\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
UDeerPEP \cite{dong2023pep} & 97.57 \% & 98.42 \% & 95.08 \% & 0.1 s / 1 core \\
VirConv-S \cite{VirConv} & 97.27 \% & 98.00 \% & 94.53 \% & 0.09 s / 1 core \\
GraR-VoI \cite{yang2022graphrcnn} & 96.38 \% & 96.81 \% & 91.20 \% & 0.07 s / 1 core \\
VirConv-T \cite{VirConv} & 96.38 \% & 98.93 \% & 93.56 \% & 0.09 s / 1 core \\
GraR-Po \cite{yang2022graphrcnn} & 96.18 \% & 96.84 \% & 91.11 \% & 0.06 s / 1 core \\
SFD \cite{wu2022sparse} & 96.17 \% & 98.97 \% & 91.13 \% & 0.1 s / 1 core \\
MLF-DET \cite{lin2023mlf} & 96.17 \% & 96.89 \% & 88.90 \% & 0.09 s / 1 core \\
VPFNet \cite{9826439} & 96.15 \% & 96.64 \% & 91.14 \% & 0.06 s / 2 cores \\
CLOCs \cite{pang2020CLOCs} & 96.07 \% & 96.77 \% & 91.11 \% & 0.1 s / 1 core \\
ACFNet \cite{10363115} & 96.06 \% & 96.68 \% & 93.36 \% & 0.11 s / 1 core \\
RDIoU \cite{sheng2022rdiou} & 96.05 \% & 98.79 \% & 91.03 \% & 0.03 s / 1 core \\
GraR-Vo \cite{yang2022graphrcnn} & 96.05 \% & 96.67 \% & 93.01 \% & 0.04 s / 1 core \\
TED \cite{TED} & 96.03 \% & 96.64 \% & 93.35 \% & 0.1 s / 1 core \\
CLOCs\_PVCas \cite{pang2020CLOCs} & 95.96 \% & 96.76 \% & 91.08 \% & 0.1 s / 1 core \\
PVT-SSD \cite{yang2023pvtssd} & 95.90 \% & 96.75 \% & 90.69 \% & 0.05 s / 1 core \\
UPIDet \cite{zhang2023upidet} & 95.89 \% & 96.25 \% & 93.25 \% & 0.11 s / 1 core \\
GraR-Pi \cite{yang2022graphrcnn} & 95.89 \% & 98.59 \% & 92.85 \% & 0.03 s / 1 core \\
OcTr \cite{zhou2023octr} & 95.84 \% & 96.48 \% & 90.99 \% & 0.06 s / GPU \\
VPA \cite{ERROR: Wrong syntax in BIBTEX file.} & 95.82 \% & 96.71 \% & 90.95 \% & 0.01 s / 1 core \\
3D Dual-Fusion \cite{kim20223d} & 95.82 \% & 96.54 \% & 93.11 \% & 0.1 s / 1 core \\
GLENet-VR \cite{zhang2023glenet} & 95.81 \% & 96.85 \% & 90.91 \% & 0.04 s / 1 core \\
TSSTDet \cite{10399338} & 95.81 \% & 96.65 \% & 93.05 \% & 0.08 s / 1 core \\
DVF-V \cite{mahmoud2022dense} & 95.77 \% & 96.60 \% & 90.89 \% & 0.1 s / 1 core \\
Fast-CLOCs \cite{Pang2022WACV} & 95.75 \% & 96.69 \% & 90.95 \% & 0.1 s / GPU \\
3D HANet \cite{10056279} & 95.73 \% & 98.61 \% & 92.96 \% & 0.1 s / 1 core \\
DSGN++ \cite{chen2022dsgn++} & 95.70 \% & 98.08 \% & 88.27 \% & 0.2 s / \\
CasA \cite{casa2022} & 95.62 \% & 96.52 \% & 92.86 \% & 0.1 s / 1 core \\
BADet \cite{qian2022BADet} & 95.61 \% & 98.75 \% & 90.64 \% & 0.14 s / 1 core \\
SE-SSD \cite{zheng2020ciassd} & 95.60 \% & 96.69 \% & 90.53 \% & 0.03 s / 1 core \\
FARP-Net \cite{10123008} & 95.57 \% & 96.11 \% & 93.07 \% & 0.06 s / GPU \\
LoGoNet \cite{li2023logonet} & 95.55 \% & 96.60 \% & 93.07 \% & 0.1 s / 1 core \\
GD-MAE \cite{yang2023gdmae} & 95.54 \% & 98.38 \% & 90.42 \% & 0.07 s / 1 core \\
DVF-PV \cite{mahmoud2022dense} & 95.49 \% & 96.42 \% & 92.57 \% & 0.1 s / 1 core \\
SPANet \cite{ye2021spanet} & 95.46 \% & 96.54 \% & 90.47 \% & 0.06 s / 1 core \\
PG-RCNN \cite{koo2023pgrcnn} & 95.40 \% & 96.66 \% & 90.55 \% & 0.06 s / GPU \\
SASA \cite{chen2022sasa} & 95.35 \% & 96.01 \% & 92.53 \% & 0.04 s / 1 core \\
TED-S Reproduced \cite{ERROR: Wrong syntax in BIBTEX file.} & 95.33 \% & 98.45 \% & 92.75 \% & 0.1 s / 1 core \\
SPG\_mini \cite{xu2021spg} & 95.32 \% & 96.23 \% & 92.68 \% & 0.09 s / GPU \\
EQ-PVRCNN \cite{Yang2022eqparadigm} & 95.32 \% & 98.23 \% & 92.65 \% & 0.2 s / GPU \\
Focals Conv \cite{focalsconvchen} & 95.28 \% & 96.30 \% & 92.69 \% & 0.1 s / 1 core \\
CasA++ \cite{casa2022} & 95.28 \% & 95.83 \% & 94.28 \% & 0.1 s / 1 core \\
VoxSeT \cite{voxset} & 95.23 \% & 96.16 \% & 90.49 \% & 33 ms / 1 core \\
PC-CNN-V2 \cite{8461232} & 95.20 \% & 96.06 \% & 89.37 \% & 0.5 s / GPU \\
VPFNet \cite{wang2021vpfnet} & 95.17 \% & 96.06 \% & 92.66 \% & 0.2 s / 1 core \\
F-PointNet \cite{qi2017frustum} & 95.17 \% & 95.85 \% & 85.42 \% & 0.17 s / GPU \\
EPNet++ \cite{9983516} & 95.17 \% & 96.73 \% & 92.10 \% & 0.1 s / GPU \\
SA-SSD \cite{he2020sassd} & 95.16 \% & 97.92 \% & 90.15 \% & 0.04 s / 1 core \\
HMFI \cite{li2022homogeneous} & 95.16 \% & 96.29 \% & 92.45 \% & 0.1 s / 1 core \\
USVLab BSAODet \cite{10052705} & 95.15 \% & 96.26 \% & 92.62 \% & 0.04 s / 1 core \\
Pyramid R-CNN \cite{mao2021pyramid} & 95.13 \% & 95.88 \% & 92.62 \% & 0.07 s / 1 core \\
Voxel R-CNN \cite{deng2020voxelrcnn} & 95.11 \% & 96.49 \% & 92.45 \% & 0.04 s / GPU \\
3DSSD \cite{yang3DSSD20} & 95.10 \% & 97.69 \% & 92.18 \% & 0.04 s / GPU \\
PDV \cite{PDV} & 95.00 \% & 96.07 \% & 92.44 \% & 0.1 s / 1 core \\
MVRA + I-FRCNN+ \cite{Choi2019ICCV} & 94.98 \% & 95.87 \% & 82.52 \% & 0.18 s / GPU \\
SIENet \cite{li2021sienet} & 94.97 \% & 96.02 \% & 92.40 \% & 0.08 s / 1 core \\
VoTr-TSD \cite{mao2021votr} & 94.94 \% & 95.97 \% & 92.44 \% & 0.07 s / 1 core \\
L-AUG \cite{cortinhal2023semanticsaware} & 94.92 \% & 95.84 \% & 92.22 \% & 0.1 s / 1 core \\
GraphAlign(ICCV2023) \cite{song2023graphalign} & 94.87 \% & 98.06 \% & 92.47 \% & 0.03 s / GPU \\
M3DeTR \cite{guan2021m3detr} & 94.83 \% & 97.39 \% & 92.10 \% & n/a s / GPU \\
StructuralIF \cite{sif3d2d} & 94.81 \% & 96.14 \% & 92.12 \% & 0.02 s / 8 cores \\
spark\_second\_focal\_w \cite{ERROR: Wrong syntax in BIBTEX file.} & 94.80 \% & 95.45 \% & 92.02 \% & 0.1 s / 1 core \\
XView \cite{xie2021xview} & 94.77 \% & 95.89 \% & 92.23 \% & 0.1 s / 1 core \\
P2V-RCNN \cite{P2VRCNN} & 94.73 \% & 96.03 \% & 92.34 \% & 0.1 s / 1 core \\
SPG \cite{xu2021spg} & 94.71 \% & 97.80 \% & 92.19 \% & 0.09 s / 1 core \\
CAT-Det \cite{zhang2022cat} & 94.71 \% & 95.97 \% & 92.07 \% & 0.3 s / GPU \\
MMLab PV-RCNN \cite{shi2020pv} & 94.70 \% & 98.17 \% & 92.04 \% & 0.08 s / 1 core \\
spark-part2 \cite{ERROR: Wrong syntax in BIBTEX file.} & 94.69 \% & 95.71 \% & 92.09 \% & 0.1 s / 1 core \\
SVGA-Net \cite{he2022svga} & 94.67 \% & 96.05 \% & 91.86 \% & 0.03s / 1 core \\
RangeDet (Official) \cite{Fan2021ICCV} & 94.64 \% & 95.50 \% & 91.77 \% & 0.02 s / 1 core \\
DSA-PV-RCNN \cite{bhattacharyya2021sadet3d} & 94.64 \% & 95.86 \% & 92.10 \% & 0.08 s / 1 core \\
RangeIoUDet \cite{liang2021rangeioudet} & 94.61 \% & 95.74 \% & 91.98 \% & 0.02 s / GPU \\
PASS-PV-RCNN-Plus \cite{context} & 94.59 \% & 95.79 \% & 92.10 \% & 1 s / 1 core \\
DVFENet \cite{HE2021} & 94.57 \% & 95.35 \% & 91.77 \% & 0.05 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 \\
pointpillar\_spark\_fo \cite{ERROR: Wrong syntax in BIBTEX file.} & 94.24 \% & 96.44 \% & 91.33 \% & 0.1 s / 1 core \\
SRDL \cite{ERROR: Wrong syntax in BIBTEX file.} & 94.24 \% & 95.86 \% & 91.80 \% & 0.05 s / 1 core \\
RangeRCNN \cite{liang2020rangercnn} & 94.03 \% & 95.48 \% & 91.74 \% & 0.06 s / GPU \\
Faraway-Frustum \cite{zhang2021faraway} & 93.99 \% & 95.81 \% & 91.72 \% & 0.1 s / GPU \\
DD3D \cite{dd3d} & 93.99 \% & 94.69 \% & 89.37 \% & n/a s / 1 core \\
SIF \cite{sif3d2d} & 93.95 \% & 95.51 \% & 91.57 \% & 0.1 s / 1 core \\
MGAF-3DSSD \cite{AnchorfreeMGASSD} & 93.87 \% & 94.45 \% & 86.37 \% & 0.1 s / 1 core \\
3ONet \cite{10183841} & 93.87 \% & 96.97 \% & 88.84 \% & 0.1 s / 1 core \\
LPCG-Monoflex \cite{peng2022lidar} & 93.86 \% & 96.90 \% & 83.94 \% & 0.03 s / 1 core \\
MMLAB LIGA-Stereo \cite{Guo2021ICCV} & 93.82 \% & 96.43 \% & 86.19 \% & 0.4 s / 1 core \\
Sem-Aug \cite{9830844} & 93.77 \% & 96.79 \% & 88.78 \% & 0.1 s / GPU \\
Patches - EMP \cite{lehner2019patch} & 93.75 \% & 97.91 \% & 90.56 \% & 0.5 s / GPU \\
KPTr \cite{ERROR: Wrong syntax in BIBTEX file.} & 93.73 \% & 96.55 \% & 90.84 \% & 0.07 s / 1 core \\
CIA-SSD \cite{zheng2020ciassd} & 93.72 \% & 96.87 \% & 86.20 \% & 0.03 s / 1 core \\
QD-3DT \cite{Hu2021QD3DT} & 93.66 \% & 94.26 \% & 83.63 \% & 0.03 s / GPU \\
MVAF-Net \cite{wang2020multi} & 93.66 \% & 95.37 \% & 90.90 \% & 0.06 s / 1 core \\
SSL-PointGNN \cite{erccelik20223d} & 93.65 \% & 96.61 \% & 88.53 \% & 0.56 s / GPU \\
PA3DNet \cite{10034840} & 93.62 \% & 96.57 \% & 88.65 \% & 0.1 s / GPU \\
IA-SSD (multi) \cite{zhang2022not} & 93.56 \% & 96.10 \% & 90.68 \% & 0.014 s / 1 core \\
MonoLiG \cite{hekimoglu2023monocular} & 93.56 \% & 96.70 \% & 83.74 \% & 0.03 s / 1 core \\
MonoPair \cite{chen2020cvpr} & 93.55 \% & 96.61 \% & 83.55 \% & 0.06 s / GPU \\
IA-SSD (single) \cite{zhang2022not} & 93.54 \% & 96.26 \% & 88.49 \% & 0.013 s / 1 core \\
EBM3DOD \cite{gustafsson2020accurate} & 93.54 \% & 96.81 \% & 88.33 \% & 0.12 s / 1 core \\
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 \\
BtcDet \cite{xu2020behind} & 93.47 \% & 96.23 \% & 88.55 \% & 0.09 s / GPU \\
LVFSD \cite{ERROR: Wrong syntax in BIBTEX file.} & 93.45 \% & 95.28 \% & 90.73 \% & 0.06 s / \\
Struc info fusion II \cite{sif} & 93.45 \% & 96.72 \% & 88.31 \% & 0.05 s / GPU \\
EBM3DOD baseline \cite{gustafsson2020accurate} & 93.45 \% & 96.72 \% & 88.25 \% & 0.05 s / 1 core \\
StereoDistill \cite{liu2020tanet} & 93.43 \% & 97.61 \% & 87.71 \% & 0.4 s / 1 core \\
MonoLSS \cite{monolss} & 93.42 \% & 96.19 \% & 83.62 \% & 0.04 s / 1 core \\
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 \\
SNVC \cite{li2022stereo} & 93.32 \% & 96.33 \% & 85.81 \% & 1 s / GPU \\
DFAF3D \cite{tang2023dfaf3d} & 93.32 \% & 96.58 \% & 90.24 \% & 0.05 s / 1 core \\
Struc info fusion I \cite{sif} & 93.31 \% & 96.59 \% & 88.23 \% & 0.05 s / 1 core \\
CityBrainLab-CT3D \cite{sheng2021ct3d} & 93.30 \% & 96.28 \% & 90.58 \% & 0.07 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 \\
H^23D R-CNN \cite{deng2021multi} & 93.20 \% & 96.20 \% & 90.55 \% & 0.03 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 \\
FromVoxelToPoint \cite{FromVoxelToPoint} & 93.06 \% & 96.08 \% & 90.53 \% & 0.1 s / 1 core \\
CLOCs\_SecCas \cite{pang2020CLOCs} & 92.95 \% & 95.43 \% & 89.21 \% & 0.1 s / 1 core \\
MonoCD \cite{yan2024monocd} & 92.91 \% & 96.43 \% & 85.55 \% & n/a s / 1 core \\
ACDet \cite{acdet} & 92.84 \% & 96.18 \% & 89.83 \% & 0.05 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 \\
Cube R-CNN \cite{brazil2023omni3d} & 92.72 \% & 95.78 \% & 84.81 \% & 0.05 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 \\
DASS \cite{Unal2021WACV} & 92.53 \% & 96.23 \% & 87.75 \% & 0.09 s / 1 core \\
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 \\
S-AT GCN \cite{DBLPjournalscorrabs210308439} & 92.44 \% & 95.06 \% & 90.78 \% & 0.02 s / GPU \\
PointRGCN \cite{Zarzar2019PointRGCNGC} & 92.33 \% & 97.51 \% & 87.07 \% & 0.26 s / GPU \\
Sem-Aug-PointRCNN++ \cite{9830844} & 92.32 \% & 95.65 \% & 87.62 \% & 0.1 s / 8 cores \\
Harmonic PointPillar \cite{context} & 92.25 \% & 95.16 \% & 89.11 \% & 0.01 s / 1 core \\
F-ConvNet \cite{wang2019frustum} & 92.19 \% & 95.85 \% & 80.09 \% & 0.47 s / GPU \\
PFF3D \cite{9340187} & 92.15 \% & 95.37 \% & 87.54 \% & 0.05 s / GPU \\
PASS-PointPillar \cite{context} & 92.09 \% & 95.20 \% & 88.73 \% & 1 s / 1 core \\
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 \\
mmFUSION \cite{ahmad2023mmfusion} & 91.84 \% & 95.69 \% & 87.05 \% & 1s / 1 core \\
WeakM3D \cite{peng2022weakm3d} & 91.81 \% & 94.51 \% & 85.35 \% & 0.08 s / 1 core \\
epBRM \cite{arxiv} & 91.77 \% & 94.59 \% & 88.45 \% & 0.1 s / GPU \\
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 \\
PointRGBNet \cite{Xie Desheng340} & 91.48 \% & 95.40 \% & 86.50 \% & 0.08 s / 4 cores \\
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 \\
EgoNet \cite{Li2021CVPR} & 91.39 \% & 96.18 \% & 81.33 \% & 0.1 s / GPU \\
Stereo CenterNet \cite{SHI2022219} & 91.27 \% & 96.61 \% & 83.50 \% & 0.04 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 \\
EOTL \cite{yang2023efficient} & 91.17 \% & 96.31 \% & 81.20 \% & TBD s / 1 core \\
WS3D \cite{meng2020eccv} & 91.15 \% & 95.13 \% & 86.52 \% & 0.1 s / GPU \\
NeurOCS \cite{Min2023CVPR} & 91.08 \% & 96.39 \% & 81.20 \% & 0.1 s / GPU \\
KM3D \cite{2009.00764} & 91.07 \% & 96.44 \% & 81.19 \% & 0.03 s / 1 core \\
DID-M3D \cite{peng2022did} & 91.04 \% & 94.29 \% & 81.31 \% & 0.04 s / 1 core \\
FII-CenterNet \cite{9316984} & 91.03 \% & 94.48 \% & 83.00 \% & 0.09 s / GPU \\
Aston-EAS \cite{wei2019enhanced} & 91.02 \% & 93.91 \% & 77.93 \% & 0.24 s / GPU \\
MonoFlex \cite{monoflex} & 91.02 \% & 96.01 \% & 83.38 \% & 0.03 s / GPU \\
Mix-Teaching \cite{Yang2022MixTeachingAS} & 91.02 \% & 96.35 \% & 83.41 \% & 30 s / 1 core \\
ARPNET \cite{Ye2019} & 90.99 \% & 94.00 \% & 83.49 \% & 0.08 s / GPU \\
CIE \cite{ye2022consistency} & 90.98 \% & 96.31 \% & 83.43 \% & 0.1 s / 1 core \\
DCD \cite{li2022densely} & 90.93 \% & 96.44 \% & 83.36 \% & 0.03 s / 1 core \\
MonoEF \cite{Zhou2021CVPR} & 90.88 \% & 96.32 \% & 83.27 \% & 0.03 s / 1 core \\
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 \\
monodle \cite{MA2021CVPR} & 90.81 \% & 93.83 \% & 80.93 \% & 0.04 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 / \\
HomoLoss(monoflex) \cite{Gu2022CVPR} & 90.69 \% & 95.92 \% & 80.91 \% & 0.04 s / 1 core \\
VSAC \cite{ERROR: Wrong syntax in BIBTEX file.} & 90.68 \% & 96.18 \% & 87.93 \% & 0.07 s / 1 core \\
TANet \cite{liu2019tanet} & 90.67 \% & 93.67 \% & 85.31 \% & 0.035s / GPU \\
MonoCInIS \cite{heylen2021monocinis} & 90.60 \% & 96.05 \% & 82.43 \% & 0,13 s / GPU \\
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 \\
CMKD \cite{YuHCMKDECCV2022} & 90.28 \% & 95.14 \% & 83.91 \% & 0.1 s / 1 core \\
PS-fld \cite{Chen2022CVPR} & 90.27 \% & 95.75 \% & 82.32 \% & 0.25 s / 1 core \\
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{rangesh2020ground} & 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 \\
ADD \cite{wu2022attention} & 89.53 \% & 94.82 \% & 81.60 \% & 0.1 s / 1 core \\
IAFA \cite{zhou2020iafa} & 89.46 \% & 93.08 \% & 79.83 \% & 0.04 s / 1 core \\
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 \\
MonoDDE \cite{liu2020smoke} & 89.19 \% & 96.76 \% & 81.60 \% & 0.04 s / 1 core \\
MonoUNI \cite{MonoUNI} & 88.96 \% & 94.30 \% & 78.95 \% & 0.04 s / 1 core \\
AVOD-FPN \cite{ku2018joint} & 88.92 \% & 94.70 \% & 84.13 \% & 0.1 s / \\
PCT \cite{wang2021pct} & 88.78 \% & 96.45 \% & 78.85 \% & 0.045 s / 1 core \\
OPA-3D \cite{su2023opa} & 88.77 \% & 96.50 \% & 76.55 \% & 0.04 s / 1 core \\
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 \\
RCD \cite{bewley2020range} & 88.46 \% & 92.52 \% & 83.73 \% & 0.1 s / GPU \\
MM-MRFC \cite{Costea2017CVPR} & 88.46 \% & 95.54 \% & 78.14 \% & 0.05 s / GPU \\
MonoDTR \cite{huang2022monodtr} & 88.41 \% & 93.90 \% & 76.20 \% & 0.04 s / 1 core \\
3DBN \cite{DBLPjournalscorrabs190108373} & 88.29 \% & 93.74 \% & 80.74 \% & 0.13s / \\
MonoCInIS \cite{heylen2021monocinis} & 88.16 \% & 96.22 \% & 75.72 \% & 0,14 s / GPU \\
MonoRUn \cite{monorun} & 87.91 \% & 95.48 \% & 78.10 \% & 0.07 s / GPU \\
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 \\
MonoNeRD \cite{xu2023mononerd} & 86.89 \% & 94.60 \% & 77.23 \% & na s / 1 core \\
MonoRCNN \cite{MonoRCNNICCV21} & 86.78 \% & 91.98 \% & 66.97 \% & 0.07 s / GPU \\
BirdNet+ \cite{barrera2021birdnet+} & 86.73 \% & 92.61 \% & 81.80 \% & 0.11 s / \\
MonoRCNN++ \cite{MonoRCNNWACV23} & 86.69 \% & 94.31 \% & 71.87 \% & 0.07 s / GPU \\
DEVIANT \cite{kumar2022deviant} & 86.64 \% & 94.42 \% & 76.69 \% & 0.04 s / \\
GUPNet \cite{lu2021geometry} & 86.45 \% & 94.15 \% & 74.18 \% & NA s / 1 core \\
DSGN \cite{Chen2020dsgn} & 86.43 \% & 95.53 \% & 78.75 \% & 0.67 s / \\
MonoDETR \cite{zhang2022monodetr} & 86.17 \% & 93.99 \% & 76.19 \% & 0.04 s / 1 core \\
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 \\
DMF \cite{chen2022DMF} & 85.49 \% & 89.50 \% & 82.52 \% & 0.2 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 \\
DLE \cite{ce21dle} & 84.45 \% & 94.66 \% & 62.10 \% & 0.06 s / \\
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 \\
MonOAPC \cite{yao2023occlusion} & 84.13 \% & 92.39 \% & 74.62 \% & 0035 s / 1 core \\
BKDStereo3D \cite{ERROR: Wrong syntax in BIBTEX file.} & 84.10 \% & 94.61 \% & 61.85 \% & 0.1 s / 1 core \\
ZoomNet \cite{xu2020zoomnet} & 83.92 \% & 94.22 \% & 69.00 \% & 0.3 s / 1 core \\
CMAN \cite{CMAN2022} & 83.74 \% & 89.74 \% & 65.35 \% & 0.15 s / 1 core \\
D4LCN \cite{ding2019learning} & 83.67 \% & 90.34 \% & 65.33 \% & 0.2 s / GPU \\
MonoLTKD\_V3 \cite{ERROR: Wrong syntax in BIBTEX file.} & 83.31 \% & 93.84 \% & 77.95 \% & 0.04 s / 1 core \\
Faster R-CNN \cite{Ren2015NIPS} & 83.16 \% & 88.97 \% & 72.62 \% & 2 s / GPU \\
SGM3D \cite{zhou2021sgm3d} & 83.05 \% & 93.66 \% & 73.35 \% & 0.03 s / 1 core \\
Pseudo-LiDAR++ \cite{you2020pseudolidar} & 82.90 \% & 94.46 \% & 75.45 \% & 0.4 s / GPU \\
Disp R-CNN \cite{sun2020disprcnn} & 82.86 \% & 93.64 \% & 68.33 \% & 0.387 s / GPU \\
BS3D \cite{gahlert2019beyond} & 82.72 \% & 95.35 \% & 70.01 \% & 22 ms / \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 82.64 \% & 93.45 \% & 70.45 \% & 0.387 s / GPU \\
HomoLoss(imvoxelnet) \cite{Gu2022CVPR} & 82.54 \% & 92.81 \% & 72.80 \% & 0.20 s / 1 core \\
YOLOStereo3D \cite{liu2021yolostereo3d} & 82.15 \% & 94.81 \% & 62.17 \% & 0.1 s / \\
Ground-Aware \cite{9327478} & 82.05 \% & 92.33 \% & 62.08 \% & 0.05 s / 1 core \\
FRCNN+Or \cite{GuindelITSM} & 82.00 \% & 92.91 \% & 68.79 \% & 0.09 s / \\
DDMP-3D \cite{ddmp3d} & 81.70 \% & 91.15 \% & 63.12 \% & 0.18 s / 1 core \\
BKDStereo3D w/o KD \cite{ERROR: Wrong syntax in BIBTEX file.} & 81.50 \% & 94.56 \% & 61.64 \% & 0.1 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 \\
CaDDN \cite{CaDDN} & 80.73 \% & 93.61 \% & 71.09 \% & 0.63 s / GPU \\
ESGN \cite{9869894} & 80.58 \% & 93.07 \% & 70.68 \% & 0.06 s / GPU \\
PGD-FCOS3D \cite{PGD} & 80.58 \% & 92.04 \% & 69.67 \% & 0.03 s / 1 core \\
GrooMeD-NMS \cite{kumar2021groomed} & 80.28 \% & 90.14 \% & 63.78 \% & 0.12 s / 1 core \\
3D-GCK \cite{gahlert2020single} & 80.19 \% & 89.55 \% & 68.08 \% & 24 ms / \\
YoloMono3D \cite{liu2021yolostereo3d} & 79.63 \% & 92.37 \% & 59.69 \% & 0.05 s / GPU \\
A3DODWTDA \cite{erino397fregu856master2018} & 79.15 \% & 82.98 \% & 68.30 \% & 0.08 s / GPU \\
ImVoxelNet \cite{rukhovich2021imvoxelnet} & 79.09 \% & 89.80 \% & 69.45 \% & 0.2 s / GPU \\
DFR-Net \cite{dfr2021} & 78.81 \% & 90.13 \% & 60.40 \% & 0.18 s / \\
spLBP \cite{Hu2016TITS} & 78.66 \% & 81.66 \% & 61.69 \% & 1.5 s / 8 cores \\
FMF-occlusion-net \cite{liu2022fine} & 78.21 \% & 92.33 \% & 61.58 \% & 0.16 s / 1 core \\
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 / \\
Aug3D-RPN \cite{he2021aug3drpn} & 77.88 \% & 85.57 \% & 61.16 \% & 0.08 s / 1 core \\
AutoShape \cite{liu2021autoshape} & 77.66 \% & 86.51 \% & 64.40 \% & 0.04 s / 1 core \\
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 \\
Mobile Stereo R-CNN \cite{mobilestereorcnn2021} & 76.73 \% & 90.08 \% & 62.23 \% & 1.8 s / \\
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 \\
Plane-Constraints \cite{yao2023vertex} & 75.43 \% & 82.54 \% & 66.82 \% & 0.05 s / 4 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 \\
GAC3D \cite{gac3d2021} & 70.73 \% & 83.30 \% & 52.23 \% & 0.25 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+ (legacy) \cite{9294293} & 68.05 \% & 92.10 \% & 65.61 \% & 0.1 s / \\
Decoupled-3D \cite{cai2020monocular} & 67.92 \% & 87.78 \% & 54.53 \% & 0.08 s / GPU \\
SparVox3D \cite{9558880} & 67.88 \% & 83.76 \% & 52.56 \% & 0.05 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 \\
MDSNet \cite{xie2022mds} & 62.74 \% & 85.94 \% & 50.27 \% & 0.05 s / 1 core \\
MODet \cite{zhang2019accurate} & 62.54 \% & 66.06 \% & 60.04 \% & 0.05 s / \\
CIE + DM3D \cite{ye2022consistency} & 61.54 \% & 79.36 \% & 53.56 \% & 0.1 s / 1 core \\
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 / \\
RT3D-GMP \cite{konigshof2020learning} & 51.95 \% & 62.41 \% & 39.14 \% & 0.06 s / GPU \\
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 \\
Neighbor-Vote \cite{chu2021neighborvote} & 0.00 \% & 0.00 \% & 0.00 \% & 0.1 s / GPU
\end{tabular}