\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
CasA++ \cite{casa2022} & 76.99 \% & 88.93 \% & 70.10 \% & 0.1 s / 1 core \\
TED \cite{TED} & 76.95 \% & 89.54 \% & 70.31 \% & 0.1 s / 1 core \\
CasA \cite{casa2022} & 75.74 \% & 88.99 \% & 68.47 \% & 0.1 s / 1 core \\
3D HA Net \cite{10056279} & 75.68 \% & 88.22 \% & 68.89 \% & 0.1 s / 1 core \\
LoGoNet \cite{li2023logonet} & 74.92 \% & 85.85 \% & 67.62 \% & 0.1 s / 1 core \\
USVLab BSAODet \cite{10052705} & 74.38 \% & 85.01 \% & 67.38 \% & 0.04 s / 1 core \\
HMFI \cite{li2022homogeneous} & 74.06 \% & 85.69 \% & 67.11 \% & 0.1 s / 1 core \\
EQ-PVRCNN \cite{Yang2022eqparadigm} & 73.30 \% & 86.25 \% & 65.49 \% & 0.2 s / GPU \\
DSA-PV-RCNN \cite{bhattacharyya2021sadet3d} & 72.61 \% & 83.93 \% & 65.82 \% & 0.08 s / 1 core \\
CAT-Det \cite{zhang2022cat} & 72.51 \% & 85.35 \% & 65.55 \% & 0.3 s / GPU \\
BtcDet \cite{xu2020behind} & 71.76 \% & 84.48 \% & 64.70 \% & 0.09 s / GPU \\
PointPainting \cite{vora2019pointpainting} & 71.54 \% & 83.91 \% & 62.97 \% & 0.4 s / GPU \\
RangeIoUDet \cite{liang2021rangeioudet} & 71.49 \% & 85.99 \% & 63.62 \% & 0.02 s / GPU \\
ACDet \cite{acdet} & 71.48 \% & 87.76 \% & 64.69 \% & 0.05 s / 1 core \\
IA-SSD (single) \cite{zhang2022not} & 71.44 \% & 85.91 \% & 63.41 \% & 0.013 s / 1 core \\
PDV \cite{PDV} & 71.31 \% & 85.54 \% & 64.40 \% & 0.1 s / 1 core \\
DFAF3D \cite{tang2023dfaf3d} & 71.27 \% & 85.75 \% & 64.25 \% & 0.05 s / 1 core \\
HVNet \cite{ye2020hvnet} & 71.17 \% & 83.97 \% & 63.65 \% & 0.03 s / GPU \\
M3DeTR \cite{guan2021m3detr} & 70.89 \% & 85.03 \% & 63.14 \% & n/a s / GPU \\
SPG\_mini \cite{xu2021spg} & 70.09 \% & 82.66 \% & 63.61 \% & 0.09 s / GPU \\
MMLab PV-RCNN \cite{shi2020pv} & 68.89 \% & 82.49 \% & 62.41 \% & 0.08 s / 1 core \\
F-ConvNet \cite{wang2019frustum} & 68.88 \% & 84.16 \% & 60.05 \% & 0.47 s / GPU \\
MMLab-PartA^2 \cite{shi2020part} & 68.73 \% & 83.43 \% & 61.85 \% & 0.08 s / GPU \\
HotSpotNet \cite{chen2020object} & 68.51 \% & 83.29 \% & 61.84 \% & 0.04 s / 1 core \\
P2V-RCNN \cite{P2VRCNN} & 68.06 \% & 81.09 \% & 60.73 \% & 0.1 s / 2 cores \\
H^23D R-CNN \cite{deng2021multi} & 67.90 \% & 82.76 \% & 60.49 \% & 0.03 s / 1 core \\
VPFNet \cite{wang2021vpfnet} & 67.66 \% & 80.83 \% & 61.36 \% & 0.2 s / 1 core \\
3DSSD \cite{yang3DSSD20} & 67.62 \% & 85.04 \% & 61.14 \% & 0.04 s / GPU \\
Fast-CLOCs \cite{Pang2022WACV} & 67.55 \% & 83.34 \% & 59.61 \% & 0.1 s / GPU \\
DVFENet \cite{HE2021} & 67.40 \% & 82.29 \% & 60.71 \% & 0.05 s / 1 core \\
FromVoxelToPoint \cite{FromVoxelToPoint} & 67.36 \% & 82.68 \% & 59.15 \% & 0.1 s / 1 core \\
Point-GNN \cite{shi2020pointgnn} & 67.28 \% & 81.17 \% & 59.67 \% & 0.6 s / GPU \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 67.24 \% & 82.56 \% & 60.28 \% & 0.1 s / GPU \\
STD \cite{std2019yang} & 67.23 \% & 81.36 \% & 59.35 \% & 0.08 s / GPU \\
SVGA-Net \cite{he2022svga} & 66.82 \% & 81.25 \% & 59.37 \% & 0.03s / 1 core \\
S-AT GCN \cite{DBLPjournalscorrabs210308439} & 66.71 \% & 78.53 \% & 60.19 \% & 0.02 s / GPU \\
ARPNET \cite{Ye2019} & 66.39 \% & 82.32 \% & 58.80 \% & 0.08 s / GPU \\
IA-SSD (multi) \cite{zhang2022not} & 66.29 \% & 81.30 \% & 59.58 \% & 0.014 s / 1 core \\
MGAF-3DSSD \cite{AnchorfreeMGASSD} & 66.00 \% & 83.03 \% & 57.57 \% & 0.1 s / 1 core \\
AB3DMOT \cite{Weng2019} & 65.85 \% & 80.00 \% & 58.69 \% & 0.0047s / 1 core \\
Faraway-Frustum \cite{zhang2021faraway} & 64.54 \% & 79.65 \% & 57.84 \% & 0.1 s / GPU \\
SRDL \cite{ERROR: Wrong syntax in BIBTEX file.} & 64.52 \% & 79.64 \% & 57.90 \% & 0.05 s / 1 core \\
SIF \cite{sif3d2d} & 64.13 \% & 79.32 \% & 57.38 \% & 0.1 s / 1 core \\
TANet \cite{liu2019tanet} & 63.77 \% & 79.16 \% & 56.21 \% & 0.035s / GPU \\
XView \cite{xie2021xview} & 63.06 \% & 81.32 \% & 56.65 \% & 0.1 s / 1 core \\
EPNet++ \cite{9983516} & 62.94 \% & 78.57 \% & 56.62 \% & 0.1 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 62.73 \% & 79.90 \% & 55.58 \% & 16 ms / \\
F-PointNet \cite{qi2017frustum} & 61.37 \% & 77.26 \% & 53.78 \% & 0.17 s / GPU \\
epBRM \cite{arxiv} & 59.79 \% & 75.13 \% & 53.36 \% & 0.10 s / 1 core \\
BirdNet+ \cite{barrera2021birdnet+} & 59.58 \% & 70.84 \% & 54.20 \% & 0.11 s / \\
DMF \cite{chen2022DMF} & 57.99 \% & 71.92 \% & 51.55 \% & 0.2 s / 1 core \\
PointRGBNet \cite{Xie Desheng340} & 57.59 \% & 73.09 \% & 51.78 \% & 0.08 s / 4 cores \\
AVOD-FPN \cite{ku2018joint} & 57.12 \% & 69.39 \% & 51.09 \% & 0.1 s / \\
PiFeNet \cite{le2022accurate} & 56.94 \% & 72.80 \% & 50.04 \% & 0.03 s / 1 core \\
SCNet \cite{8813061} & 56.39 \% & 73.73 \% & 49.99 \% & 0.04 s / GPU \\
PFF3D \cite{9340187} & 55.71 \% & 72.67 \% & 49.58 \% & 0.05 s / GPU \\
MLOD \cite{deng2019mlod} & 55.06 \% & 73.03 \% & 48.21 \% & 0.12 s / GPU \\
BirdNet+ (legacy) \cite{9294293} & 52.15 \% & 72.45 \% & 46.57 \% & 0.1 s / \\
DSGN++ \cite{chen2022dsgn++} & 49.37 \% & 68.29 \% & 43.79 \% & 0.2 s / \\
StereoDistill \cite{liu2020tanet} & 48.37 \% & 69.46 \% & 42.69 \% & 0.4 s / 1 core \\
AVOD \cite{ku2018joint} & 48.15 \% & 64.11 \% & 42.37 \% & 0.08 s / \\
BirdNet \cite{BirdNet2018} & 41.56 \% & 58.64 \% & 36.94 \% & 0.11 s / \\
SparsePool \cite{wang2017fusing} & 40.74 \% & 56.52 \% & 36.68 \% & 0.13 s / 8 cores \\
MMLAB LIGA-Stereo \cite{Guo2021ICCV} & 40.60 \% & 58.95 \% & 35.27 \% & 0.4 s / 1 core \\
TopNet-Retina \cite{8569433} & 36.83 \% & 47.48 \% & 33.58 \% & 52ms / \\
CG-Stereo \cite{li2020confidence} & 36.25 \% & 55.33 \% & 32.17 \% & 0.57 s / \\
SparsePool \cite{wang2017fusing} & 35.24 \% & 43.55 \% & 30.15 \% & 0.13 s / 8 cores \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 27.04 \% & 44.19 \% & 23.58 \% & 0.387 s / GPU \\
Disp R-CNN \cite{sun2020disprcnn} & 27.04 \% & 44.19 \% & 23.58 \% & 0.387 s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 25.43 \% & 32.00 \% & 22.88 \% & 0.06 s / GPU \\
DSGN \cite{Chen2020dsgn} & 21.04 \% & 31.23 \% & 18.93 \% & 0.67 s / \\
OC Stereo \cite{pon2020object} & 19.23 \% & 32.47 \% & 17.11 \% & 0.35 s / 1 core \\
TopNet-DecayRate \cite{8569433} & 16.00 \% & 23.02 \% & 13.24 \% & 92 ms / \\
RT3D-GMP \cite{konigshof2020learning} & 13.92 \% & 20.59 \% & 12.74 \% & 0.06 s / GPU \\
TopNet-UncEst \cite{wirges2019capturing} & 9.18 \% & 12.31 \% & 8.14 \% & 0.09 s / \\
ESGN \cite{9869894} & 9.02 \% & 15.78 \% & 7.96 \% & 0.06 s / GPU \\
CMKD \cite{YuHCMKDECCV2022} & 8.15 \% & 14.66 \% & 7.23 \% & 0.1 s / 1 core \\
PS-fld \cite{Chen2022CVPR} & 7.29 \% & 12.80 \% & 6.05 \% & 0.25 s / 1 core \\
TopNet-HighRes \cite{8569433} & 6.48 \% & 9.99 \% & 6.76 \% & 101ms / \\
MonoPSR \cite{ku2019monopsr} & 5.78 \% & 9.87 \% & 4.57 \% & 0.2 s / GPU \\
DD3D \cite{dd3d} & 5.69 \% & 9.20 \% & 5.20 \% & n/a s / 1 core \\
CaDDN \cite{CaDDN} & 5.38 \% & 9.67 \% & 4.75 \% & 0.63 s / GPU \\
Mix-Teaching \cite{Yang2022MixTeachingAS} & 5.36 \% & 8.56 \% & 4.62 \% & 30 s / 1 core \\
LPCG-Monoflex \cite{peng2022lidar} & 4.90 \% & 8.14 \% & 3.86 \% & 0.03 s / 1 core \\
Plane-Constraints \cite{yao2023vertex} & 4.79 \% & 8.67 \% & 3.90 \% & 0.05 s / 4 cores \\
MonoDDE \cite{liu2020smoke} & 4.36 \% & 6.68 \% & 3.76 \% & 0.04 s / 1 core \\
MonoDTR \cite{huang2022monodtr} & 4.11 \% & 5.84 \% & 3.48 \% & 0.04 s / 1 core \\
RT3DStereo \cite{Koenigshof2019Objects} & 4.10 \% & 7.03 \% & 3.88 \% & 0.08 s / GPU \\
HomoLoss(monoflex) \cite{Gu2022CVPR} & 4.09 \% & 6.81 \% & 3.78 \% & 0.04 s / 1 core \\
DFR-Net \cite{dfr2021} & 4.00 \% & 5.99 \% & 3.95 \% & 0.18 s / \\
DEVIANT \cite{kumar2022deviant} & 3.97 \% & 6.42 \% & 3.51 \% & 0.04 s / \\
GUPNet \cite{lu2021geometry} & 3.85 \% & 6.94 \% & 3.64 \% & NA s / 1 core \\
OPA-3D \cite{su2023opa} & 3.75 \% & 6.01 \% & 3.56 \% & 0.04 s / 1 core \\
CIE \cite{ye2022consistency} & 3.74 \% & 6.13 \% & 3.18 \% & 0.1 s / 1 core \\
SGM3D \cite{zhou2021sgm3d} & 3.63 \% & 7.05 \% & 3.33 \% & 0.03 s / 1 core \\
Cube R-CNN \cite{brazil2023omni3d} & 3.35 \% & 5.01 \% & 3.23 \% & 0.05 s / GPU \\
Aug3D-RPN \cite{he2021aug3drpn} & 3.33 \% & 5.44 \% & 2.82 \% & 0.08 s / 1 core \\
monodle \cite{MA2021CVPR} & 3.28 \% & 5.34 \% & 2.83 \% & 0.04 s / GPU \\
MDSNet \cite{xie2022mds} & 3.22 \% & 5.99 \% & 2.62 \% & 0.05 s / 1 core \\
DDMP-3D \cite{ddmp3d} & 3.14 \% & 4.92 \% & 2.44 \% & 0.18 s / 1 core \\
QD-3DT \cite{Hu2021QD3DT} & 3.02 \% & 5.71 \% & 2.73 \% & 0.03 s / GPU \\
MonoPair \cite{chen2020cvpr} & 2.87 \% & 4.76 \% & 2.42 \% & 0.06 s / GPU \\
MonoFlex \cite{monoflex} & 2.67 \% & 4.41 \% & 2.50 \% & 0.03 s / GPU \\
RefinedMPL \cite{vianney2019refinedmpl} & 2.42 \% & 4.23 \% & 2.14 \% & 0.15 s / GPU \\
MonoRCNN++ \cite{MonoRCNNWACV23} & 2.31 \% & 3.50 \% & 2.01 \% & 0.07 s / GPU \\
SS3D \cite{DBLPjournalscorrabs190608070} & 1.89 \% & 3.45 \% & 1.44 \% & 48 ms / \\
D4LCN \cite{ding2019learning} & 1.82 \% & 2.72 \% & 1.79 \% & 0.2 s / GPU \\
PGD-FCOS3D \cite{PGD} & 1.79 \% & 3.54 \% & 1.56 \% & 0.03 s / 1 core \\
FMF-occlusion-net \cite{liu2022fine} & 1.65 \% & 1.91 \% & 1.75 \% & 0.16 s / 1 core \\
CMAN \cite{CMAN2022} & 1.48 \% & 1.76 \% & 1.17 \% & 0.15 s / 1 core \\
MonoEF \cite{Zhou2021CVPR} & 1.18 \% & 2.36 \% & 1.15 \% & 0.03 s / 1 core \\
M3D-RPN \cite{brazil2019m3drpn} & 0.81 \% & 1.25 \% & 0.78 \% & 0.16 s / GPU \\
MonoRUn \cite{monorun} & 0.73 \% & 1.14 \% & 0.66 \% & 0.07 s / GPU \\
Shift R-CNN (mono) \cite{shiftrcnn} & 0.38 \% & 0.76 \% & 0.41 \% & 0.25 s / GPU \\
mBoW \cite{Behley2013IROS} & 0.00 \% & 0.00 \% & 0.00 \% & 10 s / 1 core
\end{tabular}