\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
F-PointNet \cite{qi2017frustum} & 80.13 \% & 89.83 \% & 75.05 \% & 0.17 s / GPU \\
HHA-TFFEM \cite{tan20223d} & 78.53 \% & 87.01 \% & 74.70 \% & 0.14 s / GPU \\
TuSimple \cite{yang2016exploit} & 78.40 \% & 88.87 \% & 73.66 \% & 1.6 s / GPU \\
RRC \cite{Ren17CVPR} & 76.61 \% & 85.98 \% & 71.47 \% & 3.6 s / GPU \\
WSSN \cite{guo2021weak} & 76.42 \% & 84.91 \% & 71.86 \% & 0.37 s / GPU \\
ECP Faster R-CNN \cite{DBLPjournalscorrabs180507193} & 76.25 \% & 85.96 \% & 70.55 \% & 0.25 s / GPU \\
Aston-EAS \cite{wei2019enhanced} & 76.07 \% & 86.71 \% & 70.02 \% & 0.24 s / GPU \\
MHN \cite{jiale2018arXiv} & 75.99 \% & 87.21 \% & 69.50 \% & 0.39 s / GPU \\
FFNet \cite{zhao2019monocular} & 75.81 \% & 87.17 \% & 69.86 \% & 1.07 s / GPU \\
SJTU-HW \cite{zsq2018icip} & 75.81 \% & 87.17 \% & 69.86 \% & 0.85s / GPU \\
MS-CNN \cite{Cai2016ECCV} & 74.89 \% & 85.71 \% & 68.99 \% & 0.4 s / GPU \\
DD3D \cite{dd3d} & 73.09 \% & 85.71 \% & 68.54 \% & n/a s / 1 core \\
F-ConvNet \cite{wang2019frustum} & 72.91 \% & 83.63 \% & 67.18 \% & 0.47 s / GPU \\
GN \cite{JUNG201743} & 72.29 \% & 82.93 \% & 65.56 \% & 1 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 72.27 \% & 84.88 \% & 66.82 \% & 2 s / GPU \\
VMVS \cite{ku2018joint} & 71.82 \% & 82.80 \% & 66.85 \% & 0.25 s / GPU \\
EOTL \cite{yang2023efficient} & 71.45 \% & 84.74 \% & 64.58 \% & TBD s / 1 core \\
IVA \cite{Zhu2016ACCV} & 71.37 \% & 84.61 \% & 64.90 \% & 0.4 s / GPU \\
MM-MRFC \cite{Costea2017CVPR} & 70.76 \% & 83.79 \% & 64.81 \% & 0.05 s / GPU \\
SDP+RPN \cite{Yang2016CVPR} & 70.42 \% & 82.07 \% & 65.09 \% & 0.4 s / GPU \\
3DOP \cite{Chen2015NIPS} & 69.57 \% & 83.17 \% & 63.48 \% & 3s / GPU \\
MonoPSR \cite{ku2019monopsr} & 68.56 \% & 85.60 \% & 63.34 \% & 0.2 s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 68.46 \% & 83.00 \% & 63.35 \% & 3.4 s / GPU \\
sensekitti \cite{binyang16craft} & 68.41 \% & 82.72 \% & 62.72 \% & 4.5 s / GPU \\
MonoLSS \cite{monolss} & 67.78 \% & 82.88 \% & 60.87 \% & 0.04 s / 1 core \\
Frustum-PointPillars \cite{paigwarhal03354114} & 67.51 \% & 76.80 \% & 63.81 \% & 0.06 s / 4 cores \\
FII-CenterNet \cite{9316984} & 67.31 \% & 81.32 \% & 61.29 \% & 0.09 s / GPU \\
Mono3D \cite{Chen2016CVPR} & 67.29 \% & 80.30 \% & 62.23 \% & 4.2 s / GPU \\
Faster R-CNN \cite{Ren2015NIPS} & 66.24 \% & 79.97 \% & 61.09 \% & 2 s / GPU \\
VPFNet \cite{wang2021vpfnet} & 65.68 \% & 75.03 \% & 61.95 \% & 0.2 s / 1 core \\
EQ-PVRCNN \cite{Yang2022eqparadigm} & 65.01 \% & 77.19 \% & 61.95 \% & 0.2 s / GPU \\
CasA++ \cite{casa2022} & 64.94 \% & 74.41 \% & 62.35 \% & 0.1 s / 1 core \\
TED \cite{TED} & 64.74 \% & 74.26 \% & 62.08 \% & 0.1 s / 1 core \\
LoGoNet \cite{li2023logonet} & 64.55 \% & 72.47 \% & 62.24 \% & 0.1 s / 1 core \\
SDP+CRC (ft) \cite{Yang2016CVPR} & 64.36 \% & 79.22 \% & 59.16 \% & 0.6 s / GPU \\
Pose-RCNN \cite{braun2016pose} & 63.54 \% & 80.07 \% & 57.02 \% & 2 s / >8 cores \\
USVLab BSAODet \cite{10052705} & 63.21 \% & 72.86 \% & 59.48 \% & 0.04 s / 1 core \\
MLF-DET \cite{lin2023mlf} & 63.09 \% & 70.25 \% & 59.23 \% & 0.09 s / 1 core \\
CFM \cite{7807316} & 62.84 \% & 74.76 \% & 56.06 \% & \\
CasA \cite{casa2022} & 62.73 \% & 72.65 \% & 60.12 \% & 0.1 s / 1 core \\
Fast-CLOCs \cite{Pang2022WACV} & 62.57 \% & 76.22 \% & 60.13 \% & 0.1 s / GPU \\
PiFeNet \cite{le2022accurate} & 62.35 \% & 72.74 \% & 59.29 \% & 0.03 s / 1 core \\
HotSpotNet \cite{chen2020object} & 62.31 \% & 71.43 \% & 59.24 \% & 0.04 s / 1 core \\
P2V-RCNN \cite{P2VRCNN} & 61.83 \% & 71.76 \% & 59.29 \% & 0.1 s / 2 cores \\
MonoPair \cite{chen2020cvpr} & 61.57 \% & 78.81 \% & 56.51 \% & 0.06 s / GPU \\
monodle \cite{MA2021CVPR} & 61.29 \% & 78.66 \% & 56.18 \% & 0.04 s / GPU \\
RPN+BF \cite{Zhang2016ECCV} & 61.22 \% & 77.06 \% & 55.22 \% & 0.6 s / GPU \\
3ONet \cite{10183841} & 60.89 \% & 72.45 \% & 56.65 \% & 0.1 s / 1 core \\
Regionlets \cite{Wang2015PAMI} & 60.83 \% & 73.79 \% & 54.72 \% & 1 s / >8 cores \\
3DSSD \cite{yang3DSSD20} & 60.51 \% & 72.33 \% & 56.28 \% & 0.04 s / GPU \\
ACFNet \cite{10363115} & 60.12 \% & 71.42 \% & 55.96 \% & 0.11 s / 1 core \\
KPTr \cite{ERROR: Wrong syntax in BIBTEX file.} & 59.79 \% & 69.70 \% & 56.03 \% & 0.07 s / 1 core \\
DPPFA-Net \cite{10308573} & 59.52 \% & 67.68 \% & 56.87 \% & 0.1 s / 1 core \\
ACDet \cite{acdet} & 59.51 \% & 71.27 \% & 57.03 \% & 0.05 s / 1 core \\
QD-3DT \cite{Hu2021QD3DT} & 59.26 \% & 78.41 \% & 54.37 \% & 0.03 s / GPU \\
TANet \cite{liu2019tanet} & 59.07 \% & 69.90 \% & 56.44 \% & 0.035s / GPU \\
MonoUNI \cite{MonoUNI} & 58.97 \% & 76.17 \% & 53.99 \% & 0.04 s / 1 core \\
DSA-PV-RCNN \cite{bhattacharyya2021sadet3d} & 58.81 \% & 66.93 \% & 56.57 \% & 0.08 s / 1 core \\
SRDL \cite{ERROR: Wrong syntax in BIBTEX file.} & 58.70 \% & 68.45 \% & 56.23 \% & 0.05 s / 1 core \\
MMLab PV-RCNN \cite{shi2020pv} & 58.37 \% & 68.88 \% & 55.38 \% & 0.08 s / 1 core \\
PASS-PV-RCNN-Plus \cite{context} & 58.31 \% & 67.45 \% & 55.92 \% & 1 s / 1 core \\
Point-GNN \cite{shi2020pointgnn} & 58.20 \% & 71.59 \% & 54.06 \% & 0.6 s / GPU \\
DeepParts \cite{Tian2015ICCV} & 58.15 \% & 71.47 \% & 51.92 \% & ~1 s / GPU \\
CompACT-Deep \cite{Cai2015ICCV} & 58.14 \% & 70.93 \% & 52.29 \% & 1 s / 1 core \\
EPNet++ \cite{9983516} & 58.10 \% & 68.58 \% & 55.58 \% & 0.1 s / GPU \\
DSGN++ \cite{chen2022dsgn++} & 58.09 \% & 69.70 \% & 54.45 \% & 0.2 s / \\
MMLab-PartA^2 \cite{shi2020part} & 57.96 \% & 68.78 \% & 54.01 \% & 0.08 s / GPU \\
SVGA-Net \cite{he2022svga} & 57.92 \% & 67.81 \% & 55.25 \% & 0.03s / 1 core \\
AVOD-FPN \cite{ku2018joint} & 57.87 \% & 67.95 \% & 55.23 \% & 0.1 s / \\
DFAF3D \cite{tang2023dfaf3d} & 57.65 \% & 67.45 \% & 53.89 \% & 0.05 s / 1 core \\
Faraway-Frustum \cite{zhang2021faraway} & 57.35 \% & 67.88 \% & 54.42 \% & 0.1 s / GPU \\
PDV \cite{PDV} & 57.34 \% & 65.94 \% & 54.21 \% & 0.1 s / 1 core \\
SIF \cite{sif3d2d} & 57.32 \% & 67.78 \% & 54.86 \% & 0.1 s / 1 core \\
PG-RCNN \cite{koo2023pgrcnn} & 57.31 \% & 67.77 \% & 54.83 \% & 0.06 s / GPU \\
VPA \cite{ERROR: Wrong syntax in BIBTEX file.} & 57.27 \% & 70.06 \% & 54.83 \% & 0.01 s / 1 core \\
FromVoxelToPoint \cite{FromVoxelToPoint} & 57.26 \% & 68.26 \% & 54.74 \% & 0.1 s / 1 core \\
SemanticVoxels \cite{fei2020semanticvoxels} & 57.22 \% & 67.62 \% & 54.90 \% & 0.04 s / GPU \\
LVFSD \cite{ERROR: Wrong syntax in BIBTEX file.} & 57.20 \% & 67.44 \% & 54.67 \% & 0.06 s / \\
IA-SSD (single) \cite{zhang2022not} & 56.87 \% & 66.69 \% & 54.68 \% & 0.013 s / 1 core \\
CAT-Det \cite{zhang2022cat} & 56.75 \% & 67.15 \% & 53.44 \% & 0.3 s / GPU \\
FRCNN+Or \cite{GuindelITSM} & 56.68 \% & 71.64 \% & 51.53 \% & 0.09 s / \\
FilteredICF \cite{Zhang2015CVPR} & 56.53 \% & 69.79 \% & 50.32 \% & ~ 2 s / >8 cores \\
ARPNET \cite{Ye2019} & 56.42 \% & 69.08 \% & 52.69 \% & 0.08 s / GPU \\
MonoRUn \cite{monorun} & 56.40 \% & 73.05 \% & 51.40 \% & 0.07 s / GPU \\
MV-RGBD-RF \cite{Gonzalez2016TCYB} & 56.18 \% & 72.99 \% & 49.72 \% & 4 s / 4 cores \\
HMFI \cite{li2022homogeneous} & 55.96 \% & 66.20 \% & 53.24 \% & 0.1 s / 1 core \\
MGAF-3DSSD \cite{AnchorfreeMGASSD} & 55.80 \% & 66.31 \% & 52.02 \% & 0.1 s / 1 core \\
GUPNet \cite{lu2021geometry} & 55.65 \% & 74.95 \% & 48.44 \% & NA s / 1 core \\
MLOD \cite{deng2019mlod} & 55.62 \% & 68.42 \% & 51.45 \% & 0.12 s / GPU \\
DEVIANT \cite{kumar2022deviant} & 55.16 \% & 74.27 \% & 50.21 \% & 0.04 s / \\
PointPillars \cite{lang2018pointpillars} & 55.10 \% & 65.29 \% & 52.39 \% & 16 ms / \\
StereoDistill \cite{liu2020tanet} & 55.09 \% & 69.00 \% & 50.95 \% & 0.4 s / 1 core \\
STD \cite{std2019yang} & 55.04 \% & 68.33 \% & 50.85 \% & 0.08 s / GPU \\
OPA-3D \cite{su2023opa} & 54.92 \% & 73.93 \% & 47.87 \% & 0.04 s / 1 core \\
Vote3Deep \cite{Engelcke2016ARXIV} & 54.80 \% & 67.99 \% & 51.17 \% & 1.5 s / 4 cores \\
M3DeTR \cite{guan2021m3detr} & 54.78 \% & 63.15 \% & 52.49 \% & n/a s / GPU \\
L-AUG \cite{cortinhal2023semanticsaware} & 54.61 \% & 65.71 \% & 51.67 \% & 0.1 s / 1 core \\
epBRM \cite{arxiv} & 54.13 \% & 62.90 \% & 51.95 \% & 0.10 s / 1 core \\
DVFENet \cite{HE2021} & 54.13 \% & 63.54 \% & 51.79 \% & 0.05 s / 1 core \\
XView \cite{xie2021xview} & 53.83 \% & 62.27 \% & 51.61 \% & 0.1 s / 1 core \\
PointPainting \cite{vora2019pointpainting} & 53.76 \% & 61.86 \% & 50.61 \% & 0.4 s / GPU \\
PDV2 \cite{Shen2017PR} & 53.54 \% & 65.59 \% & 47.65 \% & 3.7 s / 1 core \\
Mix-Teaching \cite{Yang2022MixTeachingAS} & 53.52 \% & 67.34 \% & 47.45 \% & 30 s / 1 core \\
Cube R-CNN \cite{brazil2023omni3d} & 53.27 \% & 64.96 \% & 47.84 \% & 0.05 s / GPU \\
TAFT \cite{Shen2018 TITS} & 53.15 \% & 67.62 \% & 47.08 \% & 0.2 s / 1 core \\
Disp R-CNN \cite{sun2020disprcnn} & 52.98 \% & 71.79 \% & 48.20 \% & 0.387 s / GPU \\
Disp R-CNN (velo) \cite{sun2020disprcnn} & 52.90 \% & 71.63 \% & 48.15 \% & 0.387 s / GPU \\
pAUCEnsT \cite{Paul2014ARXIV} & 52.88 \% & 65.84 \% & 46.97 \% & 60 s / 1 core \\
SparVox3D \cite{9558880} & 52.84 \% & 69.33 \% & 48.49 \% & 0.05 s / GPU \\
PFF3D \cite{9340187} & 52.53 \% & 62.12 \% & 50.27 \% & 0.05 s / GPU \\
IA-SSD (multi) \cite{zhang2022not} & 52.45 \% & 65.07 \% & 50.20 \% & 0.014 s / 1 core \\
S-AT GCN \cite{DBLPjournalscorrabs210308439} & 52.30 \% & 62.01 \% & 50.10 \% & 0.02 s / GPU \\
MMLAB LIGA-Stereo \cite{Guo2021ICCV} & 52.18 \% & 65.59 \% & 49.29 \% & 0.4 s / 1 core \\
Plane-Constraints \cite{yao2023vertex} & 51.57 \% & 64.64 \% & 46.98 \% & 0.05 s / 4 cores \\
Shift R-CNN (mono) \cite{shiftrcnn} & 51.30 \% & 70.86 \% & 46.37 \% & 0.25 s / GPU \\
SCNet \cite{8813061} & 49.61 \% & 60.95 \% & 46.91 \% & 0.04 s / GPU \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 49.41 \% & 58.93 \% & 46.44 \% & 0.1 s / GPU \\
HomoLoss(monoflex) \cite{Gu2022CVPR} & 48.97 \% & 63.77 \% & 44.60 \% & 0.04 s / 1 core \\
ACFD \cite{DBLPconfivsDimitrievskiVP17} & 48.63 \% & 61.62 \% & 44.15 \% & 0.2 s / 4 cores \\
R-CNN \cite{Hosang2015DnnForPedestrians} & 48.57 \% & 62.88 \% & 43.05 \% & 4 s / GPU \\
VSAC \cite{ERROR: Wrong syntax in BIBTEX file.} & 48.22 \% & 60.72 \% & 45.55 \% & 0.07 s / 1 core \\
MonoLiG \cite{hekimoglu2023monocular} & 47.69 \% & 62.87 \% & 43.27 \% & 0.03 s / 1 core \\
MonoFlex \cite{monoflex} & 47.58 \% & 62.64 \% & 43.15 \% & 0.03 s / GPU \\
BirdNet+ \cite{barrera2021birdnet+} & 47.50 \% & 54.78 \% & 45.53 \% & 0.11 s / \\
CMKD \cite{YuHCMKDECCV2022} & 46.84 \% & 61.04 \% & 42.92 \% & 0.1 s / 1 core \\
MonOAPC \cite{yao2023occlusion} & 46.31 \% & 60.93 \% & 42.05 \% & 0035 s / 1 core \\
SS3D \cite{DBLPjournalscorrabs190608070} & 45.79 \% & 61.58 \% & 41.14 \% & 48 ms / \\
MonoRCNN++ \cite{MonoRCNNWACV23} & 45.76 \% & 60.29 \% & 39.39 \% & 0.07 s / GPU \\
ACF \cite{Dollar2014PAMI} & 45.67 \% & 59.81 \% & 40.88 \% & 1 s / 1 core \\
Fusion-DPM \cite{Premebida2014IROS} & 44.99 \% & 58.93 \% & 40.19 \% & ~ 30 s / 1 core \\
ACF-MR \cite{Nattoji2016TITS} & 44.79 \% & 58.29 \% & 39.94 \% & 0.6 s / 1 core \\
LPCG-Monoflex \cite{peng2022lidar} & 44.13 \% & 62.44 \% & 39.46 \% & 0.03 s / 1 core \\
HA-SSVM \cite{Xu2016IJCV} & 43.87 \% & 58.76 \% & 38.81 \% & 21 s / 1 core \\
AB3DMOT \cite{Weng2019} & 43.86 \% & 54.55 \% & 40.99 \% & 0.0047s / 1 core \\
MonoEF \cite{Zhou2021CVPR} & 43.73 \% & 58.79 \% & 39.45 \% & 0.03 s / 1 core \\
D4LCN \cite{ding2019learning} & 43.50 \% & 59.55 \% & 37.12 \% & 0.2 s / GPU \\
DMF \cite{chen2022DMF} & 43.43 \% & 52.99 \% & 41.29 \% & 0.2 s / 1 core \\
MonoDDE \cite{liu2020smoke} & 43.36 \% & 57.80 \% & 39.00 \% & 0.04 s / 1 core \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 43.26 \% & 59.21 \% & 38.12 \% & 8 s / 1 core \\
ACF-SC \cite{Cadena2015ICRA} & 42.97 \% & 53.30 \% & 38.12 \% & \\
MonoDTR \cite{huang2022monodtr} & 42.86 \% & 59.44 \% & 38.57 \% & 0.04 s / 1 core \\
SquaresICF \cite{Benenson2013Cvpr} & 42.61 \% & 57.08 \% & 37.85 \% & 1 s / GPU \\
CG-Stereo \cite{li2020confidence} & 42.54 \% & 54.64 \% & 38.45 \% & 0.57 s / \\
BirdNet+ (legacy) \cite{9294293} & 41.97 \% & 51.38 \% & 40.15 \% & 0.1 s / \\
DDMP-3D \cite{ddmp3d} & 41.54 \% & 56.73 \% & 35.52 \% & 0.18 s / 1 core \\
CSW3D \cite{hu2019csw3d} & 41.50 \% & 53.76 \% & 37.25 \% & 0.03 s / 4 cores \\
M3D-RPN \cite{brazil2019m3drpn} & 41.46 \% & 56.64 \% & 37.31 \% & 0.16 s / GPU \\
YOLOStereo3D \cite{liu2021yolostereo3d} & 41.46 \% & 56.20 \% & 37.07 \% & 0.1 s / \\
BKDStereo3D \cite{ERROR: Wrong syntax in BIBTEX file.} & 41.17 \% & 55.94 \% & 34.99 \% & 0.1 s / 1 core \\
CIE \cite{ye2022consistency} & 41.04 \% & 53.27 \% & 37.73 \% & 0.1 s / 1 core \\
SubCat \cite{OhnBar2014CVPRWORK} & 40.50 \% & 53.75 \% & 35.66 \% & 1.2 s / 6 cores \\
PS-fld \cite{Chen2022CVPR} & 40.47 \% & 55.47 \% & 36.65 \% & 0.25 s / 1 core \\
DSGN \cite{Chen2020dsgn} & 39.93 \% & 49.28 \% & 38.13 \% & 0.67 s / \\
RT3D-GMP \cite{konigshof2020learning} & 39.83 \% & 55.56 \% & 35.18 \% & 0.06 s / GPU \\
SparsePool \cite{wang2017fusing} & 39.59 \% & 50.81 \% & 35.91 \% & 0.13 s / 8 cores \\
SparsePool \cite{wang2017fusing} & 39.43 \% & 50.94 \% & 35.78 \% & 0.13 s / 8 cores \\
AVOD \cite{ku2018joint} & 39.43 \% & 50.90 \% & 35.75 \% & 0.08 s / \\
ACF \cite{Dollar2014PAMI} & 39.12 \% & 48.42 \% & 35.03 \% & 0.2 s / 1 core \\
MonoLTKD\_V3 \cite{ERROR: Wrong syntax in BIBTEX file.} & 38.60 \% & 54.33 \% & 34.12 \% & 0.04 s / 1 core \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 37.26 \% & 50.74 \% & 33.13 \% & 10 s / 4 cores \\
BKDStereo3D w/o KD \cite{ERROR: Wrong syntax in BIBTEX file.} & 37.02 \% & 50.58 \% & 32.92 \% & 0.1 s / 1 core \\
multi-task CNN \cite{Oeljeklaus18} & 37.00 \% & 49.38 \% & 33.46 \% & 25.1 ms / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 36.45 \% & 42.16 \% & 32.91 \% & 0.06 s / GPU \\
LSVM-MDPM-us \cite{Felzenszwalb2010PAMI} & 35.92 \% & 48.73 \% & 31.70 \% & 10 s / 4 cores \\
CMAN \cite{CMAN2022} & 34.96 \% & 49.73 \% & 30.92 \% & 0.15 s / 1 core \\
Aug3D-RPN \cite{he2021aug3drpn} & 34.95 \% & 47.22 \% & 30.64 \% & 0.08 s / 1 core \\
FMF-occlusion-net \cite{liu2022fine} & 34.74 \% & 49.26 \% & 30.37 \% & 0.16 s / 1 core \\
MonoNeRD \cite{xu2023mononerd} & 34.43 \% & 46.50 \% & 31.06 \% & na s / 1 core \\
PointRGBNet \cite{Xie Desheng340} & 33.92 \% & 44.35 \% & 30.43 \% & 0.08 s / 4 cores \\
PGD-FCOS3D \cite{PGD} & 33.67 \% & 48.30 \% & 29.76 \% & 0.03 s / 1 core \\
Vote3D \cite{Wang2015RSS} & 33.04 \% & 42.66 \% & 30.59 \% & 0.5 s / 4 cores \\
ESGN \cite{9869894} & 32.60 \% & 44.09 \% & 29.10 \% & 0.06 s / GPU \\
SGM3D \cite{zhou2021sgm3d} & 32.48 \% & 45.03 \% & 28.58 \% & 0.03 s / 1 core \\
CaDDN \cite{CaDDN} & 32.42 \% & 46.35 \% & 29.98 \% & 0.63 s / GPU \\
DFR-Net \cite{dfr2021} & 31.84 \% & 45.20 \% & 27.94 \% & 0.18 s / \\
OC Stereo \cite{pon2020object} & 30.79 \% & 43.50 \% & 28.40 \% & 0.35 s / 1 core \\
mBoW \cite{Behley2013IROS} & 30.26 \% & 41.52 \% & 26.34 \% & 10 s / 1 core \\
BirdNet \cite{BirdNet2018} & 30.07 \% & 36.82 \% & 28.40 \% & 0.11 s / \\
RT3DStereo \cite{Koenigshof2019Objects} & 29.30 \% & 41.12 \% & 25.25 \% & 0.08 s / GPU \\
MDSNet \cite{xie2022mds} & 29.25 \% & 41.64 \% & 26.01 \% & 0.05 s / 1 core \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 25.34 \% & 36.40 \% & 22.00 \% & 15 s / 4 cores \\
RefinedMPL \cite{vianney2019refinedmpl} & 20.81 \% & 30.41 \% & 18.72 \% & 0.15 s / GPU \\
TopNet-Retina \cite{8569433} & 16.45 \% & 22.37 \% & 15.43 \% & 52ms / \\
TopNet-HighRes \cite{8569433} & 15.28 \% & 21.22 \% & 13.89 \% & 101ms / \\
YOLOv2 \cite{redmon2016you} & 11.46 \% & 15.37 \% & 9.67 \% & 0.02 s / GPU \\
TopNet-UncEst \cite{wirges2019capturing} & 8.58 \% & 13.00 \% & 7.38 \% & 0.09 s / \\
BIP-HETERO \cite{Mekonnen2014ICPR} & 7.05 \% & 8.51 \% & 6.30 \% & ~2 s / 1 core \\
TopNet-DecayRate \cite{8569433} & 0.01 \% & 0.01 \% & 0.01 \% & 92 ms /
\end{tabular}