\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
UDeerPEP \cite{dong2023pep} & 97.39 \% & 98.40 \% & 94.80 \% & 0.1 s / 1 core \\
VirConv-S \cite{VirConv} & 96.46 \% & 96.99 \% & 93.74 \% & 0.09 s / 1 core \\
GraR-VoI \cite{yang2022graphrcnn} & 96.29 \% & 96.81 \% & 91.06 \% & 0.07 s / 1 core \\
MLF-DET \cite{lin2023mlf} & 96.09 \% & 96.87 \% & 88.78 \% & 0.09 s / 1 core \\
GraR-Po \cite{yang2022graphrcnn} & 96.09 \% & 96.83 \% & 90.99 \% & 0.06 s / 1 core \\
SFD \cite{wu2022sparse} & 96.05 \% & 98.95 \% & 90.96 \% & 0.1 s / 1 core \\
VPFNet \cite{9826439} & 96.04 \% & 96.63 \% & 90.99 \% & 0.06 s / 2 cores \\
VirConv-T \cite{VirConv} & 96.01 \% & 98.64 \% & 93.12 \% & 0.09 s / 1 core \\
TED \cite{TED} & 95.96 \% & 96.63 \% & 93.24 \% & 0.1 s / 1 core \\
RDIoU \cite{sheng2022rdiou} & 95.95 \% & 98.77 \% & 90.90 \% & 0.03 s / 1 core \\
ACFNet \cite{10363115} & 95.95 \% & 96.64 \% & 93.17 \% & 0.11 s / 1 core \\
CLOCs \cite{pang2020CLOCs} & 95.93 \% & 96.77 \% & 90.93 \% & 0.1 s / 1 core \\
GraR-Vo \cite{yang2022graphrcnn} & 95.92 \% & 96.66 \% & 92.78 \% & 0.04 s / 1 core \\
PVT-SSD \cite{yang2023pvtssd} & 95.83 \% & 96.74 \% & 90.58 \% & 0.05 s / 1 core \\
CLOCs\_PVCas \cite{pang2020CLOCs} & 95.79 \% & 96.74 \% & 90.81 \% & 0.1 s / 1 core \\
3D Dual-Fusion \cite{kim20223d} & 95.76 \% & 96.53 \% & 93.01 \% & 0.1 s / 1 core \\
GLENet-VR \cite{zhang2023glenet} & 95.73 \% & 96.84 \% & 90.80 \% & 0.04 s / 1 core \\
GraR-Pi \cite{yang2022graphrcnn} & 95.72 \% & 98.57 \% & 92.55 \% & 0.03 s / 1 core \\
VPA \cite{ERROR: Wrong syntax in BIBTEX file.} & 95.71 \% & 96.70 \% & 90.81 \% & 0.01 s / 1 core \\
OcTr \cite{zhou2023octr} & 95.69 \% & 96.44 \% & 90.78 \% & 0.06 s / GPU \\
DVF-V \cite{mahmoud2022dense} & 95.63 \% & 96.59 \% & 90.71 \% & 0.1 s / 1 core \\
DSGN++ \cite{chen2022dsgn++} & 95.58 \% & 98.04 \% & 88.09 \% & 0.2 s / \\
Fast-CLOCs \cite{Pang2022WACV} & 95.57 \% & 96.66 \% & 90.70 \% & 0.1 s / GPU \\
TSSTDet \cite{10399338} & 95.56 \% & 96.54 \% & 92.71 \% & 0.08 s / 1 core \\
3D HANet \cite{10056279} & 95.54 \% & 98.59 \% & 92.66 \% & 0.1 s / 1 core \\
FARP-Net \cite{10123008} & 95.53 \% & 96.10 \% & 92.98 \% & 0.06 s / GPU \\
CasA \cite{casa2022} & 95.53 \% & 96.51 \% & 92.71 \% & 0.1 s / 1 core \\
LoGoNet \cite{li2023logonet} & 95.44 \% & 96.59 \% & 92.89 \% & 0.1 s / 1 core \\
GD-MAE \cite{yang2023gdmae} & 95.36 \% & 98.31 \% & 90.19 \% & 0.07 s / 1 core \\
DVF-PV \cite{mahmoud2022dense} & 95.35 \% & 96.40 \% & 92.37 \% & 0.1 s / 1 core \\
BADet \cite{qian2022BADet} & 95.34 \% & 98.65 \% & 90.28 \% & 0.14 s / 1 core \\
SASA \cite{chen2022sasa} & 95.29 \% & 96.00 \% & 92.42 \% & 0.04 s / 1 core \\
PG-RCNN \cite{koo2023pgrcnn} & 95.27 \% & 96.64 \% & 90.37 \% & 0.06 s / GPU \\
Focals Conv \cite{focalsconvchen} & 95.23 \% & 96.29 \% & 92.60 \% & 0.1 s / 1 core \\
EQ-PVRCNN \cite{Yang2022eqparadigm} & 95.20 \% & 98.22 \% & 92.47 \% & 0.2 s / GPU \\
TED-S Reproduced \cite{ERROR: Wrong syntax in BIBTEX file.} & 95.19 \% & 98.43 \% & 92.55 \% & 0.1 s / 1 core \\
CasA++ \cite{casa2022} & 95.17 \% & 95.81 \% & 94.10 \% & 0.1 s / 1 core \\
SE-SSD \cite{zheng2020ciassd} & 95.17 \% & 96.55 \% & 90.00 \% & 0.03 s / 1 core \\
VoxSeT \cite{voxset} & 95.13 \% & 96.15 \% & 90.38 \% & 33 ms / 1 core \\
HMFI \cite{li2022homogeneous} & 95.05 \% & 96.28 \% & 92.28 \% & 0.1 s / 1 core \\
SPANet \cite{ye2021spanet} & 95.03 \% & 96.31 \% & 89.99 \% & 0.06 s / 1 core \\
Pyramid R-CNN \cite{mao2021pyramid} & 95.03 \% & 95.87 \% & 92.46 \% & 0.07 s / 1 core \\
VPFNet \cite{wang2021vpfnet} & 95.01 \% & 96.03 \% & 92.41 \% & 0.2 s / 1 core \\
EPNet++ \cite{9983516} & 95.00 \% & 96.70 \% & 91.82 \% & 0.1 s / GPU \\
USVLab BSAODet \cite{10052705} & 94.99 \% & 96.23 \% & 92.36 \% & 0.04 s / 1 core \\
Voxel R-CNN \cite{deng2020voxelrcnn} & 94.96 \% & 96.47 \% & 92.24 \% & 0.04 s / GPU \\
PDV \cite{PDV} & 94.91 \% & 96.06 \% & 92.30 \% & 0.1 s / 1 core \\
SIENet \cite{li2021sienet} & 94.85 \% & 96.01 \% & 92.23 \% & 0.08 s / 1 core \\
VoTr-TSD \cite{mao2021votr} & 94.81 \% & 95.95 \% & 92.24 \% & 0.07 s / 1 core \\
L-AUG \cite{cortinhal2023semanticsaware} & 94.76 \% & 95.80 \% & 91.94 \% & 0.1 s / 1 core \\
M3DeTR \cite{guan2021m3detr} & 94.70 \% & 97.37 \% & 91.89 \% & n/a s / GPU \\
spark\_second\_focal\_w \cite{ERROR: Wrong syntax in BIBTEX file.} & 94.67 \% & 95.43 \% & 91.82 \% & 0.1 s / 1 core \\
XView \cite{xie2021xview} & 94.66 \% & 95.88 \% & 92.07 \% & 0.1 s / 1 core \\
StructuralIF \cite{sif3d2d} & 94.64 \% & 96.12 \% & 91.85 \% & 0.02 s / 8 cores \\
spark-part2 \cite{ERROR: Wrong syntax in BIBTEX file.} & 94.61 \% & 95.70 \% & 91.96 \% & 0.1 s / 1 core \\
P2V-RCNN \cite{P2VRCNN} & 94.59 \% & 96.01 \% & 92.13 \% & 0.1 s / 1 core \\
CAT-Det \cite{zhang2022cat} & 94.57 \% & 95.95 \% & 91.88 \% & 0.3 s / GPU \\
MMLab PV-RCNN \cite{shi2020pv} & 94.57 \% & 98.15 \% & 91.85 \% & 0.08 s / 1 core \\
DSA-PV-RCNN \cite{bhattacharyya2021sadet3d} & 94.52 \% & 95.84 \% & 91.93 \% & 0.08 s / 1 core \\
RangeDet (Official) \cite{Fan2021ICCV} & 94.51 \% & 95.48 \% & 91.57 \% & 0.02 s / 1 core \\
MVRA + I-FRCNN+ \cite{Choi2019ICCV} & 94.46 \% & 95.66 \% & 81.74 \% & 0.18 s / GPU \\
SVGA-Net \cite{he2022svga} & 94.45 \% & 96.02 \% & 91.54 \% & 0.03s / 1 core \\
PASS-PV-RCNN-Plus \cite{context} & 94.45 \% & 95.77 \% & 91.89 \% & 1 s / 1 core \\
DVFENet \cite{HE2021} & 94.44 \% & 95.33 \% & 91.55 \% & 0.05 s / 1 core \\
RangeIoUDet \cite{liang2021rangeioudet} & 94.42 \% & 95.69 \% & 91.70 \% & 0.02 s / GPU \\
SERCNN \cite{zhou2020joint} & 94.24 \% & 96.31 \% & 89.71 \% & 0.1 s / 1 core \\
EPNet \cite{huang2020epnet} & 94.22 \% & 96.13 \% & 89.68 \% & 0.1 s / 1 core \\
SRDL \cite{ERROR: Wrong syntax in BIBTEX file.} & 94.08 \% & 95.83 \% & 91.55 \% & 0.05 s / 1 core \\
pointpillar\_spark\_fo \cite{ERROR: Wrong syntax in BIBTEX file.} & 94.07 \% & 96.40 \% & 91.06 \% & 0.1 s / 1 core \\
RangeRCNN \cite{liang2020rangercnn} & 93.90 \% & 95.47 \% & 91.53 \% & 0.06 s / GPU \\
SIF \cite{sif3d2d} & 93.79 \% & 95.48 \% & 91.30 \% & 0.1 s / 1 core \\
DD3D \cite{dd3d} & 93.78 \% & 94.67 \% & 88.99 \% & n/a s / 1 core \\
MGAF-3DSSD \cite{AnchorfreeMGASSD} & 93.77 \% & 94.45 \% & 86.25 \% & 0.1 s / 1 core \\
MMLAB LIGA-Stereo \cite{Guo2021ICCV} & 93.71 \% & 96.40 \% & 86.00 \% & 0.4 s / 1 core \\
Sem-Aug \cite{9830844} & 93.69 \% & 96.78 \% & 88.69 \% & 0.1 s / GPU \\
3ONet \cite{10183841} & 93.58 \% & 96.86 \% & 88.45 \% & 0.1 s / 1 core \\
Patches - EMP \cite{lehner2019patch} & 93.58 \% & 97.88 \% & 90.31 \% & 0.5 s / GPU \\
PA3DNet \cite{10034840} & 93.55 \% & 96.56 \% & 88.56 \% & 0.1 s / GPU \\
MVAF-Net \cite{wang2020multi} & 93.54 \% & 95.35 \% & 90.70 \% & 0.06 s / 1 core \\
IA-SSD (multi) \cite{zhang2022not} & 93.47 \% & 96.07 \% & 90.51 \% & 0.014 s / 1 core \\
IA-SSD (single) \cite{zhang2022not} & 93.41 \% & 96.23 \% & 88.34 \% & 0.013 s / 1 core \\
CIA-SSD \cite{zheng2020ciassd} & 93.34 \% & 96.65 \% & 85.76 \% & 0.03 s / 1 core \\
Deep MANTA \cite{deepmantacvpr17} & 93.31 \% & 98.83 \% & 82.95 \% & 0.7 s / GPU \\
LVFSD \cite{ERROR: Wrong syntax in BIBTEX file.} & 93.31 \% & 95.24 \% & 90.46 \% & 0.06 s / \\
StereoDistill \cite{liu2020tanet} & 93.29 \% & 97.57 \% & 87.48 \% & 0.4 s / 1 core \\
LPCG-Monoflex \cite{peng2022lidar} & 93.26 \% & 96.68 \% & 83.34 \% & 0.03 s / 1 core \\
MonoLiG \cite{hekimoglu2023monocular} & 93.23 \% & 96.56 \% & 83.42 \% & 0.03 s / 1 core \\
CityBrainLab-CT3D \cite{sheng2021ct3d} & 93.20 \% & 96.26 \% & 90.44 \% & 0.07 s / 1 core \\
DFAF3D \cite{tang2023dfaf3d} & 93.20 \% & 96.54 \% & 90.03 \% & 0.05 s / 1 core \\
MonoLSS \cite{monolss} & 93.11 \% & 95.99 \% & 83.14 \% & 0.04 s / 1 core \\
SNVC \cite{li2022stereo} & 93.09 \% & 96.27 \% & 85.51 \% & 1 s / GPU \\
H^23D R-CNN \cite{deng2021multi} & 93.03 \% & 96.13 \% & 90.33 \% & 0.03 s / 1 core \\
FromVoxelToPoint \cite{FromVoxelToPoint} & 92.98 \% & 96.07 \% & 90.40 \% & 0.1 s / 1 core \\
EBM3DOD \cite{gustafsson2020accurate} & 92.88 \% & 96.39 \% & 87.58 \% & 0.12 s / 1 core \\
Struc info fusion II \cite{sif} & 92.88 \% & 96.44 \% & 87.67 \% & 0.05 s / GPU \\
HotSpotNet \cite{chen2020object} & 92.74 \% & 96.20 \% & 89.68 \% & 0.04 s / 1 core \\
Struc info fusion I \cite{sif} & 92.71 \% & 96.24 \% & 87.55 \% & 0.05 s / 1 core \\
EBM3DOD baseline \cite{gustafsson2020accurate} & 92.70 \% & 96.31 \% & 87.44 \% & 0.05 s / 1 core \\
SARPNET \cite{ye2019sarpnet} & 92.58 \% & 95.82 \% & 87.33 \% & 0.05 s / 1 core \\
Patches \cite{lehner2019patch} & 92.57 \% & 96.31 \% & 87.41 \% & 0.15 s / GPU \\
R-GCN \cite{Zarzar2019PointRGCNGC} & 92.53 \% & 96.16 \% & 87.45 \% & 0.16 s / GPU \\
PI-RCNN \cite{xie2020pi} & 92.52 \% & 96.15 \% & 87.47 \% & 0.1 s / 1 core \\
CenterNet3D \cite{2007.07214} & 92.48 \% & 95.71 \% & 89.54 \% & 0.04 s / GPU \\
PointPainting \cite{vora2019pointpainting} & 92.43 \% & 98.36 \% & 89.49 \% & 0.4 s / GPU \\
3D IoU-Net \cite{Li20203DIoUNet} & 92.42 \% & 96.31 \% & 87.60 \% & 0.1 s / 1 core \\
CLOCs\_SecCas \cite{pang2020CLOCs} & 92.37 \% & 95.16 \% & 88.43 \% & 0.1 s / 1 core \\
ACDet \cite{acdet} & 92.36 \% & 96.07 \% & 89.18 \% & 0.05 s / 1 core \\
DASS \cite{Unal2021WACV} & 92.25 \% & 96.20 \% & 87.26 \% & 0.09 s / 1 core \\
S-AT GCN \cite{DBLPjournalscorrabs210308439} & 92.24 \% & 95.02 \% & 90.46 \% & 0.02 s / GPU \\
Sem-Aug-PointRCNN++ \cite{9830844} & 92.20 \% & 95.64 \% & 87.48 \% & 0.1 s / 8 cores \\
SegVoxelNet \cite{yi2020SegVoxelNet} & 92.16 \% & 95.86 \% & 86.90 \% & 0.04 s / 1 core \\
PointRGCN \cite{Zarzar2019PointRGCNGC} & 92.15 \% & 97.48 \% & 86.83 \% & 0.26 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 91.98 \% & 95.81 \% & 79.83 \% & 0.47 s / GPU \\
AB3DMOT \cite{Weng2019} & 91.87 \% & 95.86 \% & 86.78 \% & 0.0047s / 1 core \\
PASS-PointPillar \cite{context} & 91.82 \% & 95.15 \% & 88.31 \% & 1 s / 1 core \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 91.77 \% & 95.90 \% & 86.92 \% & 0.1 s / GPU \\
MMLab-PartA^2 \cite{shi2020part} & 91.73 \% & 95.00 \% & 88.86 \% & 0.08 s / GPU \\
C-GCN \cite{Zarzar2019PointRGCNGC} & 91.57 \% & 95.63 \% & 86.13 \% & 0.147 s / GPU \\
PointRGBNet \cite{Xie Desheng340} & 91.33 \% & 95.39 \% & 86.29 \% & 0.08 s / 4 cores \\
mmFUSION \cite{ahmad2023mmfusion} & 91.30 \% & 95.47 \% & 86.33 \% & 1s / 1 core \\
EgoNet \cite{Li2021CVPR} & 91.23 \% & 96.11 \% & 80.96 \% & 0.1 s / GPU \\
PFF3D \cite{9340187} & 91.06 \% & 94.86 \% & 86.28 \% & 0.05 s / GPU \\
Stereo CenterNet \cite{SHI2022219} & 91.02 \% & 96.54 \% & 83.15 \% & 0.04 s / GPU \\
Mix-Teaching \cite{Yang2022MixTeachingAS} & 90.84 \% & 96.31 \% & 83.11 \% & 30 s / 1 core \\
MonoFlex \cite{monoflex} & 90.82 \% & 95.95 \% & 83.11 \% & 0.03 s / GPU \\
Harmonic PointPillar \cite{context} & 90.78 \% & 94.23 \% & 87.42 \% & 0.01 s / 1 core \\
MAFF-Net(DAF-Pillar) \cite{zhang2020maffnet} & 90.78 \% & 94.17 \% & 83.17 \% & 0.04 s / 1 core \\
HRI-VoxelFPN \cite{Kuang2020voxelFPN} & 90.76 \% & 96.35 \% & 85.37 \% & 0.02 s / GPU \\
KM3D \cite{2009.00764} & 90.70 \% & 96.34 \% & 80.72 \% & 0.03 s / 1 core \\
PointPillars \cite{lang2018pointpillars} & 90.70 \% & 93.84 \% & 87.47 \% & 16 ms / \\
WS3D \cite{meng2020eccv} & 90.69 \% & 94.85 \% & 85.94 \% & 0.1 s / GPU \\
EOTL \cite{yang2023efficient} & 90.67 \% & 96.14 \% & 80.59 \% & TBD s / 1 core \\
DCD \cite{li2022densely} & 90.66 \% & 96.31 \% & 83.01 \% & 0.03 s / 1 core \\
NeurOCS \cite{Min2023CVPR} & 90.66 \% & 96.15 \% & 80.64 \% & 0.1 s / GPU \\
MonoEF \cite{Zhou2021CVPR} & 90.65 \% & 96.19 \% & 82.95 \% & 0.03 s / 1 core \\
CIE \cite{ye2022consistency} & 90.64 \% & 96.19 \% & 82.90 \% & 0.1 s / 1 core \\
VSAC \cite{ERROR: Wrong syntax in BIBTEX file.} & 90.57 \% & 96.16 \% & 87.72 \% & 0.07 s / 1 core \\
DID-M3D \cite{peng2022did} & 90.55 \% & 94.20 \% & 80.61 \% & 0.04 s / 1 core \\
QD-3DT \cite{Hu2021QD3DT} & 90.49 \% & 92.61 \% & 80.32 \% & 0.03 s / GPU \\
HomoLoss(monoflex) \cite{Gu2022CVPR} & 90.49 \% & 95.86 \% & 80.66 \% & 0.04 s / 1 core \\
monodle \cite{MA2021CVPR} & 90.23 \% & 93.46 \% & 80.11 \% & 0.04 s / GPU \\
3D IoU Loss \cite{zhou2019} & 90.21 \% & 95.60 \% & 84.96 \% & 0.08 s / GPU \\
MonoCInIS \cite{heylen2021monocinis} & 90.20 \% & 95.80 \% & 82.00 \% & 0,13 s / GPU \\
ARPNET \cite{Ye2019} & 90.11 \% & 93.42 \% & 82.56 \% & 0.08 s / GPU \\
TANet \cite{liu2019tanet} & 90.11 \% & 93.52 \% & 84.61 \% & 0.035s / GPU \\
CG-Stereo \cite{li2020confidence} & 89.98 \% & 96.28 \% & 82.21 \% & 0.57 s / \\
Deep3DBox \cite{MousavianCVPR2017} & 89.88 \% & 94.62 \% & 76.40 \% & 1.5 s / GPU \\
CMKD \cite{YuHCMKDECCV2022} & 89.81 \% & 95.07 \% & 83.24 \% & 0.1 s / 1 core \\
PS-fld \cite{Chen2022CVPR} & 89.78 \% & 95.60 \% & 81.68 \% & 0.25 s / 1 core \\
GPP \cite{rangesh2020ground} & 89.68 \% & 93.94 \% & 80.60 \% & 0.23 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 89.53 \% & 94.11 \% & 79.14 \% & 2 s / GPU \\
SCNet \cite{8813061} & 89.36 \% & 95.23 \% & 84.03 \% & 0.04 s / GPU \\
AVOD \cite{ku2018joint} & 89.22 \% & 94.98 \% & 82.14 \% & 0.08 s / \\
IAFA \cite{zhou2020iafa} & 89.14 \% & 92.96 \% & 79.40 \% & 0.04 s / 1 core \\
MonoDDE \cite{liu2020smoke} & 89.07 \% & 96.72 \% & 81.42 \% & 0.04 s / 1 core \\
ADD \cite{wu2022attention} & 88.96 \% & 94.58 \% & 80.78 \% & 0.1 s / 1 core \\
AVOD-FPN \cite{ku2018joint} & 88.61 \% & 94.65 \% & 83.71 \% & 0.1 s / \\
MonoUNI \cite{MonoUNI} & 88.50 \% & 94.10 \% & 78.35 \% & 0.04 s / 1 core \\
OPA-3D \cite{su2023opa} & 88.44 \% & 96.41 \% & 76.17 \% & 0.04 s / 1 core \\
DeepStereoOP \cite{Pham2017SPIC} & 87.81 \% & 93.68 \% & 77.60 \% & 3.4 s / GPU \\
MonoRUn \cite{monorun} & 87.64 \% & 95.44 \% & 77.75 \% & 0.07 s / GPU \\
3DBN \cite{DBLPjournalscorrabs190108373} & 87.59 \% & 93.34 \% & 79.91 \% & 0.13s / \\
FQNet \cite{liu2019deep} & 87.49 \% & 93.66 \% & 73.61 \% & 0.5 s / 1 core \\
Shift R-CNN (mono) \cite{shiftrcnn} & 87.47 \% & 93.75 \% & 77.19 \% & 0.25 s / GPU \\
MonoPSR \cite{ku2019monopsr} & 87.45 \% & 93.29 \% & 72.26 \% & 0.2 s / GPU \\
Mono3D \cite{Chen2016CVPR} & 87.28 \% & 93.13 \% & 77.00 \% & 4.2 s / GPU \\
SMOKE \cite{liu2020smoke} & 87.02 \% & 92.94 \% & 77.12 \% & 0.03 s / GPU \\
3DOP \cite{Chen2015NIPS} & 86.93 \% & 91.31 \% & 76.72 \% & 3s / GPU \\
CDN \cite{garg2020wasserstein} & 86.90 \% & 95.79 \% & 79.05 \% & 0.6 s / GPU \\
RTM3D \cite{li2020rtm3d} & 86.73 \% & 91.75 \% & 77.18 \% & 0.05 s / GPU \\
MonoDTR \cite{huang2022monodtr} & 86.70 \% & 93.12 \% & 74.53 \% & 0.04 s / 1 core \\
MonoRCNN \cite{MonoRCNNICCV21} & 86.48 \% & 91.90 \% & 66.71 \% & 0.07 s / GPU \\
MonoRCNN++ \cite{MonoRCNNWACV23} & 86.37 \% & 94.22 \% & 71.52 \% & 0.07 s / GPU \\
BirdNet+ \cite{barrera2021birdnet+} & 86.13 \% & 92.39 \% & 81.11 \% & 0.11 s / \\
MonoNeRD \cite{xu2023mononerd} & 86.13 \% & 94.24 \% & 76.38 \% & na s / 1 core \\
MonoPair \cite{chen2020cvpr} & 86.11 \% & 91.65 \% & 76.45 \% & 0.06 s / GPU \\
DSGN \cite{Chen2020dsgn} & 86.03 \% & 95.42 \% & 78.27 \% & 0.67 s / \\
DEVIANT \cite{kumar2022deviant} & 85.97 \% & 94.01 \% & 75.84 \% & 0.04 s / \\
GUPNet \cite{lu2021geometry} & 85.90 \% & 93.92 \% & 73.55 \% & NA s / 1 core \\
MonoDETR \cite{zhang2022monodetr} & 85.44 \% & 93.78 \% & 75.29 \% & 0.04 s / 1 core \\
DMF \cite{chen2022DMF} & 85.20 \% & 89.42 \% & 82.07 \% & 0.2 s / 1 core \\
StereoFENet \cite{monofenet} & 85.14 \% & 91.28 \% & 76.80 \% & 0.15 s / 1 core \\
PL++ (SDN+GDC) \cite{you2020pseudolidar} & 84.42 \% & 94.83 \% & 76.95 \% & 0.6 s / GPU \\
SS3D \cite{DBLPjournalscorrabs190608070} & 84.38 \% & 92.57 \% & 69.82 \% & 48 ms / \\
CDN-PL++ \cite{garg2020wasserstein} & 84.21 \% & 94.45 \% & 76.69 \% & 0.4 s / GPU \\
MonoFENet \cite{monofenet} & 84.09 \% & 91.42 \% & 75.93 \% & 0.15 s / 1 core \\
MonOAPC \cite{yao2023occlusion} & 83.97 \% & 92.34 \% & 74.42 \% & 0035 s / 1 core \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 83.89 \% & 91.77 \% & 79.24 \% & 0.06 s / GPU \\
ZoomNet \cite{xu2020zoomnet} & 83.79 \% & 94.14 \% & 68.78 \% & 0.3 s / 1 core \\
DLE \cite{ce21dle} & 83.19 \% & 94.06 \% & 61.13 \% & 0.06 s / \\
M3D-RPN \cite{brazil2019m3drpn} & 82.81 \% & 88.38 \% & 67.08 \% & 0.16 s / GPU \\
MonoLTKD\_V3 \cite{ERROR: Wrong syntax in BIBTEX file.} & 82.72 \% & 93.72 \% & 77.10 \% & 0.04 s / 1 core \\
SGM3D \cite{zhou2021sgm3d} & 82.51 \% & 93.46 \% & 72.67 \% & 0.03 s / 1 core \\
BKDStereo3D \cite{ERROR: Wrong syntax in BIBTEX file.} & 82.12 \% & 93.50 \% & 60.34 \% & 0.1 s / 1 core \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 82.09 \% & 93.31 \% & 69.78 \% & 0.387 s / GPU \\
D4LCN \cite{ding2019learning} & 82.08 \% & 90.01 \% & 63.98 \% & 0.2 s / GPU \\
CMAN \cite{CMAN2022} & 81.96 \% & 89.43 \% & 63.74 \% & 0.15 s / 1 core \\
Disp R-CNN \cite{sun2020disprcnn} & 81.96 \% & 93.49 \% & 67.35 \% & 0.387 s / GPU \\
Pseudo-LiDAR++ \cite{you2020pseudolidar} & 81.87 \% & 94.14 \% & 74.29 \% & 0.4 s / GPU \\
BS3D \cite{gahlert2019beyond} & 81.22 \% & 94.66 \% & 68.39 \% & 22 ms / \\
YOLOStereo3D \cite{liu2021yolostereo3d} & 80.88 \% & 93.65 \% & 61.17 \% & 0.1 s / \\
HomoLoss(imvoxelnet) \cite{Gu2022CVPR} & 80.67 \% & 91.94 \% & 70.64 \% & 0.20 s / 1 core \\
FRCNN+Or \cite{GuindelITSM} & 80.57 \% & 91.50 \% & 67.49 \% & 0.09 s / \\
DDMP-3D \cite{ddmp3d} & 80.20 \% & 90.73 \% & 61.82 \% & 0.18 s / 1 core \\
Ground-Aware \cite{9327478} & 80.05 \% & 90.98 \% & 60.51 \% & 0.05 s / 1 core \\
GrooMeD-NMS \cite{kumar2021groomed} & 79.93 \% & 90.05 \% & 63.43 \% & 0.12 s / 1 core \\
ESGN \cite{9869894} & 79.84 \% & 92.74 \% & 69.76 \% & 0.06 s / GPU \\
PGD-FCOS3D \cite{PGD} & 79.46 \% & 91.51 \% & 68.48 \% & 0.03 s / 1 core \\
YoloMono3D \cite{liu2021yolostereo3d} & 78.50 \% & 91.43 \% & 58.80 \% & 0.05 s / GPU \\
3D-GCK \cite{gahlert2020single} & 78.44 \% & 88.59 \% & 66.28 \% & 24 ms / \\
3D-SSMFCNN \cite{novakmaster2017} & 77.82 \% & 77.84 \% & 68.67 \% & 0.1 s / GPU \\
BKDStereo3D w/o KD \cite{ERROR: Wrong syntax in BIBTEX file.} & 77.76 \% & 91.96 \% & 58.69 \% & 0.1 s / 1 core \\
DFR-Net \cite{dfr2021} & 77.41 \% & 89.79 \% & 59.20 \% & 0.18 s / \\
AutoShape \cite{liu2021autoshape} & 77.31 \% & 86.41 \% & 64.06 \% & 0.04 s / 1 core \\
ImVoxelNet \cite{rukhovich2021imvoxelnet} & 77.18 \% & 89.07 \% & 67.35 \% & 0.2 s / GPU \\
Aug3D-RPN \cite{he2021aug3drpn} & 76.89 \% & 84.89 \% & 60.21 \% & 0.08 s / 1 core \\
FMF-occlusion-net \cite{liu2022fine} & 75.95 \% & 91.51 \% & 59.55 \% & 0.16 s / 1 core \\
3DVP \cite{Xiang2015CVPR} & 75.71 \% & 84.44 \% & 64.41 \% & 40 s / 8 cores \\
GS3D \cite{li2019gs3d} & 75.63 \% & 85.79 \% & 61.85 \% & 2 s / 1 core \\
Pose-RCNN \cite{braun2016pose} & 75.41 \% & 89.49 \% & 63.57 \% & 2 s / >8 cores \\
SubCat \cite{OhnBar2015TITS} & 75.26 \% & 83.31 \% & 59.55 \% & 0.7 s / 6 cores \\
Plane-Constraints \cite{yao2023vertex} & 75.18 \% & 82.46 \% & 66.51 \% & 0.05 s / 4 cores \\
3D FCN \cite{li2017iros} & 74.54 \% & 86.65 \% & 67.73 \% & >5 s / 1 core \\
Mobile Stereo R-CNN \cite{mobilestereorcnn2021} & 74.13 \% & 88.80 \% & 59.84 \% & 1.8 s / \\
OC Stereo \cite{pon2020object} & 73.34 \% & 86.86 \% & 61.37 \% & 0.35 s / 1 core \\
GAC3D \cite{gac3d2021} & 70.49 \% & 83.27 \% & 52.04 \% & 0.25 s / 1 core \\
ROI-10D \cite{manhardt2018roi10d} & 68.14 \% & 75.32 \% & 58.98 \% & 0.2 s / GPU \\
BirdNet+ (legacy) \cite{9294293} & 67.65 \% & 91.82 \% & 65.11 \% & 0.1 s / \\
multi-task CNN \cite{Oeljeklaus18} & 67.51 \% & 79.00 \% & 58.80 \% & 25.1 ms / GPU \\
CaDDN \cite{CaDDN} & 67.31 \% & 78.28 \% & 59.52 \% & 0.63 s / GPU \\
Decoupled-3D \cite{cai2020monocular} & 67.23 \% & 87.34 \% & 53.84 \% & 0.08 s / GPU \\
BdCost48LDCF \cite{FernandezBaldera2018} & 65.50 \% & 80.44 \% & 51.24 \% & 0.5 s / 8 cores \\
OC-DPM \cite{Pepik2013CVPR} & 65.32 \% & 77.35 \% & 51.00 \% & 10 s / 8 cores \\
RefinedMPL \cite{vianney2019refinedmpl} & 64.02 \% & 87.95 \% & 52.06 \% & 0.15 s / GPU \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 63.58 \% & 79.09 \% & 46.59 \% & 8 s / 1 core \\
AOG-View \cite{Li2014ECCV} & 62.62 \% & 77.62 \% & 48.27 \% & 3 s / 1 core \\
CIE + DM3D \cite{ye2022consistency} & 61.42 \% & 79.31 \% & 53.35 \% & 0.1 s / 1 core \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 57.48 \% & 70.23 \% & 42.54 \% & 10 s / 4 cores \\
SAMME48LDCF \cite{FernandezBaldera2018} & 57.26 \% & 76.28 \% & 43.55 \% & 0.5 s / 8 cores \\
BirdNet \cite{BirdNet2018} & 56.94 \% & 79.20 \% & 54.88 \% & 0.11 s / \\
Mono3D\_PLiDAR \cite{Weng2019} & 49.39 \% & 76.90 \% & 41.13 \% & 0.1 s / \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 48.00 \% & 57.76 \% & 35.52 \% & 15 s / 4 cores \\
LTN \cite{8836625} & 46.54 \% & 48.96 \% & 41.58 \% & 0.4 s / GPU \\
sensekitti \cite{binyang16craft} & 46.12 \% & 49.16 \% & 42.79 \% & 4.5 s / GPU \\
Kinematic3D \cite{brazil2020kinematic} & 45.50 \% & 58.33 \% & 34.81 \% & 0.12 s / 1 core \\
WeakM3D \cite{peng2022weakm3d} & 41.50 \% & 41.21 \% & 38.11 \% & 0.08 s / 1 core \\
MonoCInIS \cite{heylen2021monocinis} & 40.75 \% & 45.00 \% & 34.48 \% & 0,14 s / GPU \\
3D-CVF at SPA \cite{3DCVF} & 39.79 \% & 40.44 \% & 36.10 \% & 0.06 s / 1 core \\
Cube R-CNN \cite{brazil2023omni3d} & 39.78 \% & 38.09 \% & 35.40 \% & 0.05 s / GPU \\
SPG\_mini \cite{xu2021spg} & 38.75 \% & 39.26 \% & 38.57 \% & 0.09 s / GPU \\
SPG \cite{xu2021spg} & 38.73 \% & 40.02 \% & 38.52 \% & 0.09 s / 1 core \\
SA-SSD \cite{he2020sassd} & 38.30 \% & 39.40 \% & 37.07 \% & 0.04 s / 1 core \\
BtcDet \cite{xu2020behind} & 38.00 \% & 39.26 \% & 36.82 \% & 0.09 s / GPU \\
SSL-PointGNN \cite{erccelik20223d} & 37.21 \% & 38.55 \% & 36.53 \% & 0.56 s / GPU \\
Point-GNN \cite{shi2020pointgnn} & 37.20 \% & 38.66 \% & 36.29 \% & 0.6 s / GPU \\
RT3D-GMP \cite{konigshof2020learning} & 36.31 \% & 44.06 \% & 27.32 \% & 0.06 s / GPU \\
AOG \cite{Wu2016PAMI} & 29.81 \% & 33.28 \% & 23.91 \% & 3 s / 4 cores \\
SubCat48LDCF \cite{FernandezBaldera2018} & 26.68 \% & 34.33 \% & 19.44 \% & 0.5 s / 8 cores \\
RT3DStereo \cite{Koenigshof2019Objects} & 21.41 \% & 25.58 \% & 17.52 \% & 0.08 s / GPU \\
CSoR \cite{Plotkin2015} & 20.82 \% & 30.65 \% & 17.14 \% & 3.5 s / 4 cores \\
RT3D \cite{8403277} & 18.96 \% & 24.41 \% & 19.85 \% & 0.09 s / GPU \\
VoxelJones \cite{motro2019vehicular} & 15.41 \% & 17.83 \% & 14.13 \% & .18 s / 1 core \\
Associate-3Ddet \cite{Du2020CVPR} & 1.20 \% & 0.52 \% & 1.38 \% & 0.05 s / 1 core
\end{tabular}