\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
TED \cite{TED} & 84.08 \% & 92.46 \% & 78.07 \% & 0.1 s / 1 core \\
CasA++ \cite{casa2022} & 83.98 \% & 92.24 \% & 78.05 \% & 0.1 s / 1 core \\
UPIDet \cite{zhang2023upidet} & 83.78 \% & 89.86 \% & 76.98 \% & 0.11 s / 1 core \\
LoGoNet \cite{li2023logonet} & 83.51 \% & 89.90 \% & 77.41 \% & 0.1 s / 1 core \\
CasA \cite{casa2022} & 82.95 \% & 92.71 \% & 76.78 \% & 0.1 s / 1 core \\
RangeIoUDet \cite{liang2021rangeioudet} & 81.24 \% & 90.24 \% & 74.49 \% & 0.02 s / GPU \\
HMFI \cite{li2022homogeneous} & 81.13 \% & 89.09 \% & 74.30 \% & 0.1 s / 1 core \\
VPA \cite{ERROR: Wrong syntax in BIBTEX file.} & 81.12 \% & 89.41 \% & 74.43 \% & 0.01 s / 1 core \\
MLF-DET \cite{lin2023mlf} & 81.07 \% & 87.17 \% & 73.92 \% & 0.09 s / 1 core \\
USVLab BSAODet \cite{10052705} & 80.87 \% & 86.64 \% & 73.87 \% & 0.04 s / 1 core \\
CAT-Det \cite{zhang2022cat} & 80.25 \% & 87.79 \% & 73.41 \% & 0.3 s / GPU \\
EQ-PVRCNN \cite{Yang2022eqparadigm} & 80.09 \% & 88.92 \% & 73.79 \% & 0.2 s / GPU \\
DSA-PV-RCNN \cite{bhattacharyya2021sadet3d} & 80.05 \% & 88.52 \% & 74.20 \% & 0.08 s / 1 core \\
MMLab PV-RCNN \cite{shi2020pv} & 79.70 \% & 86.43 \% & 72.96 \% & 0.08 s / 1 core \\
PDV \cite{PDV} & 79.34 \% & 88.66 \% & 72.56 \% & 0.1 s / 1 core \\
KPTr \cite{ERROR: Wrong syntax in BIBTEX file.} & 79.10 \% & 87.02 \% & 72.03 \% & 0.07 s / 1 core \\
PASS-PV-RCNN-Plus \cite{context} & 78.82 \% & 86.15 \% & 72.28 \% & 1 s / 1 core \\
M3DeTR \cite{guan2021m3detr} & 78.80 \% & 87.21 \% & 71.88 \% & n/a s / GPU \\
IA-SSD (single) \cite{zhang2022not} & 78.34 \% & 88.78 \% & 71.63 \% & 0.013 s / 1 core \\
HotSpotNet \cite{chen2020object} & 78.31 \% & 85.79 \% & 71.24 \% & 0.04 s / 1 core \\
PG-RCNN \cite{koo2023pgrcnn} & 78.30 \% & 87.89 \% & 71.76 \% & 0.06 s / GPU \\
MMLab-PartA^2 \cite{shi2020part} & 77.52 \% & 88.70 \% & 70.41 \% & 0.08 s / GPU \\
DFAF3D \cite{tang2023dfaf3d} & 77.41 \% & 86.98 \% & 70.42 \% & 0.05 s / 1 core \\
PointPainting \cite{vora2019pointpainting} & 76.92 \% & 87.33 \% & 68.21 \% & 0.4 s / GPU \\
3ONet \cite{10183841} & 76.91 \% & 88.98 \% & 69.85 \% & 0.1 s / 1 core \\
GraphAlign(ICCV2023) \cite{song2023graphalign} & 76.81 \% & 84.53 \% & 71.90 \% & 0.03 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 76.71 \% & 86.39 \% & 66.92 \% & 0.47 s / GPU \\
P2V-RCNN \cite{P2VRCNN} & 76.52 \% & 88.21 \% & 69.90 \% & 0.1 s / 2 cores \\
ACFNet \cite{10363115} & 75.34 \% & 86.11 \% & 70.41 \% & 0.11 s / 1 core \\
Fast-CLOCs \cite{Pang2022WACV} & 74.74 \% & 89.54 \% & 67.54 \% & 0.1 s / GPU \\
SVGA-Net \cite{he2022svga} & 74.64 \% & 84.62 \% & 67.64 \% & 0.03s / 1 core \\
ACDet \cite{acdet} & 74.52 \% & 88.21 \% & 68.33 \% & 0.05 s / 1 core \\
VPFNet \cite{wang2021vpfnet} & 73.62 \% & 82.08 \% & 65.27 \% & 0.2 s / 1 core \\
DVFENet \cite{HE2021} & 73.43 \% & 85.32 \% & 66.87 \% & 0.05 s / 1 core \\
SRDL \cite{ERROR: Wrong syntax in BIBTEX file.} & 73.21 \% & 85.22 \% & 66.45 \% & 0.05 s / 1 core \\
L-AUG \cite{cortinhal2023semanticsaware} & 73.07 \% & 83.69 \% & 67.72 \% & 0.1 s / 1 core \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 72.81 \% & 85.94 \% & 65.84 \% & 0.1 s / GPU \\
SIF \cite{sif3d2d} & 72.73 \% & 84.96 \% & 64.94 \% & 0.1 s / 1 core \\
XView \cite{xie2021xview} & 72.70 \% & 87.59 \% & 64.96 \% & 0.1 s / 1 core \\
FromVoxelToPoint \cite{FromVoxelToPoint} & 72.62 \% & 86.71 \% & 65.42 \% & 0.1 s / 1 core \\
EOTL \cite{yang2023efficient} & 72.37 \% & 82.07 \% & 62.06 \% & TBD s / 1 core \\
H^23D R-CNN \cite{deng2021multi} & 72.20 \% & 85.09 \% & 65.25 \% & 0.03 s / 1 core \\
S-AT GCN \cite{DBLPjournalscorrabs210308439} & 71.04 \% & 82.31 \% & 65.13 \% & 0.02 s / GPU \\
LVFSD \cite{ERROR: Wrong syntax in BIBTEX file.} & 70.52 \% & 85.37 \% & 64.21 \% & 0.06 s / \\
MGAF-3DSSD \cite{AnchorfreeMGASSD} & 70.16 \% & 86.28 \% & 62.99 \% & 0.1 s / 1 core \\
IA-SSD (multi) \cite{zhang2022not} & 70.13 \% & 84.82 \% & 65.13 \% & 0.014 s / 1 core \\
AB3DMOT \cite{Weng2019} & 69.54 \% & 82.18 \% & 62.98 \% & 0.0047s / 1 core \\
VSAC \cite{ERROR: Wrong syntax in BIBTEX file.} & 69.14 \% & 88.31 \% & 62.03 \% & 0.07 s / 1 core \\
ARPNET \cite{Ye2019} & 68.72 \% & 82.61 \% & 62.00 \% & 0.08 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 68.55 \% & 83.79 \% & 61.71 \% & 16 ms / \\
EPNet++ \cite{9983516} & 67.26 \% & 79.81 \% & 61.75 \% & 0.1 s / GPU \\
TANet \cite{liu2019tanet} & 66.37 \% & 81.15 \% & 60.10 \% & 0.035s / GPU \\
PointRGBNet \cite{Xie Desheng340} & 65.68 \% & 79.64 \% & 59.48 \% & 0.08 s / 4 cores \\
PFF3D \cite{9340187} & 64.06 \% & 78.02 \% & 58.06 \% & 0.05 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 63.36 \% & 71.97 \% & 55.42 \% & 2 s / GPU \\
PiFeNet \cite{le2022accurate} & 62.62 \% & 77.54 \% & 55.66 \% & 0.03 s / 1 core \\
Pose-RCNN \cite{braun2016pose} & 62.02 \% & 75.74 \% & 53.99 \% & 2 s / >8 cores \\
SCNet \cite{8813061} & 61.11 \% & 77.77 \% & 54.82 \% & 0.04 s / GPU \\
DMF \cite{chen2022DMF} & 60.85 \% & 71.83 \% & 54.58 \% & 0.2 s / 1 core \\
BirdNet+ \cite{barrera2021birdnet+} & 59.44 \% & 67.52 \% & 54.27 \% & 0.11 s / \\
AVOD-FPN \cite{ku2018joint} & 58.70 \% & 69.21 \% & 53.47 \% & 0.1 s / \\
Deep3DBox \cite{MousavianCVPR2017} & 58.56 \% & 68.31 \% & 50.30 \% & 1.5 s / GPU \\
3DOP \cite{Chen2015NIPS} & 58.45 \% & 72.24 \% & 51.91 \% & 3s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 58.28 \% & 65.41 \% & 54.27 \% & 0.06 s / GPU \\
DD3D \cite{dd3d} & 57.42 \% & 73.60 \% & 50.90 \% & n/a s / 1 core \\
DeepStereoOP \cite{Pham2017SPIC} & 56.55 \% & 69.36 \% & 49.37 \% & 3.4 s / GPU \\
MonoLiG \cite{hekimoglu2023monocular} & 54.91 \% & 76.10 \% & 47.58 \% & 0.03 s / 1 core \\
Mix-Teaching \cite{Yang2022MixTeachingAS} & 54.00 \% & 70.90 \% & 46.66 \% & 30 s / 1 core \\
Mono3D \cite{Chen2016CVPR} & 53.96 \% & 67.33 \% & 47.91 \% & 4.2 s / GPU \\
AVOD \cite{ku2018joint} & 51.05 \% & 64.81 \% & 45.12 \% & 0.08 s / \\
BirdNet+ (legacy) \cite{9294293} & 50.94 \% & 69.92 \% & 47.01 \% & 0.1 s / \\
FRCNN+Or \cite{GuindelITSM} & 49.53 \% & 63.45 \% & 43.65 \% & 0.09 s / \\
MonoPSR \cite{ku2019monopsr} & 49.32 \% & 58.63 \% & 43.05 \% & 0.2 s / GPU \\
StereoDistill \cite{liu2020tanet} & 48.99 \% & 65.65 \% & 43.14 \% & 0.4 s / 1 core \\
MonoFlex \cite{monoflex} & 47.91 \% & 65.51 \% & 40.40 \% & 0.03 s / GPU \\
HomoLoss(monoflex) \cite{Gu2022CVPR} & 47.36 \% & 62.89 \% & 40.55 \% & 0.04 s / 1 core \\
MonoLSS \cite{monolss} & 47.09 \% & 65.31 \% & 41.74 \% & 0.04 s / 1 core \\
QD-3DT \cite{Hu2021QD3DT} & 46.24 \% & 64.64 \% & 40.58 \% & 0.03 s / GPU \\
DSGN++ \cite{chen2022dsgn++} & 45.94 \% & 57.93 \% & 41.93 \% & 0.2 s / \\
MonoDDE \cite{liu2020smoke} & 45.58 \% & 63.91 \% & 39.29 \% & 0.04 s / 1 core \\
LPCG-Monoflex \cite{peng2022lidar} & 45.24 \% & 63.07 \% & 39.28 \% & 0.03 s / 1 core \\
MonoUNI \cite{MonoUNI} & 45.21 \% & 62.21 \% & 38.28 \% & 0.04 s / 1 core \\
MMLAB LIGA-Stereo \cite{Guo2021ICCV} & 45.13 \% & 63.89 \% & 39.23 \% & 0.4 s / 1 core \\
monodle \cite{MA2021CVPR} & 45.12 \% & 61.84 \% & 37.95 \% & 0.04 s / GPU \\
BirdNet \cite{BirdNet2018} & 45.03 \% & 62.69 \% & 41.88 \% & 0.11 s / \\
MonOAPC \cite{yao2023occlusion} & 44.74 \% & 60.40 \% & 38.01 \% & 0035 s / 1 core \\
SparsePool \cite{wang2017fusing} & 43.50 \% & 59.77 \% & 39.36 \% & 0.13 s / 8 cores \\
MonoDTR \cite{huang2022monodtr} & 42.45 \% & 56.40 \% & 36.32 \% & 0.04 s / 1 core \\
sensekitti \cite{binyang16craft} & 41.14 \% & 47.48 \% & 35.07 \% & 4.5 s / GPU \\
CG-Stereo \cite{li2020confidence} & 40.64 \% & 60.24 \% & 35.55 \% & 0.57 s / \\
MonoRCNN++ \cite{MonoRCNNWACV23} & 39.84 \% & 56.32 \% & 34.82 \% & 0.07 s / GPU \\
MonoPair \cite{chen2020cvpr} & 39.47 \% & 53.36 \% & 33.95 \% & 0.06 s / GPU \\
CMKD \cite{YuHCMKDECCV2022} & 38.70 \% & 56.46 \% & 34.00 \% & 0.1 s / 1 core \\
DEVIANT \cite{kumar2022deviant} & 38.46 \% & 57.64 \% & 32.76 \% & 0.04 s / \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 35.93 \% & 52.35 \% & 31.09 \% & 0.387 s / GPU \\
Disp R-CNN \cite{sun2020disprcnn} & 35.92 \% & 52.37 \% & 31.08 \% & 0.387 s / GPU \\
GUPNet \cite{lu2021geometry} & 35.03 \% & 55.03 \% & 31.18 \% & NA s / 1 core \\
Shift R-CNN (mono) \cite{shiftrcnn} & 34.77 \% & 51.95 \% & 31.10 \% & 0.25 s / GPU \\
SparsePool \cite{wang2017fusing} & 34.56 \% & 43.33 \% & 31.09 \% & 0.13 s / 8 cores \\
MonoRUn \cite{monorun} & 34.36 \% & 49.04 \% & 30.22 \% & 0.07 s / GPU \\
SPG\_mini \cite{xu2021spg} & 34.28 \% & 36.23 \% & 32.09 \% & 0.09 s / GPU \\
BtcDet \cite{xu2020behind} & 33.94 \% & 35.79 \% & 31.90 \% & 0.09 s / GPU \\
Plane-Constraints \cite{yao2023vertex} & 32.87 \% & 48.36 \% & 28.52 \% & 0.05 s / 4 cores \\
Point-GNN \cite{shi2020pointgnn} & 32.37 \% & 36.29 \% & 29.81 \% & 0.6 s / GPU \\
MonoEF \cite{Zhou2021CVPR} & 32.19 \% & 43.70 \% & 27.93 \% & 0.03 s / 1 core \\
D4LCN \cite{ding2019learning} & 31.70 \% & 48.03 \% & 26.99 \% & 0.2 s / GPU \\
OPA-3D \cite{su2023opa} & 31.64 \% & 45.97 \% & 27.92 \% & 0.04 s / 1 core \\
M3D-RPN \cite{brazil2019m3drpn} & 31.09 \% & 48.11 \% & 26.10 \% & 0.16 s / GPU \\
Aug3D-RPN \cite{he2021aug3drpn} & 30.01 \% & 42.60 \% & 24.74 \% & 0.08 s / 1 core \\
DDMP-3D \cite{ddmp3d} & 29.53 \% & 46.42 \% & 25.91 \% & 0.18 s / 1 core \\
PS-fld \cite{Chen2022CVPR} & 27.99 \% & 41.21 \% & 24.75 \% & 0.25 s / 1 core \\
SS3D \cite{DBLPjournalscorrabs190608070} & 27.79 \% & 42.95 \% & 24.26 \% & 48 ms / \\
CMAN \cite{CMAN2022} & 27.63 \% & 42.58 \% & 23.14 \% & 0.15 s / 1 core \\
DFR-Net \cite{dfr2021} & 24.85 \% & 38.60 \% & 21.86 \% & 0.18 s / \\
MonoLTKD\_V3 \cite{ERROR: Wrong syntax in BIBTEX file.} & 24.18 \% & 37.61 \% & 21.44 \% & 0.04 s / 1 core \\
Cube R-CNN \cite{brazil2023omni3d} & 23.98 \% & 29.00 \% & 21.67 \% & 0.05 s / GPU \\
DSGN \cite{Chen2020dsgn} & 20.28 \% & 29.76 \% & 19.13 \% & 0.67 s / \\
MonoNeRD \cite{xu2023mononerd} & 20.13 \% & 30.64 \% & 18.19 \% & na s / 1 core \\
CaDDN \cite{CaDDN} & 19.96 \% & 30.35 \% & 17.38 \% & 0.63 s / GPU \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 19.15 \% & 26.05 \% & 18.02 \% & 10 s / 4 cores \\
PGD-FCOS3D \cite{PGD} & 19.10 \% & 31.75 \% & 16.59 \% & 0.03 s / 1 core \\
OC Stereo \cite{pon2020object} & 18.99 \% & 29.07 \% & 16.40 \% & 0.35 s / 1 core \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 18.92 \% & 27.97 \% & 17.43 \% & 8 s / 1 core \\
CIE \cite{ye2022consistency} & 17.52 \% & 24.39 \% & 15.84 \% & 0.1 s / 1 core \\
SGM3D \cite{zhou2021sgm3d} & 16.50 \% & 25.51 \% & 15.09 \% & 0.03 s / 1 core \\
RT3D-GMP \cite{konigshof2020learning} & 16.18 \% & 23.91 \% & 14.23 \% & 0.06 s / GPU \\
RefinedMPL \cite{vianney2019refinedmpl} & 16.02 \% & 26.54 \% & 13.20 \% & 0.15 s / GPU \\
FMF-occlusion-net \cite{liu2022fine} & 15.24 \% & 23.82 \% & 13.84 \% & 0.16 s / 1 core \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 14.64 \% & 23.93 \% & 13.09 \% & 15 s / 4 cores \\
ESGN \cite{9869894} & 7.73 \% & 12.50 \% & 6.80 \% & 0.06 s / GPU \\
RT3DStereo \cite{Koenigshof2019Objects} & 3.88 \% & 5.46 \% & 3.54 \% & 0.08 s / GPU
\end{tabular}