\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
TED \cite{TED} & 74.12 \% & 88.82 \% & 66.84 \% & 0.1 s / 1 core \\
CasA++ \cite{casa2022} & 73.79 \% & 87.76 \% & 66.84 \% & 0.1 s / 1 core \\
CasA \cite{casa2022} & 73.47 \% & 87.91 \% & 66.17 \% & 0.1 s / 1 core \\
3D HA Net \cite{10056279} & 73.38 \% & 87.74 \% & 66.37 \% & 0.1 s / 1 core \\
LoGoNet \cite{li2023logonet} & 71.70 \% & 84.47 \% & 64.67 \% & 0.1 s / 1 core \\
USVLab BSAODet \cite{10052705} & 70.48 \% & 83.17 \% & 62.46 \% & 0.04 s / 1 core \\
HMFI \cite{li2022homogeneous} & 70.37 \% & 84.02 \% & 62.57 \% & 0.1 s / 1 core \\
EQ-PVRCNN \cite{Yang2022eqparadigm} & 69.10 \% & 85.41 \% & 62.30 \% & 0.2 s / GPU \\
CAT-Det \cite{zhang2022cat} & 68.81 \% & 83.68 \% & 61.45 \% & 0.3 s / GPU \\
BtcDet \cite{xu2020behind} & 68.68 \% & 82.81 \% & 61.81 \% & 0.09 s / GPU \\
DSA-PV-RCNN \cite{bhattacharyya2021sadet3d} & 68.54 \% & 82.19 \% & 61.33 \% & 0.08 s / 1 core \\
PDV \cite{PDV} & 67.81 \% & 83.04 \% & 60.46 \% & 0.1 s / 1 core \\
RangeIoUDet \cite{liang2021rangeioudet} & 67.77 \% & 83.12 \% & 60.26 \% & 0.02 s / GPU \\
SPG\_mini \cite{xu2021spg} & 66.96 \% & 80.21 \% & 60.50 \% & 0.09 s / GPU \\
M3DeTR \cite{guan2021m3detr} & 66.74 \% & 83.83 \% & 59.03 \% & n/a s / GPU \\
ACDet \cite{acdet} & 66.61 \% & 83.80 \% & 59.99 \% & 0.05 s / 1 core \\
IA-SSD (single) \cite{zhang2022not} & 66.25 \% & 82.36 \% & 59.70 \% & 0.013 s / 1 core \\
HotSpotNet \cite{chen2020object} & 65.95 \% & 82.59 \% & 59.00 \% & 0.04 s / 1 core \\
DFAF3D \cite{tang2023dfaf3d} & 65.86 \% & 82.09 \% & 59.02 \% & 0.05 s / 1 core \\
Fast-CLOCs \cite{Pang2022WACV} & 65.31 \% & 82.83 \% & 57.43 \% & 0.1 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 65.07 \% & 81.98 \% & 56.54 \% & 0.47 s / GPU \\
3DSSD \cite{yang3DSSD20} & 64.10 \% & 82.48 \% & 56.90 \% & 0.04 s / GPU \\
VPFNet \cite{wang2021vpfnet} & 64.10 \% & 77.64 \% & 58.00 \% & 0.2 s / 1 core \\
PointPainting \cite{vora2019pointpainting} & 63.78 \% & 77.63 \% & 55.89 \% & 0.4 s / GPU \\
MMLab PV-RCNN \cite{shi2020pv} & 63.71 \% & 78.60 \% & 57.65 \% & 0.08 s / 1 core \\
MMLab-PartA^2 \cite{shi2020part} & 63.52 \% & 79.17 \% & 56.93 \% & 0.08 s / GPU \\
Point-GNN \cite{shi2020pointgnn} & 63.48 \% & 78.60 \% & 57.08 \% & 0.6 s / GPU \\
MGAF-3DSSD \cite{AnchorfreeMGASSD} & 63.43 \% & 80.64 \% & 55.15 \% & 0.1 s / 1 core \\
FromVoxelToPoint \cite{FromVoxelToPoint} & 63.41 \% & 81.49 \% & 56.40 \% & 0.1 s / 1 core \\
P2V-RCNN \cite{P2VRCNN} & 63.13 \% & 78.62 \% & 56.81 \% & 0.1 s / 2 cores \\
H^23D R-CNN \cite{deng2021multi} & 62.74 \% & 78.67 \% & 55.78 \% & 0.03 s / 1 core \\
SVGA-Net \cite{he2022svga} & 62.28 \% & 78.58 \% & 54.88 \% & 0.03s / 1 core \\
SRDL \cite{ERROR: Wrong syntax in BIBTEX file.} & 62.02 \% & 77.35 \% & 55.52 \% & 0.05 s / 1 core \\
Faraway-Frustum \cite{zhang2021faraway} & 62.00 \% & 77.36 \% & 55.40 \% & 0.1 s / GPU \\
DVFENet \cite{HE2021} & 62.00 \% & 78.73 \% & 55.18 \% & 0.05 s / 1 core \\
IA-SSD (multi) \cite{zhang2022not} & 61.94 \% & 78.35 \% & 55.70 \% & 0.014 s / 1 core \\
S-AT GCN \cite{DBLPjournalscorrabs210308439} & 61.70 \% & 75.24 \% & 55.32 \% & 0.02 s / GPU \\
SIF \cite{sif3d2d} & 61.61 \% & 77.13 \% & 55.11 \% & 0.1 s / 1 core \\
STD \cite{std2019yang} & 61.59 \% & 78.69 \% & 55.30 \% & 0.08 s / GPU \\
AB3DMOT \cite{Weng2019} & 60.30 \% & 75.42 \% & 53.81 \% & 0.0047s / 1 core \\
EPNet++ \cite{9983516} & 59.71 \% & 76.15 \% & 53.67 \% & 0.1 s / GPU \\
XView \cite{xie2021xview} & 59.55 \% & 77.24 \% & 53.47 \% & 0.1 s / 1 core \\
TANet \cite{liu2019tanet} & 59.44 \% & 75.70 \% & 52.53 \% & 0.035s / GPU \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 58.82 \% & 74.96 \% & 52.53 \% & 0.1 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 58.65 \% & 77.10 \% & 51.92 \% & 16 ms / \\
ARPNET \cite{Ye2019} & 58.20 \% & 74.21 \% & 52.13 \% & 0.08 s / GPU \\
epBRM \cite{arxiv} & 56.13 \% & 72.08 \% & 49.91 \% & 0.10 s / 1 core \\
F-PointNet \cite{qi2017frustum} & 56.12 \% & 72.27 \% & 49.01 \% & 0.17 s / GPU \\
BirdNet+ \cite{barrera2021birdnet+} & 53.84 \% & 65.67 \% & 49.06 \% & 0.11 s / \\
PointRGBNet \cite{Xie Desheng340} & 52.15 \% & 67.05 \% & 46.78 \% & 0.08 s / 4 cores \\
DMF \cite{chen2022DMF} & 51.33 \% & 65.51 \% & 45.05 \% & 0.2 s / 1 core \\
PiFeNet \cite{le2022accurate} & 51.10 \% & 67.50 \% & 44.66 \% & 0.03 s / 1 core \\
SCNet \cite{8813061} & 50.79 \% & 67.98 \% & 45.15 \% & 0.04 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 50.55 \% & 63.76 \% & 44.93 \% & 0.1 s / \\
MLOD \cite{deng2019mlod} & 49.43 \% & 68.81 \% & 42.84 \% & 0.12 s / GPU \\
BirdNet+ (legacy) \cite{9294293} & 47.72 \% & 67.38 \% & 42.89 \% & 0.1 s / \\
PFF3D \cite{9340187} & 46.78 \% & 63.27 \% & 41.37 \% & 0.05 s / GPU \\
StereoDistill \cite{liu2020tanet} & 44.02 \% & 63.96 \% & 39.19 \% & 0.4 s / 1 core \\
DSGN++ \cite{chen2022dsgn++} & 43.90 \% & 62.82 \% & 39.21 \% & 0.2 s / \\
AVOD \cite{ku2018joint} & 42.08 \% & 57.19 \% & 38.29 \% & 0.08 s / \\
SparsePool \cite{wang2017fusing} & 37.33 \% & 52.61 \% & 33.39 \% & 0.13 s / 8 cores \\
MMLAB LIGA-Stereo \cite{Guo2021ICCV} & 36.86 \% & 54.44 \% & 32.06 \% & 0.4 s / 1 core \\
SparsePool \cite{wang2017fusing} & 32.61 \% & 40.87 \% & 29.05 \% & 0.13 s / 8 cores \\
CG-Stereo \cite{li2020confidence} & 30.89 \% & 47.40 \% & 27.23 \% & 0.57 s / \\
BirdNet \cite{BirdNet2018} & 30.25 \% & 43.98 \% & 27.21 \% & 0.11 s / \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 24.40 \% & 40.05 \% & 21.12 \% & 0.387 s / GPU \\
Disp R-CNN \cite{sun2020disprcnn} & 24.40 \% & 40.04 \% & 21.12 \% & 0.387 s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 18.53 \% & 24.27 \% & 17.31 \% & 0.06 s / GPU \\
DSGN \cite{Chen2020dsgn} & 18.17 \% & 27.76 \% & 16.21 \% & 0.67 s / \\
OC Stereo \cite{pon2020object} & 16.63 \% & 29.40 \% & 14.72 \% & 0.35 s / 1 core \\
RT3D-GMP \cite{konigshof2020learning} & 12.99 \% & 18.31 \% & 10.63 \% & 0.06 s / GPU \\
ESGN \cite{9869894} & 7.69 \% & 13.84 \% & 6.75 \% & 0.06 s / GPU \\
CMKD \cite{YuHCMKDECCV2022} & 6.67 \% & 12.52 \% & 6.34 \% & 0.1 s / 1 core \\
PS-fld \cite{Chen2022CVPR} & 6.18 \% & 11.22 \% & 5.21 \% & 0.25 s / 1 core \\
Mix-Teaching \cite{Yang2022MixTeachingAS} & 4.91 \% & 8.04 \% & 4.15 \% & 30 s / 1 core \\
DD3D \cite{dd3d} & 4.79 \% & 7.52 \% & 4.22 \% & n/a s / 1 core \\
MonoPSR \cite{ku2019monopsr} & 4.74 \% & 8.37 \% & 3.68 \% & 0.2 s / GPU \\
TopNet-UncEst \cite{wirges2019capturing} & 4.54 \% & 7.13 \% & 3.81 \% & 0.09 s / \\
LPCG-Monoflex \cite{peng2022lidar} & 4.38 \% & 6.98 \% & 3.56 \% & 0.03 s / 1 core \\
Plane-Constraints \cite{yao2023vertex} & 4.22 \% & 7.72 \% & 3.36 \% & 0.05 s / 4 cores \\
MonoDDE \cite{liu2020smoke} & 3.78 \% & 5.94 \% & 3.33 \% & 0.04 s / 1 core \\
DFR-Net \cite{dfr2021} & 3.58 \% & 5.69 \% & 3.10 \% & 0.18 s / \\
HomoLoss(monoflex) \cite{Gu2022CVPR} & 3.50 \% & 5.48 \% & 2.99 \% & 0.04 s / 1 core \\
OPA-3D \cite{su2023opa} & 3.45 \% & 5.16 \% & 2.86 \% & 0.04 s / 1 core \\
CaDDN \cite{CaDDN} & 3.41 \% & 7.00 \% & 3.30 \% & 0.63 s / GPU \\
RT3DStereo \cite{Koenigshof2019Objects} & 3.37 \% & 5.29 \% & 2.57 \% & 0.08 s / GPU \\
MonoDTR \cite{huang2022monodtr} & 3.27 \% & 5.05 \% & 3.19 \% & 0.04 s / 1 core \\
GUPNet \cite{lu2021geometry} & 3.21 \% & 5.58 \% & 2.66 \% & NA s / 1 core \\
DEVIANT \cite{kumar2022deviant} & 3.13 \% & 5.05 \% & 2.59 \% & 0.04 s / \\
CIE \cite{ye2022consistency} & 3.09 \% & 5.62 \% & 2.80 \% & 0.1 s / 1 core \\
SGM3D \cite{zhou2021sgm3d} & 2.92 \% & 5.49 \% & 2.64 \% & 0.03 s / 1 core \\
MDSNet \cite{xie2022mds} & 2.68 \% & 5.37 \% & 2.22 \% & 0.05 s / 1 core \\
Cube R-CNN \cite{brazil2023omni3d} & 2.67 \% & 3.65 \% & 2.28 \% & 0.05 s / GPU \\
monodle \cite{MA2021CVPR} & 2.66 \% & 4.59 \% & 2.45 \% & 0.04 s / GPU \\
DDMP-3D \cite{ddmp3d} & 2.50 \% & 4.18 \% & 2.32 \% & 0.18 s / 1 core \\
Aug3D-RPN \cite{he2021aug3drpn} & 2.43 \% & 4.36 \% & 2.55 \% & 0.08 s / 1 core \\
QD-3DT \cite{Hu2021QD3DT} & 2.39 \% & 4.16 \% & 1.85 \% & 0.03 s / GPU \\
MonoFlex \cite{monoflex} & 2.35 \% & 4.17 \% & 2.04 \% & 0.03 s / GPU \\
MonoPair \cite{chen2020cvpr} & 2.12 \% & 3.79 \% & 1.83 \% & 0.06 s / GPU \\
RefinedMPL \cite{vianney2019refinedmpl} & 1.82 \% & 3.23 \% & 1.77 \% & 0.15 s / GPU \\
MonoRCNN++ \cite{MonoRCNNWACV23} & 1.81 \% & 3.17 \% & 1.75 \% & 0.07 s / GPU \\
TopNet-HighRes \cite{8569433} & 1.67 \% & 2.49 \% & 1.88 \% & 101ms / \\
D4LCN \cite{ding2019learning} & 1.67 \% & 2.45 \% & 1.36 \% & 0.2 s / GPU \\
FMF-occlusion-net \cite{liu2022fine} & 1.60 \% & 1.87 \% & 1.66 \% & 0.16 s / 1 core \\
SS3D \cite{DBLPjournalscorrabs190608070} & 1.45 \% & 2.80 \% & 1.35 \% & 48 ms / \\
PGD-FCOS3D \cite{PGD} & 1.38 \% & 2.81 \% & 1.20 \% & 0.03 s / 1 core \\
CMAN \cite{CMAN2022} & 1.05 \% & 1.59 \% & 1.11 \% & 0.15 s / 1 core \\
MonoEF \cite{Zhou2021CVPR} & 0.92 \% & 1.80 \% & 0.71 \% & 0.03 s / 1 core \\
M3D-RPN \cite{brazil2019m3drpn} & 0.65 \% & 0.94 \% & 0.47 \% & 0.16 s / GPU \\
MonoRUn \cite{monorun} & 0.61 \% & 1.01 \% & 0.48 \% & 0.07 s / GPU \\
Shift R-CNN (mono) \cite{shiftrcnn} & 0.29 \% & 0.48 \% & 0.31 \% & 0.25 s / GPU \\
mBoW \cite{Behley2013IROS} & 0.00 \% & 0.00 \% & 0.00 \% & 10 s / 1 core
\end{tabular}