\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
VMVS \cite{ku2018joint} & 68.19 \% & 79.98 \% & 63.18 \% & 0.25 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 66.70 \% & 79.65 \% & 61.35 \% & 2 s / GPU \\
DD3D \cite{dd3d} & 63.92 \% & 77.09 \% & 59.41 \% & n/a s / 1 core \\
F-ConvNet \cite{wang2019frustum} & 63.87 \% & 75.19 \% & 58.57 \% & 0.47 s / GPU \\
CasA++ \cite{casa2022} & 61.59 \% & 71.78 \% & 58.71 \% & 0.1 s / 1 core \\
3DOP \cite{Chen2015NIPS} & 61.48 \% & 74.22 \% & 55.89 \% & 3s / GPU \\
TED \cite{TED} & 61.44 \% & 71.72 \% & 58.59 \% & 0.1 s / 1 core \\
LoGoNet \cite{li2023logonet} & 60.70 \% & 69.16 \% & 58.13 \% & 0.1 s / 1 core \\
HotSpotNet \cite{chen2020object} & 60.65 \% & 70.36 \% & 57.42 \% & 0.04 s / 1 core \\
MonoLSS \cite{monolss} & 60.28 \% & 75.13 \% & 53.85 \% & 0.04 s / 1 core \\
DeepStereoOP \cite{Pham2017SPIC} & 60.15 \% & 73.76 \% & 55.30 \% & 3.4 s / GPU \\
Pose-RCNN \cite{braun2016pose} & 59.84 \% & 76.24 \% & 53.59 \% & 2 s / >8 cores \\
USVLab BSAODet \cite{10052705} & 59.73 \% & 69.95 \% & 55.85 \% & 0.04 s / 1 core \\
CasA \cite{casa2022} & 59.69 \% & 70.33 \% & 56.89 \% & 0.1 s / 1 core \\
FFNet \cite{zhao2019monocular} & 58.87 \% & 69.24 \% & 53.75 \% & 1.07 s / GPU \\
Mono3D \cite{Chen2016CVPR} & 58.66 \% & 71.19 \% & 53.94 \% & 4.2 s / GPU \\
VPFNet \cite{wang2021vpfnet} & 58.63 \% & 67.96 \% & 54.77 \% & 0.2 s / 1 core \\
P2V-RCNN \cite{P2VRCNN} & 57.94 \% & 68.67 \% & 55.07 \% & 0.1 s / 2 cores \\
Fast-CLOCs \cite{Pang2022WACV} & 57.35 \% & 70.93 \% & 54.48 \% & 0.1 s / GPU \\
EOTL \cite{yang2023efficient} & 57.17 \% & 68.99 \% & 51.48 \% & TBD s / 1 core \\
MLF-DET \cite{lin2023mlf} & 56.89 \% & 64.49 \% & 53.17 \% & 0.09 s / 1 core \\
DFAF3D \cite{tang2023dfaf3d} & 54.99 \% & 65.42 \% & 51.21 \% & 0.05 s / 1 core \\
3ONet \cite{10183841} & 54.88 \% & 66.35 \% & 50.82 \% & 0.1 s / 1 core \\
FromVoxelToPoint \cite{FromVoxelToPoint} & 54.80 \% & 66.21 \% & 52.03 \% & 0.1 s / 1 core \\
MonoPSR \cite{ku2019monopsr} & 54.65 \% & 68.98 \% & 50.07 \% & 0.2 s / GPU \\
DSA-PV-RCNN \cite{bhattacharyya2021sadet3d} & 54.38 \% & 63.12 \% & 51.98 \% & 0.08 s / 1 core \\
PDV \cite{PDV} & 54.08 \% & 63.43 \% & 50.75 \% & 0.1 s / 1 core \\
ACFNet \cite{10363115} & 53.97 \% & 65.55 \% & 49.97 \% & 0.11 s / 1 core \\
PASS-PV-RCNN-Plus \cite{context} & 53.82 \% & 63.49 \% & 51.30 \% & 1 s / 1 core \\
monodle \cite{MA2021CVPR} & 53.78 \% & 69.94 \% & 48.98 \% & 0.04 s / GPU \\
VPA \cite{ERROR: Wrong syntax in BIBTEX file.} & 53.76 \% & 66.61 \% & 51.11 \% & 0.01 s / 1 core \\
MGAF-3DSSD \cite{AnchorfreeMGASSD} & 53.73 \% & 64.69 \% & 49.84 \% & 0.1 s / 1 core \\
SRDL \cite{ERROR: Wrong syntax in BIBTEX file.} & 53.36 \% & 63.39 \% & 50.43 \% & 0.05 s / 1 core \\
PG-RCNN \cite{koo2023pgrcnn} & 53.12 \% & 63.73 \% & 50.46 \% & 0.06 s / GPU \\
LVFSD \cite{ERROR: Wrong syntax in BIBTEX file.} & 52.72 \% & 63.39 \% & 49.93 \% & 0.06 s / \\
IA-SSD (single) \cite{zhang2022not} & 52.69 \% & 62.90 \% & 50.27 \% & 0.013 s / 1 core \\
MonoUNI \cite{MonoUNI} & 52.62 \% & 69.15 \% & 47.89 \% & 0.04 s / 1 core \\
HMFI \cite{li2022homogeneous} & 52.47 \% & 63.10 \% & 49.57 \% & 0.1 s / 1 core \\
MMLab PV-RCNN \cite{shi2020pv} & 52.42 \% & 63.45 \% & 49.23 \% & 0.08 s / 1 core \\
SVGA-Net \cite{he2022svga} & 52.27 \% & 62.33 \% & 49.44 \% & 0.03s / 1 core \\
MMLab-PartA^2 \cite{shi2020part} & 52.20 \% & 63.51 \% & 48.27 \% & 0.08 s / GPU \\
FRCNN+Or \cite{GuindelITSM} & 52.15 \% & 67.03 \% & 47.14 \% & 0.09 s / \\
SIF \cite{sif3d2d} & 52.10 \% & 62.72 \% & 49.19 \% & 0.1 s / 1 core \\
QD-3DT \cite{Hu2021QD3DT} & 51.46 \% & 68.64 \% & 47.00 \% & 0.03 s / GPU \\
ACDet \cite{acdet} & 50.90 \% & 62.39 \% & 48.34 \% & 0.05 s / 1 core \\
GUPNet \cite{lu2021geometry} & 50.74 \% & 68.93 \% & 44.01 \% & NA s / 1 core \\
DEVIANT \cite{kumar2022deviant} & 50.66 \% & 68.78 \% & 45.89 \% & 0.04 s / \\
DVFENet \cite{HE2021} & 50.52 \% & 60.32 \% & 47.92 \% & 0.05 s / 1 core \\
OPA-3D \cite{su2023opa} & 50.42 \% & 68.35 \% & 43.91 \% & 0.04 s / 1 core \\
PointPainting \cite{vora2019pointpainting} & 50.22 \% & 59.25 \% & 46.95 \% & 0.4 s / GPU \\
Mix-Teaching \cite{Yang2022MixTeachingAS} & 50.19 \% & 64.04 \% & 44.37 \% & 30 s / 1 core \\
M3DeTR \cite{guan2021m3detr} & 50.09 \% & 58.90 \% & 47.66 \% & n/a s / GPU \\
IA-SSD (multi) \cite{zhang2022not} & 49.58 \% & 62.51 \% & 47.17 \% & 0.014 s / 1 core \\
XView \cite{xie2021xview} & 49.30 \% & 58.39 \% & 46.81 \% & 0.1 s / 1 core \\
ARPNET \cite{Ye2019} & 48.49 \% & 60.47 \% & 45.02 \% & 0.08 s / GPU \\
DPPFA-Net \cite{10308573} & 48.38 \% & 56.13 \% & 45.93 \% & 0.1 s / 1 core \\
PointPillars \cite{lang2018pointpillars} & 48.05 \% & 57.47 \% & 45.40 \% & 16 ms / \\
MonoRUn \cite{monorun} & 47.82 \% & 63.28 \% & 43.23 \% & 0.07 s / GPU \\
L-AUG \cite{cortinhal2023semanticsaware} & 47.59 \% & 58.42 \% & 44.64 \% & 0.1 s / 1 core \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 47.33 \% & 57.19 \% & 44.31 \% & 0.1 s / GPU \\
S-AT GCN \cite{DBLPjournalscorrabs210308439} & 46.64 \% & 56.55 \% & 44.23 \% & 0.02 s / GPU \\
PiFeNet \cite{le2022accurate} & 46.59 \% & 55.11 \% & 44.14 \% & 0.03 s / 1 core \\
Shift R-CNN (mono) \cite{shiftrcnn} & 46.56 \% & 64.73 \% & 41.86 \% & 0.25 s / GPU \\
VSAC \cite{ERROR: Wrong syntax in BIBTEX file.} & 45.97 \% & 58.47 \% & 43.24 \% & 0.07 s / 1 core \\
Disp R-CNN \cite{sun2020disprcnn} & 45.80 \% & 63.23 \% & 41.32 \% & 0.387 s / GPU \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 45.66 \% & 63.16 \% & 41.14 \% & 0.387 s / GPU \\
HomoLoss(monoflex) \cite{Gu2022CVPR} & 45.44 \% & 59.94 \% & 41.15 \% & 0.04 s / 1 core \\
Plane-Constraints \cite{yao2023vertex} & 44.76 \% & 57.28 \% & 40.56 \% & 0.05 s / 4 cores \\
MonoFlex \cite{monoflex} & 44.20 \% & 58.96 \% & 39.89 \% & 0.03 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 43.99 \% & 53.48 \% & 41.56 \% & 0.1 s / \\
CAT-Det \cite{zhang2022cat} & 43.86 \% & 52.75 \% & 41.15 \% & 0.3 s / GPU \\
DSGN++ \cite{chen2022dsgn++} & 43.35 \% & 54.16 \% & 40.10 \% & 0.2 s / \\
EPNet++ \cite{9983516} & 43.29 \% & 51.89 \% & 40.98 \% & 0.1 s / GPU \\
Frustum-PointPillars \cite{paigwarhal03354114} & 42.97 \% & 49.04 \% & 40.69 \% & 0.06 s / 4 cores \\
MonoRCNN++ \cite{MonoRCNNWACV23} & 42.54 \% & 56.59 \% & 36.64 \% & 0.07 s / GPU \\
MonOAPC \cite{yao2023occlusion} & 42.52 \% & 56.84 \% & 38.43 \% & 0035 s / 1 core \\
MonoPair \cite{chen2020cvpr} & 42.38 \% & 55.26 \% & 38.53 \% & 0.06 s / GPU \\
MonoDDE \cite{liu2020smoke} & 41.09 \% & 55.28 \% & 36.85 \% & 0.04 s / 1 core \\
PFF3D \cite{9340187} & 40.99 \% & 48.75 \% & 38.99 \% & 0.05 s / GPU \\
MMLAB LIGA-Stereo \cite{Guo2021ICCV} & 40.98 \% & 53.16 \% & 38.12 \% & 0.4 s / 1 core \\
LPCG-Monoflex \cite{peng2022lidar} & 39.79 \% & 56.60 \% & 35.42 \% & 0.03 s / 1 core \\
AB3DMOT \cite{Weng2019} & 39.76 \% & 50.30 \% & 36.90 \% & 0.0047s / 1 core \\
SS3D \cite{DBLPjournalscorrabs190608070} & 39.60 \% & 53.72 \% & 35.40 \% & 48 ms / \\
SemanticVoxels \cite{fei2020semanticvoxels} & 38.95 \% & 45.59 \% & 37.21 \% & 0.04 s / GPU \\
MonoLiG \cite{hekimoglu2023monocular} & 38.92 \% & 52.66 \% & 35.05 \% & 0.03 s / 1 core \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 37.79 \% & 52.91 \% & 33.27 \% & 8 s / 1 core \\
StereoDistill \cite{liu2020tanet} & 37.58 \% & 48.49 \% & 34.41 \% & 0.4 s / 1 core \\
EQ-PVRCNN \cite{Yang2022eqparadigm} & 36.49 \% & 43.67 \% & 34.67 \% & 0.2 s / GPU \\
CG-Stereo \cite{li2020confidence} & 36.47 \% & 48.23 \% & 32.77 \% & 0.57 s / \\
TANet \cite{liu2019tanet} & 36.21 \% & 42.54 \% & 34.39 \% & 0.035s / GPU \\
YOLOStereo3D \cite{liu2021yolostereo3d} & 35.62 \% & 48.99 \% & 31.58 \% & 0.1 s / \\
SCNet \cite{8813061} & 35.49 \% & 44.50 \% & 33.38 \% & 0.04 s / GPU \\
MonoDTR \cite{huang2022monodtr} & 35.11 \% & 49.41 \% & 31.41 \% & 0.04 s / 1 core \\
BirdNet+ \cite{barrera2021birdnet+} & 35.01 \% & 41.84 \% & 33.03 \% & 0.11 s / \\
MonoEF \cite{Zhou2021CVPR} & 34.63 \% & 47.45 \% & 31.01 \% & 0.03 s / 1 core \\
sensekitti \cite{binyang16craft} & 34.26 \% & 41.03 \% & 31.51 \% & 4.5 s / GPU \\
D4LCN \cite{ding2019learning} & 33.62 \% & 46.73 \% & 28.71 \% & 0.2 s / GPU \\
DDMP-3D \cite{ddmp3d} & 33.35 \% & 46.12 \% & 28.45 \% & 0.18 s / 1 core \\
SparsePool \cite{wang2017fusing} & 33.35 \% & 43.86 \% & 29.99 \% & 0.13 s / 8 cores \\
SparsePool \cite{wang2017fusing} & 33.29 \% & 43.52 \% & 30.01 \% & 0.13 s / 8 cores \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 33.01 \% & 45.60 \% & 29.27 \% & 10 s / 4 cores \\
PointRGBNet \cite{Xie Desheng340} & 32.57 \% & 43.08 \% & 29.17 \% & 0.08 s / 4 cores \\
AVOD \cite{ku2018joint} & 32.19 \% & 42.54 \% & 29.09 \% & 0.08 s / \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 32.13 \% & 37.32 \% & 28.94 \% & 0.06 s / GPU \\
RPN+BF \cite{Zhang2016ECCV} & 32.12 \% & 41.19 \% & 28.83 \% & 0.6 s / GPU \\
DMF \cite{chen2022DMF} & 32.00 \% & 39.86 \% & 30.12 \% & 0.2 s / 1 core \\
CMKD \cite{YuHCMKDECCV2022} & 31.97 \% & 42.60 \% & 29.13 \% & 0.1 s / 1 core \\
M3D-RPN \cite{brazil2019m3drpn} & 31.88 \% & 44.33 \% & 28.55 \% & 0.16 s / GPU \\
Point-GNN \cite{shi2020pointgnn} & 31.86 \% & 39.16 \% & 29.65 \% & 0.6 s / GPU \\
SubCat \cite{OhnBar2014CVPRWORK} & 31.26 \% & 42.31 \% & 27.39 \% & 1.2 s / 6 cores \\
Aug3D-RPN \cite{he2021aug3drpn} & 29.75 \% & 40.50 \% & 25.96 \% & 0.08 s / 1 core \\
BirdNet+ (legacy) \cite{9294293} & 29.56 \% & 36.76 \% & 28.10 \% & 0.1 s / \\
RT3D-GMP \cite{konigshof2020learning} & 28.75 \% & 40.81 \% & 25.13 \% & 0.06 s / GPU \\
CMAN \cite{CMAN2022} & 28.16 \% & 40.27 \% & 24.82 \% & 0.15 s / 1 core \\
Cube R-CNN \cite{brazil2023omni3d} & 28.07 \% & 34.26 \% & 25.14 \% & 0.05 s / GPU \\
CIE \cite{ye2022consistency} & 27.84 \% & 37.65 \% & 25.24 \% & 0.1 s / 1 core \\
BKDStereo3D w/o KD \cite{ERROR: Wrong syntax in BIBTEX file.} & 27.81 \% & 38.59 \% & 24.48 \% & 0.1 s / 1 core \\
BKDStereo3D \cite{ERROR: Wrong syntax in BIBTEX file.} & 27.64 \% & 38.65 \% & 23.62 \% & 0.1 s / 1 core \\
PGD-FCOS3D \cite{PGD} & 27.61 \% & 40.20 \% & 24.29 \% & 0.03 s / 1 core \\
FMF-occlusion-net \cite{liu2022fine} & 26.28 \% & 38.13 \% & 22.91 \% & 0.16 s / 1 core \\
DFR-Net \cite{dfr2021} & 24.88 \% & 35.75 \% & 21.72 \% & 0.18 s / \\
MonoLTKD\_V3 \cite{ERROR: Wrong syntax in BIBTEX file.} & 24.37 \% & 35.52 \% & 21.37 \% & 0.04 s / 1 core \\
DSGN \cite{Chen2020dsgn} & 24.32 \% & 31.21 \% & 23.09 \% & 0.67 s / \\
ACF \cite{Dollar2014PAMI} & 24.31 \% & 32.23 \% & 21.70 \% & 1 s / 1 core \\
PS-fld \cite{Chen2022CVPR} & 23.67 \% & 32.84 \% & 21.40 \% & 0.25 s / 1 core \\
SGM3D \cite{zhou2021sgm3d} & 23.54 \% & 33.73 \% & 20.50 \% & 0.03 s / 1 core \\
multi-task CNN \cite{Oeljeklaus18} & 22.80 \% & 30.30 \% & 20.47 \% & 25.1 ms / GPU \\
ACF-MR \cite{Nattoji2016TITS} & 22.61 \% & 29.23 \% & 20.08 \% & 0.6 s / 1 core \\
OC Stereo \cite{pon2020object} & 22.02 \% & 31.36 \% & 20.20 \% & 0.35 s / 1 core \\
BirdNet \cite{BirdNet2018} & 21.83 \% & 27.12 \% & 20.56 \% & 0.11 s / \\
MonoNeRD \cite{xu2023mononerd} & 20.54 \% & 28.43 \% & 18.36 \% & na s / 1 core \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 19.17 \% & 27.79 \% & 16.48 \% & 15 s / 4 cores \\
ESGN \cite{9869894} & 19.17 \% & 26.02 \% & 16.90 \% & 0.06 s / GPU \\
RefinedMPL \cite{vianney2019refinedmpl} & 17.26 \% & 25.83 \% & 15.41 \% & 0.15 s / GPU \\
CaDDN \cite{CaDDN} & 17.13 \% & 24.45 \% & 15.79 \% & 0.63 s / GPU \\
RT3DStereo \cite{Koenigshof2019Objects} & 15.34 \% & 21.41 \% & 13.23 \% & 0.08 s / GPU
\end{tabular}