 \% & 69.26 \% & 0.16 s / GPU \\
CDN-PL++ \cite{garg2020wasserstein} & 85.01 \% & 94.66 \% & 77.60 \% & 0.4 s / GPU \\
SDP+CRC (ft) \cite{Yang2016CVPR} & 85.00 \% & 92.06 \% & 71.71 \% & 0.6 s / GPU \\
SS3D \cite{DBLPjournalscorrabs190608070} & 84.92 \% & 92.72 \% & 70.35 \% & 48 ms / \\
MonoFENet \cite{monofenet} & 84.63 \% & 91.68 \% & 76.71 \% & 0.15 s / 1 core \\
MV3D (LIDAR) \cite{Chen2017CVPR} & 84.39 \% & 93.08 \% & 79.27 \% & 0.24 s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 84.16 \% & 91.92 \% & 79.62 \% & 0.06 s / GPU \\
ZoomNet \cite{xu2020zoomnet} & 83.92 \% & 94.22 \% & 69.00 \% & 0.3 s / 1 core \\
D4LCN \cite{ding2019learning} & 83.67 \% & 90.34 \% & 65.33 \% & 0.2 s / GPU \\
Faster R-CNN \cite{Ren2015NIPS} & 83.16 \% & 88.97 \% & 72.62 \% & 2 s / GPU \\
Pseudo-LiDAR++ \cite{you2020pseudolidar} & 82.90 \% & 94.46 \% & 75.45 \% & 0.4 s / GPU \\
BS3D \cite{gahlert2019beyond} & 82.72 \% & 95.35 \% & 70.01 \% & 22 ms / \\
Disp R-CNN \cite{sun2020disprcnn} & 82.57 \% & 93.26 \% & 68.20 \% & 0.42 s / GPU \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 82.47 \% & 93.20 \% & 68.09 \% & 0.42 s / GPU \\
FRCNN+Or \cite{GuindelITSM} & 82.00 \% & 92.91 \% & 68.79 \% & 0.09 s / \\
yyyyolo \cite{ERROR: Wrong syntax in BIBTEX file.} & 81.33 \% & 94.36 \% & 68.72 \% & 0.01 s / 1 core \\
A3DODWTDA (image) \cite{erino397fregu856master2018} & 81.25 \% & 78.96 \% & 70.56 \% & 0.8 s / GPU \\
RefineNet \cite{7944662} & 81.01 \% & 91.91 \% & 65.67 \% & 0.20 s / GPU \\
3D-GCK \cite{gahlert2020single} & 80.19 \% & 89.55 \% & 68.08 \% & 24 ms / \\
A3DODWTDA \cite{erino397fregu856master2018} & 79.15 \% & 82.98 \% & 68.30 \% & 0.08 s / GPU \\
spLBP \cite{Hu2016TITS} & 78.66 \% & 81.66 \% & 61.69 \% & 1.5 s / 8 cores \\
3D-SSMFCNN \cite{novakmaster2017} & 78.19 \% & 77.92 \% & 69.19 \% & 0.1 s / GPU \\
MonoGRNet \cite{qin2019monogrnet} & 77.94 \% & 88.65 \% & 63.31 \% & 0.04s / \\
Reinspect \cite{Stewart2016CVPR} & 77.48 \% & 90.27 \% & 66.73 \% & 2s / 1 core \\
multi-task CNN \cite{Oeljeklaus18} & 77.18 \% & 86.12 \% & 68.09 \% & 25.1 ms / GPU \\
Regionlets \cite{Wang2015PAMI} & 76.99 \% & 88.75 \% & 60.49 \% & 1 s / >8 cores \\
3DVP \cite{Xiang2015CVPR} & 76.98 \% & 84.95 \% & 65.78 \% & 40 s / 8 cores \\
SubCat \cite{OhnBar2015TITS} & 76.36 \% & 84.10 \% & 60.56 \% & 0.7 s / 6 cores \\
GS3D \cite{li2019gs3d} & 76.35 \% & 86.23 \% & 62.67 \% & 2 s / 1 core \\
AOG \cite{Wu2016PAMI} & 76.24 \% & 86.08 \% & 61.51 \% & 3 s / 4 cores \\
Pose-RCNN \cite{braun2016pose} & 75.83 \% & 89.59 \% & 64.06 \% & 2 s / >8 cores \\
3D FCN \cite{li2017iros} & 74.65 \% & 86.74 \% & 67.85 \% & >5 s / 1 core \\
OC Stereo \cite{pon2020object} & 74.60 \% & 87.39 \% & 62.56 \% & 0.35 s / 1 core \\
Kinematic3D \cite{brazil2020kinematic} & 71.73 \% & 89.67 \% & 54.97 \% & 0.12 s / 1 core \\
AOG-View \cite{Li2014ECCV} & 71.26 \% & 85.01 \% & 55.73 \% & 3 s / 1 core \\
MV-RGBD-RF \cite{Gonzalez2016TCYB} & 70.70 \% & 77.89 \% & 57.41 \% & 4 s / 4 cores \\
Vote3Deep \cite{Engelcke2016ARXIV} & 70.30 \% & 78.95 \% & 63.12 \% & 1.5 s / 4 cores \\
ROI-10D \cite{manhardt2018roi10d} & 70.16 \% & 76.56 \% & 61.15 \% & 0.2 s / GPU \\
BirdNet+ \cite{Barrera2020} & 68.05 \% & 92.10 \% & 65.61 \% & 0.1 s / \\
Decoupled-3D \cite{cai2020monocular} & 67.92 \% & 87.78 \% & 54.53 \% & 0.08 s / GPU \\
Pseudo-Lidar \cite{Wang2019CVPR} & 67.79 \% & 85.40 \% & 58.50 \% & 0.4 s / GPU \\
OC-DPM \cite{Pepik2013CVPR} & 67.06 \% & 79.07 \% & 52.61 \% & 10 s / 8 cores \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 66.72 \% & 82.15 \% & 49.01 \% & 8 s / 1 core \\
BdCost48LDCF \cite{FernandezBaldera2018} & 66.63 \% & 81.38 \% & 52.20 \% & 0.5 s / 8 cores \\
RefinedMPL \cite{vianney2019refinedmpl} & 65.24 \% & 88.29 \% & 53.20 \% & 0.15 s / GPU \\
MDPM-un-BB \cite{Felzenszwalb10} & 64.06 \% & 79.74 \% & 49.07 \% & 60 s / 4 core \\
TLNet (Stereo) \cite{qin2019tlnet} & 63.53 \% & 76.92 \% & 54.58 \% & 0.1 s / 1 core \\
PDV-Subcat \cite{Shen2017PR} & 63.24 \% & 78.27 \% & 47.67 \% & 7 s / 1 core \\
MODet \cite{zhang2019accurate} & 62.54 \% & 66.06 \% & 60.04 \% & 0.05 s / \\
SubCat48LDCF \cite{FernandezBaldera2018} & 61.16 \% & 78.86 \% & 44.69 \% & 0.5 s / 8 cores \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 60.21 \% & 75.24 \% & 44.73 \% & 15 s / 4 cores \\
SAMME48LDCF \cite{FernandezBaldera2018} & 58.38 \% & 77.47 \% & 44.43 \% & 0.5 s / 8 cores \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 58.36 \% & 71.11 \% & 43.22 \% & 10 s / 4 cores \\
BirdNet \cite{BirdNet2018} & 57.12 \% & 79.30 \% & 55.16 \% & 0.11 s / \\
ACF-SC \cite{Cadena2015ICRA} & 56.60 \% & 69.90 \% & 43.61 \% & \\
LSVM-MDPM-us \cite{Felzenszwalb2010PAMI} & 55.95 \% & 68.94 \% & 41.45 \% & 10 s / 4 cores \\
ACF \cite{Dollar2014PAMI} & 54.09 \% & 63.05 \% & 41.81 \% & 0.2 s / 1 core \\
Mono3D\_PLiDAR \cite{Weng2019} & 53.36 \% & 80.85 \% & 44.80 \% & 0.1 s / \\
SF \cite{ERROR: Wrong syntax in BIBTEX file.} & 46.68 \% & 60.62 \% & 38.22 \% & 0.5 s / 1 core \\
Vote3D \cite{Wang2015RSS} & 45.94 \% & 54.38 \% & 40.48 \% & 0.5 s / 4 cores \\
TopNet-HighRes \cite{8569433} & 45.85 \% & 58.04 \% & 41.11 \% & 101ms / \\
RT3DStereo \cite{Koenigshof2019Objects} & 45.81 \% & 56.53 \% & 37.63 \% & 0.08 s / GPU \\
Multimodal Detection \cite{asvadi2017multimodal} & 45.46 \% & 63.91 \% & 37.25 \% & 0.06 s / GPU \\
RT3D \cite{8403277} & 39.69 \% & 50.33 \% & 40.04 \% & 0.09 s / GPU \\
VoxelJones \cite{motro2019vehicular} & 36.31 \% & 43.89 \% & 34.16 \% & .18 s / 1 core \\
CSoR \cite{Plotkin2015} & 21.66 \% & 31.52 \% & 17.99 \% & 3.5 s / 4 cores \\
mBoW \cite{Behley2013IROS} & 21.59 \% & 35.22 \% & 16.89 \% & 10 s / 1 core \\
DepthCN \cite{asvadi2017depthcn} & 21.18 \% & 37.45 \% & 16.08 \% & 2.3 s / GPU \\
YOLOv2 \cite{redmon2016you} & 14.31 \% & 26.74 \% & 10.94 \% & 0.02 s / GPU \\
TopNet-UncEst \cite{wirges2019capturing} & 6.24 \% & 7.24 \% & 5.42 \% & 0.09 s / \\
TopNet-Retina \cite{8569433} & 5.00 \% & 6.82 \% & 4.52 \% & 52ms / \\
TopNet-DecayRate \cite{8569433} & 0.01 \% & 0.00 \% & 0.01 \% & 92 ms / \\
LaserNet \cite{lasernet} & 0.00 \% & 0.00 \% & 0.00 \% & 12 ms / GPU
\end{tabular}