\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
Deep MANTA \cite{deepmantacvpr17} & 89.86 \% & 97.19 \% & 80.39 \% & 0.7 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 89.60 \% & 90.41 \% & 80.39 \% & 0.47 s / GPU \\
HRI-VoxelFPN \cite{wang2019voxelFPN} & 89.27 \% & 90.43 \% & 80.31 \% & 0.02 s / GPU \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 89.22 \% & 90.73 \% & 85.53 \% & 0.1 s / GPU \\
AB3DMOT \cite{Weng2019} & 89.16 \% & 90.66 \% & 86.24 \% & 0.0047s / 1 core \\
MMLab-PartA^2 \cite{shi2019part} & 88.98 \% & 90.41 \% & 87.08 \% & 0.08 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 88.76 \% & 90.19 \% & 86.38 \% & 16 ms / \\
3D IoU Loss \cite{zhou2019} & 88.72 \% & 90.08 \% & 80.06 \% & 0.08 s / GPU \\
Deep3DBox \cite{MousavianCVPR2017} & 88.56 \% & 90.39 \% & 77.17 \% & 1.5 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 88.43 \% & 90.61 \% & 78.63 \% & 2 s / GPU \\
GPP \cite{rangesh2018ground} & 87.96 \% & 90.35 \% & 78.57 \% & 0.23 s / GPU \\
3DBN \cite{DBLPjournalscorrabs190108373} & 87.95 \% & 89.93 \% & 79.32 \% & 0.13s / \\
Shift R-CNN (mono) \cite{shiftrcnn} & 87.91 \% & 90.27 \% & 78.72 \% & 0.25 s / GPU \\
MonoPSR \cite{ku2019monopsr} & 87.83 \% & 89.88 \% & 70.48 \% & 0.2 s / GPU \\
AVOD \cite{ku2018joint} & 87.46 \% & 89.59 \% & 79.54 \% & 0.08 s / \\
AVOD-FPN \cite{ku2018joint} & 87.13 \% & 89.95 \% & 79.74 \% & 0.1 s / \\
DeepStereoOP \cite{Pham2017SPIC} & 86.57 \% & 89.01 \% & 77.13 \% & 3.4 s / GPU \\
FQNet \cite{liu2019deep} & 86.29 \% & 89.48 \% & 74.40 \% & 0.5 s / 1 core \\
Mono3D \cite{Chen2016CVPR} & 85.83 \% & 89.00 \% & 76.00 \% & 4.2 s / GPU \\
3DOP \cite{Chen2015NIPS} & 85.81 \% & 88.56 \% & 76.21 \% & 3s / GPU \\
StereoFENet \cite{monofenet} & 83.13 \% & 88.83 \% & 76.33 \% & 0.15 s / 1 core \\
MonoFENet \cite{monofenet} & 82.05 \% & 88.86 \% & 75.63 \% & 0.15 s / 1 core \\
M3D-RPN \cite{brazil2019m3drpn} & 81.66 \% & 83.80 \% & 65.94 \% & 0.16 s / GPU \\
SECOND \cite{yan2018second} & 81.31 \% & 87.84 \% & 71.95 \% & 38 ms / \\
SS3D \cite{DBLPjournalscorrabs190608070} & 79.70 \% & 89.02 \% & 69.91 \% & 48 ms / \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 79.08 \% & 87.97 \% & 78.75 \% & 0.06 s / GPU \\
BS3D \cite{gahlert2019beyond} & 78.68 \% & 89.28 \% & 68.52 \% & 22 ms / \\
FRCNN+Or \cite{GuindelITSM} & 77.61 \% & 88.52 \% & 67.69 \% & 0.09 s / \\
3D FCN \cite{li2017iros} & 75.71 \% & 85.46 \% & 68.19 \% & >5 s / 1 core \\
3D-SSMFCNN \cite{novakmaster2017} & 75.42 \% & 75.44 \% & 67.27 \% & 0.1 s / GPU \\
Pose-RCNN \cite{braun2016pose} & 75.35 \% & 88.78 \% & 61.47 \% & 2 s / >8 cores \\
GS3D \cite{li2019gs3d} & 75.16 \% & 83.52 \% & 59.59 \% & 2 s / 1 core \\
3DVP \cite{Xiang2015CVPR} & 74.59 \% & 81.02 \% & 64.11 \% & 40 s / 8 cores \\
SubCat \cite{OhnBar2015TITS} & 74.42 \% & 80.74 \% & 58.83 \% & 0.7 s / 6 cores \\
ROI-10D \cite{manhardt2018roi10d} & 67.85 \% & 74.24 \% & 59.28 \% & 0.2 s / GPU \\
multi-task CNN \cite{Oeljeklaus18} & 66.19 \% & 76.69 \% & 58.11 \% & 25.1 ms / GPU \\
BdCost48LDCF \cite{FernandezBaldera2018} & 66.01 \% & 77.10 \% & 50.35 \% & 0.5 s / 8 cores \\
OC-DPM \cite{Pepik2013CVPR} & 64.88 \% & 74.66 \% & 52.24 \% & 10 s / 8 cores \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 63.27 \% & 77.51 \% & 47.57 \% & 8 s / 1 core \\
AOG-View \cite{Li2014ECCV} & 62.25 \% & 77.37 \% & 50.44 \% & 3 s / 1 core \\
SAMME48LDCF \cite{FernandezBaldera2018} & 57.49 \% & 75.12 \% & 46.64 \% & 0.5 s / 8 cores \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 56.69 \% & 70.86 \% & 45.91 \% & 10 s / 4 cores \\
Mono3D\_PLiDAR \cite{Weng2019} & 50.76 \% & 76.57 \% & 43.30 \% & 0.1 s / \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 50.32 \% & 59.53 \% & 39.22 \% & 15 s / 4 cores \\
ODES \cite{ERROR: Wrong syntax in BIBTEX file.} & 48.06 \% & 46.22 \% & 42.43 \% & 0.02 s / GPU \\
sensekitti \cite{binyang16craft} & 44.56 \% & 47.06 \% & 41.50 \% & 4.5 s / GPU \\
BirdNet \cite{BirdNet2018} & 35.81 \% & 50.85 \% & 34.90 \% & 0.11 s / \\
AOG \cite{Wu2016PAMI} & 30.81 \% & 34.05 \% & 24.86 \% & 3 s / 4 cores \\
SubCat48LDCF \cite{FernandezBaldera2018} & 26.78 \% & 34.43 \% & 19.46 \% & 0.5 s / 8 cores \\
CSoR \cite{Plotkin2015} & 25.38 \% & 34.43 \% & 21.95 \% & 3.5 s / 4 cores \\
RT3D \cite{8403277} & 18.98 \% & 24.23 \% & 20.56 \% & 0.09 s / GPU
\end{tabular}