\begin{tabular}{c | c | c | c | c}
{\bf Method} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime}\\ \hline
TuSimple \cite{yang2016exploit} & 90.33 \% & 90.77 \% & 82.86 \% & 1.6 s / GPU \\
RRC \cite{Ren17CVPR} & 90.23 \% & 90.61 \% & 87.44 \% & 3.6 s / GPU \\
UberATG-MMF \cite{Liang2019CVPR} & 90.17 \% & 91.82 \% & 88.54 \% & 0.08 s / GPU \\
PC-CNN-V2 \cite{8461232} & 90.15 \% & 90.79 \% & 87.58 \% & 0.5 s / GPU \\
SJTU-HW \cite{zsq2018icip} & 90.08 \% & 90.81 \% & 79.98 \% & 0.85s / GPU \\
Deep MANTA \cite{deepmantacvpr17} & 90.03 \% & 97.25 \% & 80.62 \% & 0.7 s / GPU \\
sensekitti \cite{binyang16craft} & 90.00 \% & 90.76 \% & 81.83 \% & 4.5 s / GPU \\
F-PointNet \cite{qi2017frustum} & 90.00 \% & 90.78 \% & 80.80 \% & 0.17 s / GPU \\
Cascade MS-CNN \cite{cai2019cascade} & 89.95 \% & 90.68 \% & 78.40 \% & 0.25 s / GPU \\
HRI-VoxelFPN \cite{wang2019voxelFPN} & 89.89 \% & 90.66 \% & 80.97 \% & 0.02 s / GPU \\
F-ConvNet \cite{wang2019frustum} & 89.79 \% & 90.44 \% & 80.66 \% & 0.47 s / GPU \\
SINet+ \cite{hu2019sinet} & 89.73 \% & 90.51 \% & 77.82 \% & 0.3 s / \\
STD \cite{std2019yang} & 89.72 \% & 90.57 \% & 88.90 \% & 0.08 s / GPU \\
Fast Point R-CNNv1.1 \cite{Chen2019fastpointrcnn} & 89.71 \% & 90.59 \% & 88.13 \% & 0.06 s / GPU \\
Aston-EAS \cite{wei2019enhanced} & 89.64 \% & 90.49 \% & 77.95 \% & 0.24 s / GPU \\
SINet\_VGG \cite{hu2019sinet} & 89.56 \% & 90.60 \% & 78.19 \% & 0.2 s / \\
SDP+RPN \cite{Yang2016CVPR} & 89.42 \% & 89.90 \% & 78.54 \% & 0.4 s / GPU \\
MMLab-PartA^2 \cite{shi2019part} & 89.34 \% & 90.60 \% & 87.57 \% & 0.08 s / GPU \\
MMLab-PointRCNN \cite{shi2019pointrcnn} & 89.32 \% & 90.74 \% & 85.73 \% & 0.1 s / GPU \\
AB3DMOT \cite{Weng2019} & 89.28 \% & 90.67 \% & 86.45 \% & 0.0047s / 1 core \\
3D IoU Loss \cite{zhou2019} & 89.26 \% & 90.34 \% & 80.62 \% & 0.08 s / GPU \\
ITVD \cite{liu2018learning} & 89.23 \% & 90.57 \% & 79.31 \% & 0.3 s / GPU \\
PointPillars \cite{lang2018pointpillars} & 89.22 \% & 90.33 \% & 87.04 \% & 16 ms / \\
MV3D \cite{Chen2017CVPR} & 89.17 \% & 90.53 \% & 80.16 \% & 0.36 s / GPU \\
SINet\_PVA \cite{hu2019sinet} & 89.08 \% & 90.44 \% & 75.85 \% & 0.11 s / \\
CLA \cite{Zhang2019CVPR} & 88.99 \% & 90.51 \% & 75.50 \% & 0.3 s / GPU \\
Shift R-CNN (mono) \cite{shiftrcnn} & 88.90 \% & 90.56 \% & 79.86 \% & 0.25 s / GPU \\
SubCNN \cite{xiang2017subcategory} & 88.86 \% & 90.75 \% & 79.24 \% & 2 s / GPU \\
Deep3DBox \cite{MousavianCVPR2017} & 88.86 \% & 90.47 \% & 77.60 \% & 1.5 s / GPU \\
MonoPSR \cite{ku2019monopsr} & 88.84 \% & 90.18 \% & 71.44 \% & 0.2 s / GPU \\
FQNet \cite{liu2019deep} & 88.83 \% & 90.45 \% & 77.55 \% & 0.5 s / 1 core \\
MS-CNN \cite{Cai2016ECCV} & 88.83 \% & 90.46 \% & 74.76 \% & 0.4 s / GPU \\
DeepStereoOP \cite{Pham2017SPIC} & 88.75 \% & 90.34 \% & 79.39 \% & 3.4 s / GPU \\
3DBN \cite{DBLPjournalscorrabs190108373} & 88.62 \% & 90.30 \% & 80.08 \% & 0.13s / \\
SECOND \cite{yan2018second} & 88.40 \% & 90.40 \% & 80.21 \% & 38 ms / \\
3DOP \cite{Chen2015NIPS} & 88.34 \% & 90.09 \% & 78.79 \% & 3s / GPU \\
GPP \cite{rangesh2018ground} & 88.24 \% & 90.42 \% & 79.02 \% & 0.23 s / GPU \\
MM-MRFC \cite{Costea2017CVPR} & 88.20 \% & 90.93 \% & 78.02 \% & 0.05 s / GPU \\
AVOD \cite{ku2018joint} & 88.08 \% & 89.73 \% & 80.14 \% & 0.08 s / \\
Mono3D \cite{Chen2016CVPR} & 87.86 \% & 90.27 \% & 78.09 \% & 4.2 s / GPU \\
AVOD-FPN \cite{ku2018joint} & 87.44 \% & 89.99 \% & 80.05 \% & 0.1 s / \\
ODES \cite{ERROR: Wrong syntax in BIBTEX file.} & 87.10 \% & 86.82 \% & 78.32 \% & 0.02 s / GPU \\
AM3D \cite{ma2019accurate} & 85.42 \% & 87.33 \% & 77.43 \% & 0.4 s / GPU \\
M3D-RPN \cite{brazil2019m3drpn} & 83.78 \% & 84.34 \% & 67.85 \% & 0.16 s / GPU \\
StereoFENet \cite{monofenet} & 83.65 \% & 89.01 \% & 77.12 \% & 0.15 s / 1 core \\
MonoFENet \cite{monofenet} & 82.54 \% & 89.10 \% & 76.39 \% & 0.15 s / 1 core \\
A3DODWTDA (image) \cite{erino397fregu856master2018} & 81.54 \% & 76.21 \% & 66.85 \% & 0.8 s / GPU \\
SDP+CRC (ft) \cite{Yang2016CVPR} & 81.33 \% & 90.39 \% & 70.33 \% & 0.6 s / GPU \\
ResNet-RRC \cite{rrcresnet} & 81.00 \% & 89.89 \% & 71.56 \% & 0.06 s / GPU \\
Stereo R-CNN \cite{licvpr2019} & 80.80 \% & 90.23 \% & 71.42 \% & 0.3 s / GPU \\
SS3D \cite{DBLPjournalscorrabs190608070} & 80.11 \% & 89.15 \% & 70.52 \% & 48 ms / \\
BS3D \cite{gahlert2019beyond} & 80.02 \% & 89.85 \% & 70.14 \% & 22 ms / \\
MV3D (LIDAR) \cite{Chen2017CVPR} & 79.76 \% & 89.80 \% & 78.61 \% & 0.24 s / GPU \\
Complexer-YOLO \cite{Simon2019CVPRWorkshops} & 79.31 \% & 88.11 \% & 79.11 \% & 0.06 s / GPU \\
RefineNet \cite{7944662} & 79.21 \% & 90.16 \% & 65.71 \% & 0.20 s / GPU \\
Faster R-CNN \cite{Ren2015NIPS} & 79.11 \% & 87.90 \% & 70.19 \% & 2 s / GPU \\
FRCNN+Or \cite{GuindelITSM} & 78.95 \% & 89.87 \% & 68.97 \% & 0.09 s / \\
MonoGRNet \cite{qin2019monogrnet} & 77.46 \% & 87.23 \% & 61.12 \% & 0.04s / \\
spLBP \cite{Hu2016TITS} & 77.39 \% & 80.16 \% & 60.59 \% & 1.5 s / 8 cores \\
yolov3\_warp \cite{ERROR: Wrong syntax in BIBTEX file.} & 76.73 \% & 89.13 \% & 67.70 \% & 0.5 s / 1 core \\
Reinspect \cite{Stewart2016CVPR} & 76.65 \% & 88.36 \% & 66.56 \% & 2s / 1 core \\
Regionlets \cite{Wang2015PAMI} & 76.56 \% & 86.50 \% & 59.82 \% & 1 s / >8 cores \\
AOG \cite{Wu2016PAMI} & 75.97 \% & 85.58 \% & 60.96 \% & 3 s / 4 cores \\
GS3D \cite{li2019gs3d} & 75.84 \% & 83.92 \% & 60.24 \% & 2 s / 1 core \\
3D FCN \cite{li2017iros} & 75.83 \% & 85.54 \% & 68.30 \% & >5 s / 1 core \\
3D-SSMFCNN \cite{novakmaster2017} & 75.78 \% & 75.51 \% & 67.75 \% & 0.1 s / GPU \\
3DVP \cite{Xiang2015CVPR} & 75.77 \% & 81.46 \% & 65.38 \% & 40 s / 8 cores \\
Pose-RCNN \cite{braun2016pose} & 75.74 \% & 88.89 \% & 61.86 \% & 2 s / >8 cores \\
SubCat \cite{OhnBar2015TITS} & 75.46 \% & 81.45 \% & 59.71 \% & 0.7 s / 6 cores \\
multi-task CNN \cite{Oeljeklaus18} & 75.21 \% & 83.45 \% & 66.89 \% & 25.1 ms / GPU \\
A3DODWTDA \cite{erino397fregu856master2018} & 74.71 \% & 78.21 \% & 66.70 \% & 0.08 s / GPU \\
Int-YOLO \cite{ERROR: Wrong syntax in BIBTEX file.} & 70.65 \% & 74.76 \% & 63.70 \% & 0.03 s / 1 core \\
MV-RGBD-RF \cite{Gonzalez2016TCYB} & 69.92 \% & 76.49 \% & 57.47 \% & 4 s / 4 cores \\
AOG-View \cite{Li2014ECCV} & 69.89 \% & 84.29 \% & 57.25 \% & 3 s / 1 core \\
ROI-10D \cite{manhardt2018roi10d} & 69.64 \% & 75.33 \% & 61.18 \% & 0.2 s / GPU \\
Vote3Deep \cite{Engelcke2016ARXIV} & 68.39 \% & 76.95 \% & 63.22 \% & 1.5 s / 4 cores \\
Pseudo-LiDAR \cite{wangcvpr2019} & 67.96 \% & 85.08 \% & 59.55 \% & 0.4 s / GPU \\
BdCost48LDCF \cite{FernandezBaldera2018} & 67.08 \% & 77.93 \% & 51.15 \% & 0.5 s / 8 cores \\
OC-DPM \cite{Pepik2013CVPR} & 66.45 \% & 76.16 \% & 53.70 \% & 10 s / 8 cores \\
DPM-VOC+VP \cite{Pepik2015PAMI} & 66.25 \% & 80.45 \% & 49.86 \% & 8 s / 1 core \\
MDPM-un-BB \cite{Felzenszwalb10} & 64.20 \% & 77.32 \% & 50.18 \% & 60 s / 4 core \\
PDV-Subcat \cite{Shen2017PR} & 63.15 \% & 77.33 \% & 49.75 \% & 7 s / 1 core \\
DPM-C8B1 \cite{Yebes2015SENSORS} & 60.99 \% & 74.95 \% & 47.16 \% & 15 s / 4 cores \\
SubCat48LDCF \cite{FernandezBaldera2018} & 60.53 \% & 78.16 \% & 43.66 \% & 0.5 s / 8 cores \\
SAMME48LDCF \cite{FernandezBaldera2018} & 58.50 \% & 76.22 \% & 47.50 \% & 0.5 s / 8 cores \\
BirdNet \cite{BirdNet2018} & 57.47 \% & 78.18 \% & 56.66 \% & 0.11 s / \\
LSVM-MDPM-sv \cite{Felzenszwalb2010PAMI} & 57.44 \% & 71.70 \% & 46.58 \% & 10 s / 4 cores \\
LSVM-MDPM-us \cite{Felzenszwalb2010PAMI} & 56.10 \% & 70.52 \% & 42.87 \% & 10 s / 4 cores \\
ACF-SC \cite{Cadena2015ICRA} & 55.76 \% & 69.76 \% & 46.27 \% & \\
Mono3D\_PLiDAR \cite{Weng2019} & 54.41 \% & 80.29 \% & 46.67 \% & 0.1 s / \\
ACF \cite{Dollar2014PAMI} & 52.81 \% & 62.82 \% & 43.89 \% & 0.2 s / 1 core \\
TopNet-HighRes \cite{8569433} & 48.87 \% & 59.77 \% & 43.15 \% & 101ms / \\
Vote3D \cite{Wang2015RSS} & 48.05 \% & 56.66 \% & 42.64 \% & 0.5 s / 4 cores \\
Multimodal Detection \cite{asvadi2017multimodal} & 46.77 \% & 64.04 \% & 39.38 \% & 0.06 s / GPU \\
RT3D \cite{8403277} & 39.71 \% & 49.96 \% & 41.47 \% & 0.09 s / GPU \\
CSoR \cite{Plotkin2015} & 26.13 \% & 35.24 \% & 22.69 \% & 3.5 s / 4 cores \\
mBoW \cite{Behley2013IROS} & 23.76 \% & 37.63 \% & 18.44 \% & 10 s / 1 core \\
DepthCN \cite{asvadi2017depthcn} & 23.21 \% & 37.59 \% & 18.00 \% & 2.3 s / GPU \\
YOLOv2 \cite{redmon2016you} & 19.31 \% & 28.37 \% & 15.94 \% & 0.02 s / GPU \\
TopNet-UncEst \cite{wirges2019capturing} & 13.77 \% & 10.35 \% & 13.49 \% & 0.09 s / \\
TopNet-Retina \cite{8569433} & 6.36 \% & 7.79 \% & 6.31 \% & 52ms / \\
TopNet-DecayRate \cite{8569433} & 0.04 \% & 0.04 \% & 0.04 \% & 92 ms / \\
LaserNet \cite{lasernet} & 0.00 \% & 0.00 \% & 0.00 \% & 12 ms / GPU
\end{tabular}