\begin{tabular}{c | c | c | c | c | c | c | c}
{\bf Method} & {\bf MaxF} & {\bf AP} & {\bf PRE} & {\bf REC} & {\bf FPR} & {\bf FNR} & {\bf Runtime}\\ \hline
PLARD \cite{chen2019progressive} & 97.05 \% & 93.53 \% & 97.18 \% & 96.92 \% & 1.28 \% & 3.08 \% & 0.16 s / GPU \\
SNE-RoadSeg \cite{fan2020sneroadseg} & 96.42 \% & 93.67 \% & 96.59 \% & 96.26 \% & 1.55 \% & 3.74 \% & 0.2 s / GPU \\
RBANet \cite{sun2019reverse} & 95.78 \% & 89.14 \% & 94.92 \% & 96.66 \% & 2.36 \% & 3.34 \% & 0.16 s / GPU \\
NIM-RTFNet \cite{wang2020applying} & 95.71 \% & 93.56 \% & 95.84 \% & 95.59 \% & 1.89 \% & 4.41 \% & 0.05 s / GPU \\
LidCamNet \cite{1809.07941} & 95.62 \% & 93.54 \% & 95.77 \% & 95.48 \% & 1.92 \% & 4.52 \% & 0.15 s / GPU \\
TVFNet \cite{GuZYAK19} & 94.96 \% & 89.17 \% & 94.95 \% & 94.97 \% & 2.30 \% & 5.03 \% & 0.04 s / GPU \\
LC-CRF \cite{GuZTYK19} & 94.91 \% & 86.41 \% & 91.92 \% & 98.11 \% & 3.93 \% & 1.89 \% & 0.18 s / GPU \\
RBNet \cite{chen2017rbnet} & 94.77 \% & 91.42 \% & 95.16 \% & 94.37 \% & 2.19 \% & 5.63 \% & 0.18 s / GPU \\
SSLGAN \cite{Han2018Semisupervised} & 94.62 \% & 89.50 \% & 95.32 \% & 93.93 \% & 2.10 \% & 6.07 \% & 700ms / GPU \\
RGB36-Cotrain \cite{CaltagironeEtAl2019} & 94.55 \% & 93.12 \% & 94.81 \% & 94.29 \% & 2.35 \% & 5.71 \% & 0.1 s / 1 core \\
DEEP-DIG \cite{munozbulnesdeep2017} & 94.16 \% & 93.41 \% & 95.02 \% & 93.32 \% & 2.23 \% & 6.68 \% & 0.14 s / GPU \\
StixelNet II \cite{DanLevi2017ICCV} & 94.05 \% & 85.85 \% & 91.30 \% & 96.98 \% & 4.21 \% & 3.02 \% & 1.2 s / 1 core \\
MultiNet \cite{DBLPjournalscorrTeichmannWZCU16} & 93.99 \% & 93.24 \% & 94.51 \% & 93.48 \% & 2.47 \% & 6.52 \% & 0.17 s / GPU \\
ChipNet \cite{8580596} & 93.73 \% & 87.62 \% & 93.25 \% & 94.21 \% & 3.11 \% & 5.79 \% & 12 ms / GPU \\
DDN \cite{Mohan2014ARXIV} & 93.65 \% & 88.55 \% & 94.28 \% & 93.03 \% & 2.57 \% & 6.97 \% & 2 s / GPU \\
RoadNet3 \cite{lyu2019road} & 93.54 \% & 92.64 \% & 93.65 \% & 93.44 \% & 2.89 \% & 6.56 \% & 300 ms / GPU \\
HID-LS \cite{GuZYK17} & 93.10 \% & 86.38 \% & 91.89 \% & 94.33 \% & 3.79 \% & 5.67 \% & 0.25 s / 1 cores \\
RGB36-Super \cite{CaltagironeEtAl2019} & 93.04 \% & 91.85 \% & 93.62 \% & 92.46 \% & 2.87 \% & 7.54 \% & 0.1 s / 1 core \\
LoDNN \cite{CaltagironeEtAl2016} & 92.75 \% & 89.98 \% & 90.09 \% & 95.58 \% & 4.79 \% & 4.42 \% & 18 ms / GPU \\
PT-ResNet \cite{fan2019ptresnet} & 92.26 \% & 92.48 \% & 92.15 \% & 92.36 \% & 3.58 \% & 7.64 \% & 0.3 s / GPU \\
Up-Conv-Poly \cite{Oliveira2016IROS} & 92.20 \% & 88.85 \% & 92.57 \% & 91.83 \% & 3.36 \% & 8.17 \% & 0.08 s / GPU \\
OFA Net \cite{zhang2019one} & 92.08 \% & 82.73 \% & 87.87 \% & 96.72 \% & 6.08 \% & 3.28 \% & 0.04 s / GPU \\
MixedCRF \cite{Han2017Road} & 91.57 \% & 84.68 \% & 90.02 \% & 93.19 \% & 4.71 \% & 6.81 \% & 6s / 1 core \\
FTP \cite{Laddha2016IV} & 91.20 \% & 90.60 \% & 91.11 \% & 91.29 \% & 4.06 \% & 8.71 \% & 0.28 s / GPU \\
ALO-AVG-MM \cite{Reis2019IJCNN2019} & 91.15 \% & 83.82 \% & 89.07 \% & 93.33 \% & 5.22 \% & 6.67 \% & 0.0296 sec / \\
HybridCRF \cite{XIAO2018HybridCRF} & 90.99 \% & 85.26 \% & 90.65 \% & 91.33 \% & 4.29 \% & 8.67 \% & 1.5 s / 1 core \\
NNP \cite{Chen2015NIPS} & 90.50 \% & 87.95 \% & 91.43 \% & 89.59 \% & 3.83 \% & 10.41 \% & 5 s / 4 cores \\
Up-Conv \cite{Oliveira2016IROS} & 90.48 \% & 88.20 \% & 91.30 \% & 89.67 \% & 3.90 \% & 10.33 \% & 0.05 s / GPU \\
HIM \cite{Munoz2010ECCV} & 90.07 \% & 79.98 \% & 90.79 \% & 89.35 \% & 4.13 \% & 10.65 \% & 7 s / >8 cores \\
LidarHisto \cite{7989159} & 89.87 \% & 83.03 \% & 91.28 \% & 88.49 \% & 3.85 \% & 11.51 \% & 0.1 s / 1 core \\
FusedCRF \cite{Xiao2015IV} & 89.55 \% & 80.00 \% & 84.87 \% & 94.78 \% & 7.70 \% & 5.22 \% & 2 s / 1 core \\
BMCF \cite{wang2016multi} & 89.42 \% & 83.13 \% & 88.31 \% & 90.55 \% & 5.46 \% & 9.45 \% & 2.5 s / 1 core \\
FCN-LC \cite{Mendes2016ICRA} & 89.36 \% & 78.80 \% & 89.35 \% & 89.37 \% & 4.85 \% & 10.63 \% & 0.03 s / \\
CB \cite{Mendes2015ARXIV} & 88.89 \% & 82.17 \% & 87.26 \% & 90.58 \% & 6.03 \% & 9.42 \% & 2 s / 1 core \\
SPRAY \cite{Kuehnl2012ITSC} & 88.14 \% & 91.24 \% & 88.60 \% & 87.68 \% & 5.14 \% & 12.32 \% & 45 ms / \\
ProbBoost \cite{Vitor2014ICRAWORK} & 87.48 \% & 80.13 \% & 85.02 \% & 90.09 \% & 7.23 \% & 9.91 \% & 2.5 min / >8 cores \\
MAP \cite{Laddha2016IV} & 87.33 \% & 89.62 \% & 85.77 \% & 88.95 \% & 6.73 \% & 11.05 \% & 0.28s / \\
CN24 \cite{Brust2015CPN} & 86.32 \% & 89.19 \% & 87.80 \% & 84.89 \% & 5.37 \% & 15.11 \% & 30 s / >8 cores \\
multi-task CNN \cite{Oeljeklaus18} & 85.95 \% & 81.28 \% & 77.40 \% & 96.64 \% & 12.86 \% & 3.36 \% & 25.1 ms / GPU \\
GRES3D+VELO \cite{Shinzato2015} & 85.43 \% & 83.04 \% & 82.69 \% & 88.37 \% & 8.43 \% & 11.63 \% & 60 ms / 4 cores \\
StixelNet \cite{Levi2015BMVC} & 85.33 \% & 72.14 \% & 81.21 \% & 89.89 \% & 9.48 \% & 10.11 \% & 1 s / GPU \\
SPlane + BL \cite{Einecke2014IV} & 85.23 \% & 88.66 \% & 83.43 \% & 87.12 \% & 7.89 \% & 12.88 \% & 2 s / 1 core \\
geo+gpr+crf \cite{doi10.11771729881417717058} & 85.13 \% & 72.24 \% & 81.33 \% & 89.29 \% & 9.34 \% & 10.71 \% & 30 s / 1 core \\
RES3D-Velo \cite{Shinzato2014IV} & 83.81 \% & 73.95 \% & 78.56 \% & 89.80 \% & 11.16 \% & 10.20 \% & 0.36 s / 1 core \\
SCRFFPFHGSP \cite{Gheorghe2015} & 83.73 \% & 72.89 \% & 82.13 \% & 85.39 \% & 8.47 \% & 14.61 \% & 5 s / 8 cores \\
GRES3D+SELAS \cite{Shinzato2015} & 83.69 \% & 84.61 \% & 78.31 \% & 89.88 \% & 11.35 \% & 10.12 \% & 110 ms / 4 core \\
HistonBoost \cite{GioIV14} & 83.68 \% & 72.79 \% & 82.01 \% & 85.42 \% & 8.54 \% & 14.58 \% & 2.5 min / >8 cores \\
PGM-ARS \cite{Passani15IV} & 80.97 \% & 69.11 \% & 77.51 \% & 84.76 \% & 11.21 \% & 15.24 \% & 0.05 s / i74700MQ \\
RES3D-Stereo \cite{Shinzato2014ITSC} & 78.98 \% & 80.06 \% & 75.94 \% & 82.27 \% & 11.88 \% & 17.73 \% & 0.7 s / 1 core \\
BM \cite{Wang2014IVWORK} & 78.90 \% & 66.06 \% & 69.53 \% & 91.19 \% & 18.21 \% & 8.81 \% & 2 s / 2 cores \\
SPlane \cite{Einecke2014IV} & 78.19 \% & 76.41 \% & 72.03 \% & 85.50 \% & 15.13 \% & 14.50 \% & 2 s / 1 core \\
SRF \cite{Xiao2016IJARS} & 76.43 \% & 83.24 \% & 75.53 \% & 77.35 \% & 11.42 \% & 22.65 \% & 0.2 s / 1 core \\
CN24 \cite{Brust2015CPN} & 76.28 \% & 79.29 \% & 72.44 \% & 80.55 \% & 13.96 \% & 19.45 \% & 20 s / >8 cores \\
CN \cite{Alvarez2012ECCV} & 73.69 \% & 76.68 \% & 69.18 \% & 78.83 \% & 16.00 \% & 21.17 \% & 2 s / 1 core \\
ARSL-AMI \cite{Passani2014ITSC} & 71.97 \% & 61.04 \% & 78.03 \% & 66.79 \% & 8.57 \% & 33.21 \% & 0.05 s / 4 cores \\
ANN \cite{Vitor2013IV} & 62.83 \% & 46.77 \% & 50.21 \% & 83.91 \% & 37.91 \% & 16.09 \% & 3 s / 1 core
\end{tabular}