\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
VirConv-S \cite{VirConv} & 93.52 \% & 95.99 \% & 90.38 \% & 0.09 s / 1 core \\
UDeerPEP \cite{dong2023pep} & 93.40 \% & 95.34 \% & 89.07 \% & 0.1 s / 1 core \\
VirConv-T \cite{VirConv} & 92.65 \% & 96.11 \% & 89.69 \% & 0.09 s / 1 core \\
GraR-Po \cite{yang2022graphrcnn} & 92.12 \% & 95.79 \% & 87.11 \% & 0.06 s / 1 core \\
TSSTDet \cite{10399338} & 92.11 \% & 95.80 \% & 89.23 \% & 0.08 s / 1 core \\
TED \cite{TED} & 92.05 \% & 95.44 \% & 87.30 \% & 0.1 s / 1 core \\
VPFNet \cite{9826439} & 91.86 \% & 93.02 \% & 86.94 \% & 0.06 s / 2 cores \\
SFD \cite{wu2022sparse} & 91.85 \% & 95.64 \% & 86.83 \% & 0.1 s / 1 core \\
SE-SSD \cite{zheng2020ciassd} & 91.84 \% & 95.68 \% & 86.72 \% & 0.03 s / 1 core \\
ACFNet \cite{10363115} & 91.78 \% & 92.91 \% & 87.06 \% & 0.11 s / 1 core \\
GraR-Vo \cite{yang2022graphrcnn} & 91.72 \% & 95.27 \% & 86.51 \% & 0.04 s / 1 core \\
PVT-SSD \cite{yang2023pvtssd} & 91.63 \% & 95.23 \% & 86.43 \% & 0.05 s / 1 core \\
SPANet \cite{ye2021spanet} & 91.59 \% & 95.59 \% & 86.53 \% & 0.06 s / 1 core \\
CasA \cite{casa2022} & 91.54 \% & 95.19 \% & 86.82 \% & 0.1 s / 1 core \\
LoGoNet \cite{li2023logonet} & 91.52 \% & 95.48 \% & 87.09 \% & 0.1 s / 1 core \\
GraR-Pi \cite{yang2022graphrcnn} & 91.52 \% & 95.06 \% & 86.42 \% & 0.03 s / 1 core \\
UPIDet \cite{zhang2023upidet} & 91.36 \% & 92.96 \% & 86.80 \% & 0.11 s / 1 core \\
BADet \cite{qian2022BADet} & 91.32 \% & 95.23 \% & 86.48 \% & 0.14 s / 1 core \\
TED-S Reproduced \cite{ERROR: Wrong syntax in BIBTEX file.} & 91.23 \% & 95.34 \% & 86.68 \% & 0.1 s / 1 core \\
CasA++ \cite{casa2022} & 91.22 \% & 94.57 \% & 88.43 \% & 0.1 s / 1 core \\
3D HANet \cite{10056279} & 91.13 \% & 94.33 \% & 86.33 \% & 0.1 s / 1 core \\
SA-SSD \cite{he2020sassd} & 91.03 \% & 95.03 \% & 85.96 \% & 0.04 s / 1 core \\
L-AUG \cite{cortinhal2023semanticsaware} & 91.00 \% & 94.52 \% & 88.08 \% & 0.1 s / 1 core \\
3D Dual-Fusion \cite{kim20223d} & 90.86 \% & 93.08 \% & 86.44 \% & 0.1 s / 1 core \\
GraphAlign(ICCV2023) \cite{song2023graphalign} & 90.73 \% & 94.46 \% & 88.34 \% & 0.03 s / GPU \\
MMLab PV-RCNN \cite{shi2020pv} & 90.65 \% & 94.98 \% & 86.14 \% & 0.08 s / 1 core \\
VPFNet \cite{wang2021vpfnet} & 90.52 \% & 93.94 \% & 86.25 \% & 0.2 s / 1 core \\
PDV \cite{PDV} & 90.48 \% & 94.56 \% & 86.23 \% & 0.1 s / 1 core \\
M3DeTR \cite{guan2021m3detr} & 90.37 \% & 94.41 \% & 85.98 \% & n/a s / GPU \\
VoTr-TSD \cite{mao2021votr} & 90.34 \% & 94.03 \% & 86.14 \% & 0.07 s / 1 core \\
DSA-PV-RCNN \cite{bhattacharyya2021sadet3d} & 90.13 \% & 92.42 \% & 85.93 \% & 0.08 s / 1 core \\
XView \cite{xie2021xview} & 90.12 \% & 92.27 \% & 85.94 \% & 0.1 s / 1 core \\
GraR-VoI \cite{yang2022graphrcnn} & 90.10 \% & 95.69 \% & 86.85 \% & 0.07 s / 1 core \\
CAT-Det \cite{zhang2022cat} & 90.07 \% & 92.59 \% & 85.82 \% & 0.3 s / GPU \\
3ONet \cite{10183841} & 90.07 \% & 95.87 \% & 85.09 \% & 0.1 s / 1 core \\
spark-part2 \cite{ERROR: Wrong syntax in BIBTEX file.} & 90.01 \% & 93.82 \% & 85.89 \% & 0.1 s / 1 core \\
SVGA-Net \cite{he2022svga} & 89.88 \% & 92.07 \% & 85.59 \% & 0.03s / 1 core \\
EBM3DOD \cite{gustafsson2020accurate} & 89.86 \% & 95.64 \% & 84.56 \% & 0.12 s / 1 core \\
CIA-SSD \cite{zheng2020ciassd} & 89.84 \% & 93.74 \% & 82.39 \% & 0.03 s / 1 core \\
MLF-DET \cite{lin2023mlf} & 89.82 \% & 93.38 \% & 84.78 \% & 0.09 s / 1 core \\
CLOCs\_PVCas \cite{pang2020CLOCs} & 89.80 \% & 93.05 \% & 86.57 \% & 0.1 s / 1 core \\
GLENet-VR \cite{zhang2023glenet} & 89.76 \% & 93.48 \% & 84.89 \% & 0.04 s / 1 core \\
RDIoU \cite{sheng2022rdiou} & 89.75 \% & 94.90 \% & 84.67 \% & 0.03 s / 1 core \\
EBM3DOD baseline \cite{gustafsson2020accurate} & 89.63 \% & 95.44 \% & 84.34 \% & 0.05 s / 1 core \\
VPA \cite{ERROR: Wrong syntax in BIBTEX file.} & 89.61 \% & 95.46 \% & 86.81 \% & 0.01 s / 1 core \\
3D-CVF at SPA \cite{3DCVF} & 89.56 \% & 93.52 \% & 82.45 \% & 0.06 s / 1 core \\
OcTr \cite{zhou2023octr} & 89.56 \% & 93.08 \% & 86.74 \% & 0.06 s / GPU \\
Struc info fusion II \cite{sif} & 89.54 \% & 95.26 \% & 82.31 \% & 0.05 s / GPU \\
spark\_second\_focal\_w \cite{ERROR: Wrong syntax in BIBTEX file.} & 89.53 \% & 91.19 \% & 85.11 \% & 0.1 s / 1 core \\
SASA \cite{chen2022sasa} & 89.51 \% & 92.87 \% & 86.35 \% & 0.04 s / 1 core \\
Fast-CLOCs \cite{Pang2022WACV} & 89.49 \% & 93.03 \% & 86.40 \% & 0.1 s / GPU \\
IA-SSD (single) \cite{zhang2022not} & 89.48 \% & 93.14 \% & 84.42 \% & 0.013 s / 1 core \\
KPTr \cite{ERROR: Wrong syntax in BIBTEX file.} & 89.48 \% & 92.74 \% & 84.50 \% & 0.07 s / 1 core \\
CLOCs \cite{pang2020CLOCs} & 89.48 \% & 92.91 \% & 86.42 \% & 0.1 s / 1 core \\
PA3DNet \cite{10034840} & 89.46 \% & 93.11 \% & 84.60 \% & 0.1 s / GPU \\
PG-RCNN \cite{koo2023pgrcnn} & 89.46 \% & 93.39 \% & 86.54 \% & 0.06 s / GPU \\
DFAF3D \cite{tang2023dfaf3d} & 89.45 \% & 93.14 \% & 84.22 \% & 0.05 s / 1 core \\
DVF-V \cite{mahmoud2022dense} & 89.42 \% & 93.12 \% & 86.50 \% & 0.1 s / 1 core \\
Struc info fusion I \cite{sif} & 89.38 \% & 94.91 \% & 84.29 \% & 0.05 s / 1 core \\
BtcDet \cite{xu2020behind} & 89.34 \% & 92.81 \% & 84.55 \% & 0.09 s / GPU \\
IA-SSD (multi) \cite{zhang2022not} & 89.33 \% & 92.79 \% & 84.35 \% & 0.014 s / 1 core \\
ACDet \cite{acdet} & 89.21 \% & 92.87 \% & 85.80 \% & 0.05 s / 1 core \\
DVF-PV \cite{mahmoud2022dense} & 89.20 \% & 93.08 \% & 86.28 \% & 0.1 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 \\
HMFI \cite{li2022homogeneous} & 89.17 \% & 93.04 \% & 86.37 \% & 0.1 s / 1 core \\
SSL-PointGNN \cite{erccelik20223d} & 89.16 \% & 92.92 \% & 83.99 \% & 0.56 s / GPU \\
SPG\_mini \cite{xu2021spg} & 89.12 \% & 92.80 \% & 86.27 \% & 0.09 s / GPU \\
EQ-PVRCNN \cite{Yang2022eqparadigm} & 89.09 \% & 94.55 \% & 86.42 \% & 0.2 s / GPU \\
VoxSeT \cite{voxset} & 89.07 \% & 92.70 \% & 86.29 \% & 33 ms / 1 core \\
3DSSD \cite{yang3DSSD20} & 89.02 \% & 92.66 \% & 85.86 \% & 0.04 s / GPU \\
EPNet++ \cite{9983516} & 89.00 \% & 95.41 \% & 85.73 \% & 0.1 s / GPU \\
Focals Conv \cite{focalsconvchen} & 89.00 \% & 92.67 \% & 86.33 \% & 0.1 s / 1 core \\
USVLab BSAODet \cite{10052705} & 88.90 \% & 92.66 \% & 86.23 \% & 0.04 s / 1 core \\
H^23D R-CNN \cite{deng2021multi} & 88.87 \% & 92.85 \% & 86.07 \% & 0.03 s / 1 core \\
Pyramid R-CNN \cite{mao2021pyramid} & 88.84 \% & 92.19 \% & 86.21 \% & 0.07 s / 1 core \\
CityBrainLab-CT3D \cite{sheng2021ct3d} & 88.83 \% & 92.36 \% & 84.07 \% & 0.07 s / 1 core \\
Voxel R-CNN \cite{deng2020voxelrcnn} & 88.83 \% & 94.85 \% & 86.13 \% & 0.04 s / GPU \\
HVNet \cite{ye2020hvnet} & 88.82 \% & 92.83 \% & 83.38 \% & 0.03 s / GPU \\
GD-MAE \cite{yang2023gdmae} & 88.82 \% & 94.22 \% & 83.54 \% & 0.07 s / 1 core \\
SPG \cite{xu2021spg} & 88.70 \% & 94.33 \% & 85.98 \% & 0.09 s / 1 core \\
SIENet \cite{li2021sienet} & 88.65 \% & 92.38 \% & 86.03 \% & 0.08 s / 1 core \\
P2V-RCNN \cite{P2VRCNN} & 88.63 \% & 92.72 \% & 86.14 \% & 0.1 s / 1 core \\
FromVoxelToPoint \cite{FromVoxelToPoint} & 88.61 \% & 92.23 \% & 86.11 \% & 0.1 s / 1 core \\
RangeIoUDet \cite{liang2021rangeioudet} & 88.59 \% & 92.28 \% & 85.83 \% & 0.02 s / GPU \\
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 \\
FARP-Net \cite{10123008} & 88.45 \% & 91.20 \% & 86.01 \% & 0.06 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 \\
StructuralIF \cite{sif3d2d} & 88.38 \% & 91.78 \% & 85.67 \% & 0.02 s / 8 cores \\
PASS-PV-RCNN-Plus \cite{context} & 88.37 \% & 92.17 \% & 85.75 \% & 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 \\
SRDL \cite{ERROR: Wrong syntax in BIBTEX file.} & 88.17 \% & 92.01 \% & 85.43 \% & 0.05 s / 1 core \\
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 \\
Faraway-Frustum \cite{zhang2021faraway} & 88.08 \% & 91.90 \% & 85.35 \% & 0.1 s / GPU \\
pointpillar\_spark\_fo \cite{ERROR: Wrong syntax in BIBTEX file.} & 88.02 \% & 92.48 \% & 84.82 \% & 0.1 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 \\
SIF \cite{sif3d2d} & 87.76 \% & 91.44 \% & 85.15 \% & 0.1 s / 1 core \\
MVAF-Net \cite{wang2020multi} & 87.73 \% & 91.95 \% & 85.00 \% & 0.06 s / 1 core \\
DVFENet \cite{HE2021} & 87.68 \% & 90.93 \% & 84.60 \% & 0.05 s / 1 core \\
S-AT GCN \cite{DBLPjournalscorrabs210308439} & 87.68 \% & 90.85 \% & 84.20 \% & 0.02 s / GPU \\
RangeDet (Official) \cite{Fan2021ICCV} & 87.67 \% & 90.93 \% & 82.92 \% & 0.02 s / 1 core \\
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 \\
MGAF-3DSSD \cite{AnchorfreeMGASSD} & 87.47 \% & 92.70 \% & 82.19 \% & 0.1 s / 1 core \\
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 \\
Sem-Aug \cite{9830844} & 87.37 \% & 93.35 \% & 82.43 \% & 0.1 s / GPU \\
MAFF-Net(DAF-Pillar) \cite{zhang2020maffnet} & 87.34 \% & 90.79 \% & 77.66 \% & 0.04 s / 1 core \\
Harmonic PointPillar \cite{context} & 87.28 \% & 90.89 \% & 82.54 \% & 0.01 s / 1 core \\
PASS-PointPillar \cite{context} & 87.23 \% & 91.07 \% & 81.98 \% & 1 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 \\
LVFSD \cite{ERROR: Wrong syntax in BIBTEX file.} & 87.12 \% & 90.42 \% & 83.91 \% & 0.06 s / \\
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 \\
3D IoU Loss \cite{zhou2019} & 86.22 \% & 91.36 \% & 81.20 \% & 0.08 s / GPU \\
VSAC \cite{ERROR: Wrong syntax in BIBTEX file.} & 86.22 \% & 91.98 \% & 81.50 \% & 0.07 s / 1 core \\
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 \\
Sem-Aug-PointRCNN++ \cite{9830844} & 85.88 \% & 91.68 \% & 83.37 \% & 0.1 s / 8 cores \\
DASS \cite{Unal2021WACV} & 85.85 \% & 91.74 \% & 80.97 \% & 0.09 s / 1 core \\
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 \\
PointRGBNet \cite{Xie Desheng340} & 85.73 \% & 91.39 \% & 80.68 \% & 0.08 s / 4 cores \\
UberATG-ContFuse \cite{Liang2018ECCV} & 85.35 \% & 94.07 \% & 75.88 \% & 0.06 s / GPU \\
PFF3D \cite{9340187} & 85.08 \% & 89.61 \% & 80.42 \% & 0.05 s / GPU \\
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 \\
mmFUSION \cite{ahmad2023mmfusion} & 84.60 \% & 90.35 \% & 79.82 \% & 1s / 1 core \\
3DBN \cite{DBLPjournalscorrabs190108373} & 83.94 \% & 89.66 \% & 76.50 \% & 0.13s / \\
EOTL \cite{yang2023efficient} & 83.14 \% & 89.10 \% & 71.41 \% & TBD s / 1 core \\
MLOD \cite{deng2019mlod} & 82.68 \% & 90.25 \% & 77.97 \% & 0.12 s / GPU \\
BirdNet+ \cite{barrera2021birdnet+} & 81.85 \% & 87.43 \% & 75.36 \% & 0.11 s / \\
DMF \cite{chen2022DMF} & 80.29 \% & 84.64 \% & 76.05 \% & 0.2 s / 1 core \\
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 \\
DSGN++ \cite{chen2022dsgn++} & 78.94 \% & 88.55 \% & 69.74 \% & 0.2 s / \\
MV3D \cite{Chen2017CVPR} & 78.93 \% & 86.62 \% & 69.80 \% & 0.36 s / GPU \\
StereoDistill \cite{liu2020tanet} & 78.59 \% & 89.03 \% & 69.34 \% & 0.4 s / 1 core \\
MMLAB LIGA-Stereo \cite{Guo2021ICCV} & 76.78 \% & 88.15 \% & 67.40 \% & 0.4 s / 1 core \\
RCD \cite{bewley2020range} & 75.83 \% & 82.26 \% & 69.61 \% & 0.1 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 \\
SNVC \cite{li2022stereo} & 73.61 \% & 86.88 \% & 64.49 \% & 1 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 / \\
PLUME \cite{wang2021plume} & 66.27 \% & 82.97 \% & 56.70 \% & 0.15 s / GPU \\
CDN \cite{garg2020wasserstein} & 66.24 \% & 83.32 \% & 57.65 \% & 0.6 s / GPU \\
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+ (legacy) \cite{9294293} & 63.33 \% & 84.80 \% & 61.23 \% & 0.1 s / \\
3D FCN \cite{li2017iros} & 61.67 \% & 70.62 \% & 55.61 \% & >5 s / 1 core \\
CDN-PL++ \cite{garg2020wasserstein} & 61.04 \% & 81.27 \% & 52.84 \% & 0.4 s / GPU \\
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 / \\
RT3D-GMP \cite{konigshof2020learning} & 59.00 \% & 69.14 \% & 45.49 \% & 0.06 s / GPU \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 58.62 \% & 79.76 \% & 47.73 \% & 0.387 s / GPU \\
ESGN \cite{9869894} & 58.12 \% & 78.10 \% & 49.28 \% & 0.06 s / GPU \\
Pseudo-LiDAR++ \cite{you2020pseudolidar} & 58.01 \% & 78.31 \% & 51.25 \% & 0.4 s / GPU \\
Disp R-CNN \cite{sun2020disprcnn} & 57.98 \% & 79.61 \% & 47.09 \% & 0.387 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 / \\
OC Stereo \cite{pon2020object} & 51.47 \% & 68.89 \% & 42.97 \% & 0.35 s / 1 core \\
YOLOStereo3D \cite{liu2021yolostereo3d} & 50.28 \% & 76.10 \% & 36.86 \% & 0.1 s / \\
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 \\
BKDStereo3D \cite{ERROR: Wrong syntax in BIBTEX file.} & 44.02 \% & 70.19 \% & 32.78 \% & 0.1 s / 1 core \\
RT3D \cite{8403277} & 44.00 \% & 56.44 \% & 42.34 \% & 0.09 s / GPU \\
Stereo CenterNet \cite{SHI2022219} & 42.12 \% & 62.97 \% & 35.37 \% & 0.04 s / GPU \\
Stereo R-CNN \cite{licvpr2019} & 41.31 \% & 61.92 \% & 33.42 \% & 0.3 s / GPU \\
BKDStereo3D w/o KD \cite{ERROR: Wrong syntax in BIBTEX file.} & 40.69 \% & 67.38 \% & 29.98 \% & 0.1 s / 1 core \\
CIE + DM3D \cite{ye2022consistency} & 33.13 \% & 46.17 \% & 28.80 \% & 0.1 s / 1 core \\
StereoFENet \cite{monofenet} & 32.96 \% & 49.29 \% & 25.90 \% & 0.15 s / 1 core \\
Mobile Stereo R-CNN \cite{mobilestereorcnn2021} & 28.78 \% & 44.51 \% & 22.30 \% & 1.8 s / \\
CIE \cite{ye2022consistency} & 28.50 \% & 41.41 \% & 23.88 \% & 0.1 s / 1 core \\
MonoLTKD\_V3 \cite{ERROR: Wrong syntax in BIBTEX file.} & 27.75 \% & 38.75 \% & 24.13 \% & 0.04 s / 1 core \\
MonoLiG \cite{hekimoglu2023monocular} & 26.83 \% & 35.73 \% & 24.24 \% & 0.03 s / 1 core \\
MonoLSS \cite{monolss} & 25.95 \% & 34.89 \% & 22.59 \% & 0.04 s / 1 core \\
CMKD \cite{YuHCMKDECCV2022} & 25.82 \% & 38.98 \% & 22.80 \% & 0.1 s / 1 core \\
LPCG-Monoflex \cite{peng2022lidar} & 24.81 \% & 35.96 \% & 21.86 \% & 0.03 s / 1 core \\
NeurOCS \cite{Min2023CVPR} & 24.49 \% & 37.27 \% & 20.89 \% & 0.1 s / GPU \\
Mix-Teaching \cite{Yang2022MixTeachingAS} & 24.23 \% & 35.74 \% & 20.80 \% & 30 s / 1 core \\
PS-fld \cite{Chen2022CVPR} & 23.76 \% & 32.64 \% & 20.64 \% & 0.25 s / 1 core \\
ADD \cite{wu2022attention} & 23.58 \% & 35.20 \% & 20.08 \% & 0.1 s / 1 core \\
MonoNeRD \cite{xu2023mononerd} & 23.46 \% & 31.13 \% & 20.97 \% & na s / 1 core \\
MonoDDE \cite{liu2020smoke} & 23.46 \% & 33.58 \% & 20.37 \% & 0.04 s / 1 core \\
DD3D \cite{dd3d} & 23.41 \% & 32.35 \% & 20.42 \% & n/a s / 1 core \\
MonoUNI \cite{MonoUNI} & 23.05 \% & 33.28 \% & 19.39 \% & 0.04 s / 1 core \\
MonoCD \cite{yan2024monocd} & 22.81 \% & 33.41 \% & 19.57 \% & n/a s / 1 core \\
DID-M3D \cite{peng2022did} & 22.76 \% & 32.95 \% & 19.83 \% & 0.04 s / 1 core \\
OPA-3D \cite{su2023opa} & 22.53 \% & 33.54 \% & 19.22 \% & 0.04 s / 1 core \\
DCD \cite{li2022densely} & 21.50 \% & 32.55 \% & 18.25 \% & 0.03 s / 1 core \\
MonoDETR \cite{zhang2022monodetr} & 21.45 \% & 32.20 \% & 18.68 \% & 0.04 s / 1 core \\
SGM3D \cite{zhou2021sgm3d} & 21.37 \% & 31.49 \% & 18.43 \% & 0.03 s / 1 core \\
Cube R-CNN \cite{brazil2023omni3d} & 21.20 \% & 31.70 \% & 18.43 \% & 0.05 s / GPU \\
GUPNet \cite{lu2021geometry} & 21.19 \% & 30.29 \% & 18.20 \% & NA s / 1 core \\
HomoLoss(monoflex) \cite{Gu2022CVPR} & 20.68 \% & 29.60 \% & 17.81 \% & 0.04 s / 1 core \\
DEVIANT \cite{kumar2022deviant} & 20.44 \% & 29.65 \% & 17.43 \% & 0.04 s / \\
MonoDTR \cite{huang2022monodtr} & 20.38 \% & 28.59 \% & 17.14 \% & 0.04 s / 1 core \\
MDSNet \cite{xie2022mds} & 20.14 \% & 32.81 \% & 15.77 \% & 0.05 s / 1 core \\
AutoShape \cite{liu2021autoshape} & 20.08 \% & 30.66 \% & 15.95 \% & 0.04 s / 1 core \\
MonoFlex \cite{monoflex} & 19.75 \% & 28.23 \% & 16.89 \% & 0.03 s / GPU \\
MonoEF \cite{Zhou2021CVPR} & 19.70 \% & 29.03 \% & 17.26 \% & 0.03 s / 1 core \\
MonOAPC \cite{yao2023occlusion} & 19.67 \% & 28.91 \% & 16.99 \% & 0035 s / 1 core \\
HomoLoss(imvoxelnet) \cite{Gu2022CVPR} & 19.25 \% & 29.18 \% & 16.21 \% & 0.20 s / 1 core \\
DFR-Net \cite{dfr2021} & 19.17 \% & 28.17 \% & 14.84 \% & 0.18 s / \\
DLE \cite{ce21dle} & 19.05 \% & 31.09 \% & 14.13 \% & 0.06 s / \\
PCT \cite{wang2021pct} & 19.03 \% & 29.65 \% & 15.92 \% & 0.045 s / 1 core \\
CaDDN \cite{CaDDN} & 18.91 \% & 27.94 \% & 17.19 \% & 0.63 s / GPU \\
monodle \cite{MA2021CVPR} & 18.89 \% & 24.79 \% & 16.00 \% & 0.04 s / GPU \\
Neighbor-Vote \cite{chu2021neighborvote} & 18.65 \% & 27.39 \% & 16.54 \% & 0.1 s / GPU \\
MonoRCNN++ \cite{MonoRCNNWACV23} & 18.62 \% & 27.20 \% & 15.69 \% & 0.07 s / GPU \\
GrooMeD-NMS \cite{kumar2021groomed} & 18.27 \% & 26.19 \% & 14.05 \% & 0.12 s / 1 core \\
MonoRCNN \cite{MonoRCNNICCV21} & 18.11 \% & 25.48 \% & 14.10 \% & 0.07 s / GPU \\
Ground-Aware \cite{9327478} & 17.98 \% & 29.81 \% & 13.08 \% & 0.05 s / 1 core \\
Aug3D-RPN \cite{he2021aug3drpn} & 17.89 \% & 26.00 \% & 14.18 \% & 0.08 s / 1 core \\
DDMP-3D \cite{ddmp3d} & 17.89 \% & 28.08 \% & 13.44 \% & 0.18 s / 1 core \\
IAFA \cite{zhou2020iafa} & 17.88 \% & 25.88 \% & 15.35 \% & 0.04 s / 1 core \\
FMF-occlusion-net \cite{liu2022fine} & 17.60 \% & 27.39 \% & 13.25 \% & 0.16 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 \\
MonoRUn \cite{monorun} & 17.34 \% & 27.94 \% & 15.24 \% & 0.07 s / GPU \\
AM3D \cite{ma2019accurate} & 17.32 \% & 25.03 \% & 14.91 \% & 0.4 s / GPU \\
YoloMono3D \cite{liu2021yolostereo3d} & 17.15 \% & 26.79 \% & 12.56 \% & 0.05 s / GPU \\
CMAN \cite{CMAN2022} & 17.04 \% & 25.89 \% & 12.88 \% & 0.15 s / 1 core \\
GAC3D \cite{gac3d2021} & 16.93 \% & 25.80 \% & 12.50 \% & 0.25 s / 1 core \\
PatchNet \cite{Ma2020ECCV} & 16.86 \% & 22.97 \% & 14.97 \% & 0.4 s / 1 core \\
PGD-FCOS3D \cite{PGD} & 16.51 \% & 26.89 \% & 13.49 \% & 0.03 s / 1 core \\
ImVoxelNet \cite{rukhovich2021imvoxelnet} & 16.37 \% & 25.19 \% & 13.58 \% & 0.2 s / GPU \\
KM3D \cite{2009.00764} & 16.20 \% & 23.44 \% & 14.47 \% & 0.03 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 \\
QD-3DT \cite{Hu2021QD3DT} & 14.71 \% & 20.16 \% & 12.76 \% & 0.03 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 \\
Plane-Constraints \cite{yao2023vertex} & 12.06 \% & 17.31 \% & 10.05 \% & 0.05 s / 4 cores \\
MonoCInIS \cite{heylen2021monocinis} & 11.64 \% & 22.28 \% & 9.95 \% & 0,13 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 \\
MonoCInIS \cite{heylen2021monocinis} & 10.96 \% & 20.42 \% & 9.23 \% & 0,14 s / GPU \\
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 \\
SparVox3D \cite{9558880} & 6.39 \% & 10.20 \% & 5.06 \% & 0.05 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 \\
WeakM3D \cite{peng2022weakm3d} & 5.66 \% & 11.82 \% & 4.08 \% & 0.08 s / 1 core \\
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}