\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
Patches - EMP \cite{lehner2019patch} & 93.58 \% & 97.88 \% & 90.31 \% & 0.5 s / GPU \\
Deep MANTA \cite{deepmantacvpr17} & 93.31 \% & 98.83 \% & 82.95 \% & 0.7 s / GPU \\
SARPNET \cite{ye2019sarpnet} & 92.58 \% & 95.82 \% & 87.33 \% & 0.05 s / 1 core \\
Patches \cite{lehner2019patch} & 92.57 \% & 96.31 \% & 87.41 \% & 0.15 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 91.98 \% & 95.81 \% & 79.83 \% & 0.47 s / GPU \\
AB3DMOT \cite{Weng2019} & 91.87 \% & 95.86 \% & 86.78 \% & 0.0047s / 1 core \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 91.77 \% & 95.90 \% & 86.92 \% & 0.1 s / GPU \\
MMLab-PartA^2 \cite{shi2019part} & 91.73 \% & 95.00 \% & 88.86 \% & 0.08 s / GPU \\
HRI-VoxelFPN \cite{wang2019voxelFPN} & 90.76 \% & 96.35 \% & 85.37 \% & 0.02 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 90.70 \% & 93.84 \% & 87.47 \% & 16 ms / \\
3D IoU Loss \cite{zhou2019} & 90.21 \% & 95.60 \% & 84.96 \% & 0.08 s / GPU \\
ARPNET \cite{Ye2019} & 90.11 \% & 93.42 \% & 82.56 \% & 0.08 s / GPU \\
Deep3DBox \cite{MousavianCVPR2017} & 89.88 \% & 94.62 \% & 76.40 \% & 1.5 s / GPU \\
VOXEL\_FPN\_HR \cite{ERROR: Wrong syntax in BIBTEX file.} & 89.81 \% & 93.52 \% & 84.59 \% & 0.12 s / 8 cores \\
GPP \cite{rangesh2018ground} & 89.68 \% & 93.94 \% & 80.60 \% & 0.23 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 89.53 \% & 94.11 \% & 79.14 \% & 2 s / GPU \\
SCNet \cite{8813061} & 89.36 \% & 95.23 \% & 84.03 \% & 0.04 s / GPU \\
AVOD \cite{ku2018joint} & 89.22 \% & 94.98 \% & 82.14 \% & 0.08 s / \\
AVOD-FPN \cite{ku2018joint} & 88.61 \% & 94.65 \% & 83.71 \% & 0.1 s / \\
DeepStereoOP \cite{Pham2017SPIC} & 87.81 \% & 93.68 \% & 77.60 \% & 3.4 s / GPU \\
3DBN \cite{DBLPjournalscorrabs190108373} & 87.59 \% & 93.34 \% & 79.91 \% & 0.13s / \\
FQNet \cite{liu2019deep} & 87.49 \% & 93.66 \% & 73.61 \% & 0.5 s / 1 core \\
Shift R-CNN (mono) \cite{shiftrcnn} & 87.47 \% & 93.75 \% & 77.19 \% & 0.25 s / GPU \\
MonoPSR \cite{ku2019monopsr} & 87.45 \% & 93.29 \% & 72.26 \% & 0.2 s / GPU \\
Mono3D \cite{Chen2016CVPR} & 87.28 \% & 93.13 \% & 77.00 \% & 4.2 s / GPU \\
3DOP \cite{Chen2015NIPS} & 86.93 \% & 91.31 \% & 76.72 \% & 3s / GPU \\
StereoFENet \cite{monofenet} & 85.14 \% & 91.28 \% & 76.80 \% & 0.15 s / 1 core \\
SS3D \cite{DBLPjournalscorrabs190608070} & 84.38 \% & 92.57 \% & 69.82 \% & 48 ms / \\
MonoFENet \cite{monofenet} & 84.09 \% & 91.42 \% & 75.93 \% & 0.15 s / 1 core \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 83.89 \% & 91.77 \% & 79.24 \% & 0.06 s / GPU \\
M3D-RPN \cite{brazil2019m3drpn} & 82.81 \% & 88.38 \% & 67.08 \% & 0.16 s / GPU \\
SECOND \cite{yan2018second} & 82.55 \% & 90.93 \% & 73.62 \% & 38 ms / \\
BS3D \cite{gahlert2019beyond} & 81.22 \% & 94.66 \% & 68.39 \% & 22 ms / \\
FRCNN+Or \cite{GuindelITSM} & 80.57 \% & 91.50 \% & 67.49 \% & 0.09 s / \\
3D-SSMFCNN \cite{novakmaster2017} & 77.82 \% & 77.84 \% & 68.67 \% & 0.1 s / GPU \\
3DVP \cite{Xiang2015CVPR} & 75.71 \% & 84.44 \% & 64.41 \% & 40 s / 8 cores \\
GS3D \cite{li2019gs3d} & 75.63 \% & 85.79 \% & 61.85 \% & 2 s / 1 core \\
Pose-RCNN \cite{braun2016pose} & 75.41 \% & 89.49 \% & 63.57 \% & 2 s / >8 cores \\
SubCat \cite{OhnBar2015TITS} & 75.26 \% & 83.31 \% & 59.55 \% & 0.7 s / 6 cores \\
3D FCN \cite{li2017iros} & 74.54 \% & 86.65 \% & 67.73 \% & >5 s / 1 core \\
ROI-10D \cite{manhardt2018roi10d} & 68.14 \% & 75.32 \% & 58.98 \% & 0.2 s / GPU \\
multi-task CNN \cite{Oeljeklaus18} & 67.51 \% & 79.00 \% & 58.80 \% & 25.1 ms / GPU \\
BdCost48LDCF \cite{FernandezBaldera2018} & 65.50 \% & 80.44 \% & 51.24 \% & 0.5 s / 8 cores \\
OC-DPM \cite{Pepik2013CVPR} & 65.32 \% & 77.35 \% & 51.00 \% & 10 s / 8 cores \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 63.58 \% & 79.09 \% & 46.59 \% & 8 s / 1 core \\
AOG-View \cite{Li2014ECCV} & 62.62 \% & 77.62 \% & 48.27 \% & 3 s / 1 core \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 57.48 \% & 70.23 \% & 42.54 \% & 10 s / 4 cores \\
SAMME48LDCF \cite{FernandezBaldera2018} & 57.26 \% & 76.28 \% & 43.55 \% & 0.5 s / 8 cores \\
Mono3D\_PLiDAR \cite{Weng2019} & 49.39 \% & 76.90 \% & 41.13 \% & 0.1 s / \\
ODES \cite{ERROR: Wrong syntax in BIBTEX file.} & 48.86 \% & 48.07 \% & 41.72 \% & 0.02 s / GPU \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 48.00 \% & 57.76 \% & 35.52 \% & 15 s / 4 cores \\
LTN \cite{8836625} & 46.54 \% & 48.96 \% & 41.58 \% & 0.4 s / GPU \\
sensekitti \cite{binyang16craft} & 46.12 \% & 49.16 \% & 42.79 \% & 4.5 s / GPU \\
IPOD \cite{yang2018ipod} & 37.79 \% & 38.58 \% & 36.57 \% & 0.2 s / GPU \\
BirdNet \cite{BirdNet2018} & 35.00 \% & 50.34 \% & 33.40 \% & 0.11 s / \\
AOG \cite{Wu2016PAMI} & 29.81 \% & 33.28 \% & 23.91 \% & 3 s / 4 cores \\
SubCat48LDCF \cite{FernandezBaldera2018} & 26.68 \% & 34.33 \% & 19.44 \% & 0.5 s / 8 cores \\
RT3DStereo \cite{Koenigshof2019Objects} & 21.41 \% & 25.58 \% & 17.52 \% & 0.08 s / GPU \\
CSoR \cite{Plotkin2015} & 20.82 \% & 30.65 \% & 17.14 \% & 3.5 s / 4 cores \\
RT3D \cite{8403277} & 18.96 \% & 24.41 \% & 19.85 \% & 0.09 s / GPU \\
VoxelJones \cite{motro2019vehicular} & 15.41 \% & 17.83 \% & 14.13 \% & .18 s / 1 core
\end{tabular}