\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
UPIDet \cite{zhang2023upidet} & 84.44 \% & 90.16 \% & 77.71 \% & 0.11 s / 1 core \\
TED \cite{TED} & 84.36 \% & 92.60 \% & 78.43 \% & 0.1 s / 1 core \\
CasA++ \cite{casa2022} & 84.26 \% & 92.38 \% & 78.42 \% & 0.1 s / 1 core \\
LoGoNet \cite{li2023logonet} & 84.00 \% & 90.14 \% & 77.97 \% & 0.1 s / 1 core \\
CasA \cite{casa2022} & 83.21 \% & 92.86 \% & 77.12 \% & 0.1 s / 1 core \\
MLF-DET \cite{lin2023mlf} & 81.95 \% & 87.34 \% & 74.79 \% & 0.09 s / 1 core \\
VPA \cite{ERROR: Wrong syntax in BIBTEX file.} & 81.85 \% & 89.87 \% & 75.16 \% & 0.01 s / 1 core \\
HMFI \cite{li2022homogeneous} & 81.76 \% & 89.35 \% & 74.93 \% & 0.1 s / 1 core \\
RangeIoUDet \cite{liang2021rangeioudet} & 81.67 \% & 90.43 \% & 74.90 \% & 0.02 s / GPU \\
USVLab BSAODet \cite{10052705} & 81.36 \% & 86.82 \% & 74.40 \% & 0.04 s / 1 core \\
CAT-Det \cite{zhang2022cat} & 80.70 \% & 87.94 \% & 73.86 \% & 0.3 s / GPU \\
SPG\_mini \cite{xu2021spg} & 80.58 \% & 87.77 \% & 74.86 \% & 0.09 s / GPU \\
DSA-PV-RCNN \cite{bhattacharyya2021sadet3d} & 80.57 \% & 88.65 \% & 74.81 \% & 0.08 s / 1 core \\
BtcDet \cite{xu2020behind} & 80.46 \% & 88.41 \% & 74.59 \% & 0.09 s / GPU \\
MMLab PV-RCNN \cite{shi2020pv} & 80.42 \% & 86.62 \% & 73.64 \% & 0.08 s / 1 core \\
EQ-PVRCNN \cite{Yang2022eqparadigm} & 80.37 \% & 89.07 \% & 74.20 \% & 0.2 s / GPU \\
PDV \cite{PDV} & 79.84 \% & 88.76 \% & 73.04 \% & 0.1 s / 1 core \\
KPTr \cite{ERROR: Wrong syntax in BIBTEX file.} & 79.55 \% & 87.31 \% & 72.47 \% & 0.07 s / 1 core \\
M3DeTR \cite{guan2021m3detr} & 79.29 \% & 87.38 \% & 72.46 \% & n/a s / GPU \\
PASS-PV-RCNN-Plus \cite{context} & 79.22 \% & 86.26 \% & 72.68 \% & 1 s / 1 core \\
HotSpotNet \cite{chen2020object} & 78.81 \% & 86.06 \% & 71.74 \% & 0.04 s / 1 core \\
IA-SSD (single) \cite{zhang2022not} & 78.71 \% & 88.99 \% & 72.03 \% & 0.013 s / 1 core \\
PG-RCNN \cite{koo2023pgrcnn} & 78.69 \% & 88.17 \% & 72.16 \% & 0.06 s / GPU \\
MMLab-PartA^2 \cite{shi2020part} & 78.29 \% & 88.90 \% & 71.19 \% & 0.08 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 78.05 \% & 86.75 \% & 68.12 \% & 0.47 s / GPU \\
PointPainting \cite{vora2019pointpainting} & 78.04 \% & 87.70 \% & 69.27 \% & 0.4 s / GPU \\
DFAF3D \cite{tang2023dfaf3d} & 77.74 \% & 87.20 \% & 70.77 \% & 0.05 s / 1 core \\
3ONet \cite{10183841} & 77.36 \% & 89.11 \% & 70.31 \% & 0.1 s / 1 core \\
GraphAlign(ICCV2023) \cite{song2023graphalign} & 77.15 \% & 84.72 \% & 72.34 \% & 0.03 s / GPU \\
P2V-RCNN \cite{P2VRCNN} & 76.93 \% & 88.40 \% & 70.35 \% & 0.1 s / 2 cores \\
EOTL \cite{yang2023efficient} & 76.88 \% & 85.62 \% & 66.04 \% & TBD s / 1 core \\
RRC \cite{Ren17CVPR} & 76.81 \% & 86.81 \% & 66.59 \% & 3.6 s / GPU \\
ACFNet \cite{10363115} & 76.15 \% & 86.92 \% & 71.33 \% & 0.11 s / 1 core \\
ACDet \cite{acdet} & 75.41 \% & 88.54 \% & 69.45 \% & 0.05 s / 1 core \\
MS-CNN \cite{Cai2016ECCV} & 75.30 \% & 84.88 \% & 65.27 \% & 0.4 s / GPU \\
TuSimple \cite{yang2016exploit} & 75.22 \% & 83.68 \% & 65.22 \% & 1.6 s / GPU \\
SVGA-Net \cite{he2022svga} & 75.14 \% & 85.13 \% & 68.14 \% & 0.03s / 1 core \\
Point-GNN \cite{shi2020pointgnn} & 75.08 \% & 85.75 \% & 68.69 \% & 0.6 s / GPU \\
Fast-CLOCs \cite{Pang2022WACV} & 75.07 \% & 89.73 \% & 67.93 \% & 0.1 s / GPU \\
Deep3DBox \cite{MousavianCVPR2017} & 74.78 \% & 84.36 \% & 64.05 \% & 1.5 s / GPU \\
VPFNet \cite{wang2021vpfnet} & 74.52 \% & 82.60 \% & 66.04 \% & 0.2 s / 1 core \\
3DSSD \cite{yang3DSSD20} & 74.12 \% & 87.09 \% & 67.67 \% & 0.04 s / GPU \\
SDP+RPN \cite{Yang2016CVPR} & 73.85 \% & 82.59 \% & 64.87 \% & 0.4 s / GPU \\
SRDL \cite{ERROR: Wrong syntax in BIBTEX file.} & 73.68 \% & 85.44 \% & 66.94 \% & 0.05 s / 1 core \\
DVFENet \cite{HE2021} & 73.66 \% & 85.45 \% & 67.10 \% & 0.05 s / 1 core \\
Faraway-Frustum \cite{zhang2021faraway} & 73.63 \% & 85.43 \% & 66.64 \% & 0.1 s / GPU \\
sensekitti \cite{binyang16craft} & 73.48 \% & 82.90 \% & 64.03 \% & 4.5 s / GPU \\
L-AUG \cite{cortinhal2023semanticsaware} & 73.43 \% & 83.88 \% & 68.12 \% & 0.1 s / 1 core \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 73.42 \% & 86.21 \% & 66.45 \% & 0.1 s / GPU \\
SIF \cite{sif3d2d} & 73.19 \% & 85.18 \% & 65.41 \% & 0.1 s / 1 core \\
F-PointNet \cite{qi2017frustum} & 73.16 \% & 86.86 \% & 65.21 \% & 0.17 s / GPU \\
FromVoxelToPoint \cite{FromVoxelToPoint} & 73.16 \% & 87.07 \% & 65.98 \% & 0.1 s / 1 core \\
XView \cite{xie2021xview} & 73.16 \% & 88.02 \% & 65.37 \% & 0.1 s / 1 core \\
S-AT GCN \cite{DBLPjournalscorrabs210308439} & 72.81 \% & 82.79 \% & 66.72 \% & 0.02 s / GPU \\
H^23D R-CNN \cite{deng2021multi} & 72.73 \% & 85.50 \% & 65.81 \% & 0.03 s / 1 core \\
MonoPSR \cite{ku2019monopsr} & 72.08 \% & 82.06 \% & 62.43 \% & 0.2 s / GPU \\
ARPNET \cite{Ye2019} & 71.95 \% & 84.96 \% & 65.21 \% & 0.08 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 71.72 \% & 79.36 \% & 62.74 \% & 2 s / GPU \\
STD \cite{std2019yang} & 71.63 \% & 83.99 \% & 64.92 \% & 0.08 s / GPU \\
LVFSD \cite{ERROR: Wrong syntax in BIBTEX file.} & 71.09 \% & 85.81 \% & 64.75 \% & 0.06 s / \\
IA-SSD (multi) \cite{zhang2022not} & 70.46 \% & 84.98 \% & 65.55 \% & 0.014 s / 1 core \\
MGAF-3DSSD \cite{AnchorfreeMGASSD} & 70.41 \% & 86.42 \% & 63.26 \% & 0.1 s / 1 core \\
AB3DMOT \cite{Weng2019} & 70.18 \% & 82.86 \% & 63.55 \% & 0.0047s / 1 core \\
VSAC \cite{ERROR: Wrong syntax in BIBTEX file.} & 69.36 \% & 88.58 \% & 62.27 \% & 0.07 s / 1 core \\
PointPillars \cite{lang2018pointpillars} & 68.98 \% & 83.97 \% & 62.17 \% & 16 ms / \\
Vote3Deep \cite{Engelcke2016ARXIV} & 68.82 \% & 78.41 \% & 62.50 \% & 1.5 s / 4 cores \\
3DOP \cite{Chen2015NIPS} & 68.71 \% & 80.52 \% & 61.07 \% & 3s / GPU \\
Pose-RCNN \cite{braun2016pose} & 68.40 \% & 81.53 \% & 59.43 \% & 2 s / >8 cores \\
EPNet++ \cite{9983516} & 68.30 \% & 80.27 \% & 63.00 \% & 0.1 s / GPU \\
TANet \cite{liu2019tanet} & 68.20 \% & 82.24 \% & 62.13 \% & 0.035s / GPU \\
IVA \cite{Zhu2016ACCV} & 67.57 \% & 78.48 \% & 58.83 \% & 0.4 s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 67.22 \% & 79.35 \% & 58.60 \% & 3.4 s / GPU \\
Cube R-CNN \cite{brazil2023omni3d} & 66.98 \% & 81.99 \% & 58.56 \% & 0.05 s / GPU \\
FII-CenterNet \cite{9316984} & 66.54 \% & 79.04 \% & 57.76 \% & 0.09 s / GPU \\
epBRM \cite{arxiv} & 66.51 \% & 79.65 \% & 60.31 \% & 0.10 s / 1 core \\
PFF3D \cite{9340187} & 66.25 \% & 79.44 \% & 60.11 \% & 0.05 s / GPU \\
DD3D \cite{dd3d} & 65.98 \% & 81.13 \% & 58.86 \% & n/a s / 1 core \\
PointRGBNet \cite{Xie Desheng340} & 65.98 \% & 79.87 \% & 59.75 \% & 0.08 s / 4 cores \\
BirdNet+ \cite{barrera2021birdnet+} & 65.40 \% & 72.96 \% & 60.23 \% & 0.11 s / \\
Mono3D \cite{Chen2016CVPR} & 65.15 \% & 77.19 \% & 57.88 \% & 4.2 s / GPU \\
DMF \cite{chen2022DMF} & 63.39 \% & 74.69 \% & 56.96 \% & 0.2 s / 1 core \\
PiFeNet \cite{le2022accurate} & 63.34 \% & 78.05 \% & 56.46 \% & 0.03 s / 1 core \\
Faster R-CNN \cite{Ren2015NIPS} & 62.86 \% & 72.40 \% & 54.97 \% & 2 s / GPU \\
SCNet \cite{8813061} & 62.50 \% & 78.48 \% & 56.34 \% & 0.04 s / GPU \\
DSGN++ \cite{chen2022dsgn++} & 62.10 \% & 77.71 \% & 55.78 \% & 0.2 s / \\
StereoDistill \cite{liu2020tanet} & 61.46 \% & 80.92 \% & 54.64 \% & 0.4 s / 1 core \\
AVOD-FPN \cite{ku2018joint} & 60.79 \% & 70.38 \% & 55.37 \% & 0.1 s / \\
SDP+CRC (ft) \cite{Yang2016CVPR} & 60.72 \% & 75.63 \% & 53.00 \% & 0.6 s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 59.78 \% & 66.94 \% & 55.63 \% & 0.06 s / GPU \\
Mix-Teaching \cite{Yang2022MixTeachingAS} & 58.65 \% & 75.15 \% & 50.54 \% & 30 s / 1 core \\
Regionlets \cite{Wang2015PAMI} & 58.52 \% & 71.12 \% & 50.83 \% & 1 s / >8 cores \\
MonoLiG \cite{hekimoglu2023monocular} & 58.35 \% & 80.41 \% & 51.21 \% & 0.03 s / 1 core \\
FRCNN+Or \cite{GuindelITSM} & 57.01 \% & 70.99 \% & 50.14 \% & 0.09 s / \\
QD-3DT \cite{Hu2021QD3DT} & 56.51 \% & 75.55 \% & 49.70 \% & 0.03 s / GPU \\
MonoPair \cite{chen2020cvpr} & 56.37 \% & 74.77 \% & 48.37 \% & 0.06 s / GPU \\
MLOD \cite{deng2019mlod} & 56.04 \% & 75.35 \% & 49.11 \% & 0.12 s / GPU \\
MonoFlex \cite{monoflex} & 54.76 \% & 72.41 \% & 46.21 \% & 0.03 s / GPU \\
MonoLSS \cite{monolss} & 54.63 \% & 74.54 \% & 47.98 \% & 0.04 s / 1 core \\
BirdNet+ (legacy) \cite{9294293} & 54.61 \% & 74.97 \% & 50.29 \% & 0.1 s / \\
MMLAB LIGA-Stereo \cite{Guo2021ICCV} & 54.57 \% & 74.40 \% & 48.11 \% & 0.4 s / 1 core \\
HomoLoss(monoflex) \cite{Gu2022CVPR} & 54.12 \% & 70.14 \% & 46.16 \% & 0.04 s / 1 core \\
MonoUNI \cite{MonoUNI} & 53.71 \% & 71.68 \% & 45.26 \% & 0.04 s / 1 core \\
monodle \cite{MA2021CVPR} & 53.29 \% & 70.78 \% & 45.01 \% & 0.04 s / GPU \\
LPCG-Monoflex \cite{peng2022lidar} & 53.04 \% & 72.36 \% & 46.11 \% & 0.03 s / 1 core \\
AVOD \cite{ku2018joint} & 52.60 \% & 66.45 \% & 46.39 \% & 0.08 s / \\
CMKD \cite{YuHCMKDECCV2022} & 51.76 \% & 73.18 \% & 45.37 \% & 0.1 s / 1 core \\
MonOAPC \cite{yao2023occlusion} & 51.68 \% & 68.18 \% & 44.08 \% & 0035 s / 1 core \\
MonoDDE \cite{liu2020smoke} & 51.10 \% & 70.85 \% & 44.02 \% & 0.04 s / 1 core \\
MonoDTR \cite{huang2022monodtr} & 49.48 \% & 64.93 \% & 42.76 \% & 0.04 s / 1 core \\
MonoRUn \cite{monorun} & 49.13 \% & 67.47 \% & 43.41 \% & 0.07 s / GPU \\
MonoRCNN++ \cite{MonoRCNNWACV23} & 48.84 \% & 67.78 \% & 42.44 \% & 0.07 s / GPU \\
CG-Stereo \cite{li2020confidence} & 48.46 \% & 69.98 \% & 42.41 \% & 0.57 s / \\
BirdNet \cite{BirdNet2018} & 47.64 \% & 64.91 \% & 44.59 \% & 0.11 s / \\
DEVIANT \cite{kumar2022deviant} & 46.42 \% & 67.71 \% & 39.44 \% & 0.04 s / \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 46.37 \% & 63.22 \% & 40.15 \% & 0.387 s / GPU \\
Disp R-CNN \cite{sun2020disprcnn} & 46.37 \% & 63.24 \% & 40.15 \% & 0.387 s / GPU \\
SparsePool \cite{wang2017fusing} & 44.57 \% & 60.53 \% & 40.37 \% & 0.13 s / 8 cores \\
Shift R-CNN (mono) \cite{shiftrcnn} & 42.96 \% & 63.24 \% & 38.22 \% & 0.25 s / GPU \\
D4LCN \cite{ding2019learning} & 42.86 \% & 65.29 \% & 36.29 \% & 0.2 s / GPU \\
GUPNet \cite{lu2021geometry} & 42.78 \% & 67.11 \% & 37.94 \% & NA s / 1 core \\
M3D-RPN \cite{brazil2019m3drpn} & 41.54 \% & 61.54 \% & 35.23 \% & 0.16 s / GPU \\
MonoEF \cite{Zhou2021CVPR} & 41.19 \% & 51.06 \% & 35.70 \% & 0.03 s / 1 core \\
PS-fld \cite{Chen2022CVPR} & 41.13 \% & 58.13 \% & 35.90 \% & 0.25 s / 1 core \\
Plane-Constraints \cite{yao2023vertex} & 41.01 \% & 58.71 \% & 35.35 \% & 0.05 s / 4 cores \\
MV-RGBD-RF \cite{Gonzalez2016TCYB} & 40.94 \% & 51.10 \% & 34.83 \% & 4 s / 4 cores \\
MonoLTKD\_V3 \cite{ERROR: Wrong syntax in BIBTEX file.} & 38.84 \% & 58.63 \% & 33.99 \% & 0.04 s / 1 core \\
DDMP-3D \cite{ddmp3d} & 38.62 \% & 58.70 \% & 34.10 \% & 0.18 s / 1 core \\
CMAN \cite{CMAN2022} & 38.36 \% & 58.12 \% & 31.79 \% & 0.15 s / 1 core \\
OPA-3D \cite{su2023opa} & 38.35 \% & 55.98 \% & 33.83 \% & 0.04 s / 1 core \\
Aug3D-RPN \cite{he2021aug3drpn} & 36.69 \% & 51.49 \% & 30.04 \% & 0.08 s / 1 core \\
SparsePool \cite{wang2017fusing} & 36.26 \% & 44.21 \% & 32.57 \% & 0.13 s / 8 cores \\
SS3D \cite{DBLPjournalscorrabs190608070} & 35.48 \% & 52.97 \% & 31.07 \% & 48 ms / \\
DSGN \cite{Chen2020dsgn} & 35.15 \% & 49.10 \% & 31.41 \% & 0.67 s / \\
pAUCEnsT \cite{Paul2014ARXIV} & 34.90 \% & 50.51 \% & 30.35 \% & 60 s / 1 core \\
TopNet-Retina \cite{8569433} & 31.98 \% & 47.51 \% & 29.84 \% & 52ms / \\
DFR-Net \cite{dfr2021} & 31.93 \% & 48.34 \% & 27.95 \% & 0.18 s / \\
CIE \cite{ye2022consistency} & 30.10 \% & 38.03 \% & 26.99 \% & 0.1 s / 1 core \\
MonoNeRD \cite{xu2023mononerd} & 29.89 \% & 45.35 \% & 26.49 \% & na s / 1 core \\
OC Stereo \cite{pon2020object} & 28.76 \% & 43.18 \% & 24.80 \% & 0.35 s / 1 core \\
Vote3D \cite{Wang2015RSS} & 27.99 \% & 39.81 \% & 25.19 \% & 0.5 s / 4 cores \\
SGM3D \cite{zhou2021sgm3d} & 27.89 \% & 42.21 \% & 24.73 \% & 0.03 s / 1 core \\
LSVM-MDPM-us \cite{Felzenszwalb2010PAMI} & 27.81 \% & 37.66 \% & 24.83 \% & 10 s / 4 cores \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 27.73 \% & 41.58 \% & 24.61 \% & 8 s / 1 core \\
RefinedMPL \cite{vianney2019refinedmpl} & 27.17 \% & 44.47 \% & 22.84 \% & 0.15 s / GPU \\
CaDDN \cite{CaDDN} & 27.13 \% & 40.03 \% & 23.23 \% & 0.63 s / GPU \\
PGD-FCOS3D \cite{PGD} & 26.48 \% & 44.28 \% & 23.03 \% & 0.03 s / 1 core \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 26.05 \% & 35.70 \% & 23.56 \% & 10 s / 4 cores \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 25.57 \% & 41.47 \% & 21.93 \% & 15 s / 4 cores \\
FMF-occlusion-net \cite{liu2022fine} & 23.59 \% & 37.41 \% & 21.20 \% & 0.16 s / 1 core \\
RT3D-GMP \cite{konigshof2020learning} & 22.90 \% & 33.64 \% & 19.87 \% & 0.06 s / GPU \\
mBoW \cite{Behley2013IROS} & 17.63 \% & 26.66 \% & 16.02 \% & 10 s / 1 core \\
MDSNet \cite{xie2022mds} & 16.64 \% & 28.23 \% & 14.14 \% & 0.05 s / 1 core \\
TopNet-HighRes \cite{8569433} & 13.98 \% & 22.86 \% & 14.52 \% & 101ms / \\
ESGN \cite{9869894} & 13.45 \% & 21.13 \% & 11.72 \% & 0.06 s / GPU \\
RT3DStereo \cite{Koenigshof2019Objects} & 12.96 \% & 19.58 \% & 11.47 \% & 0.08 s / GPU \\
TopNet-UncEst \cite{wirges2019capturing} & 12.00 \% & 18.14 \% & 11.85 \% & 0.09 s / \\
YOLOv2 \cite{redmon2016you} & 0.06 \% & 0.15 \% & 0.07 \% & 0.02 s / GPU \\
TopNet-DecayRate \cite{8569433} & 0.04 \% & 0.00 \% & 0.04 \% & 92 ms /
\end{tabular}