\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} & 97.03 \% & 94.03 \% & 97.19 \% & 96.88 \% & 1.54 \% & 3.12 \% & 0.16 s / GPU \\
SNE-RoadSeg \cite{fan2020sneroadseg} & 96.75 \% & 94.07 \% & 96.90 \% & 96.61 \% & 1.70 \% & 3.39 \% & 0.2 s / GPU \\
RBANet \cite{sun2019reverse} & 96.30 \% & 89.72 \% & 95.14 \% & 97.50 \% & 2.75 \% & 2.50 \% & 0.16 s / GPU \\
LidCamNet \cite{1809.07941} & 96.03 \% & 93.93 \% & 96.23 \% & 95.83 \% & 2.07 \% & 4.17 \% & 0.15 s / GPU \\
NIM-RTFNet \cite{wang2020applying} & 96.02 \% & 94.01 \% & 96.43 \% & 95.62 \% & 1.95 \% & 4.38 \% & 0.05 s / GPU \\
LC-CRF \cite{GuZTYK19} & 95.68 \% & 88.34 \% & 93.62 \% & 97.83 \% & 3.67 \% & 2.17 \% & 0.18 s / GPU \\
RGB36-Cotrain \cite{CaltagironeEtAl2019} & 95.55 \% & 93.71 \% & 95.68 \% & 95.42 \% & 2.37 \% & 4.58 \% & 0.1 s / 1 core \\
SSLGAN \cite{Han2018Semisupervised} & 95.53 \% & 90.35 \% & 95.84 \% & 95.24 \% & 2.28 \% & 4.76 \% & 700ms / GPU \\
TVFNet \cite{GuZYAK19} & 95.34 \% & 90.26 \% & 95.73 \% & 94.94 \% & 2.33 \% & 5.06 \% & 0.04 s / GPU \\
RBNet \cite{chen2017rbnet} & 94.97 \% & 91.49 \% & 94.94 \% & 95.01 \% & 2.79 \% & 4.99 \% & 0.18 s / GPU \\
StixelNet II \cite{DanLevi2017ICCV} & 94.88 \% & 87.75 \% & 92.97 \% & 96.87 \% & 4.04 \% & 3.13 \% & 1.2 s / 1 core \\
MultiNet \cite{DBLPjournalscorrTeichmannWZCU16} & 94.88 \% & 93.71 \% & 94.84 \% & 94.91 \% & 2.85 \% & 5.09 \% & 0.17 s / GPU \\
RoadNet3 \cite{lyu2019road} & 94.44 \% & 93.45 \% & 94.69 \% & 94.18 \% & 2.91 \% & 5.82 \% & 300 ms / GPU \\
LoDNN \cite{CaltagironeEtAl2016} & 94.07 \% & 92.03 \% & 92.81 \% & 95.37 \% & 4.07 \% & 4.63 \% & 18 ms / GPU \\
ChipNet \cite{8580596} & 94.05 \% & 88.29 \% & 93.57 \% & 94.53 \% & 3.58 \% & 5.47 \% & 12 ms / GPU \\
DEEP-DIG \cite{munozbulnesdeep2017} & 93.98 \% & 93.65 \% & 94.26 \% & 93.69 \% & 3.14 \% & 6.31 \% & 0.14 s / GPU \\
Up-Conv-Poly \cite{Oliveira2016IROS} & 93.83 \% & 90.47 \% & 94.00 \% & 93.67 \% & 3.29 \% & 6.33 \% & 0.08 s / GPU \\
OFA Net \cite{zhang2019one} & 93.74 \% & 85.37 \% & 90.36 \% & 97.38 \% & 5.72 \% & 2.62 \% & 0.04 s / GPU \\
DDN \cite{Mohan2014ARXIV} & 93.43 \% & 89.67 \% & 95.09 \% & 91.82 \% & 2.61 \% & 8.18 \% & 2 s / GPU \\
HID-LS \cite{GuZYK17} & 93.11 \% & 87.33 \% & 92.52 \% & 93.71 \% & 4.18 \% & 6.29 \% & 0.25 s / 1 cores \\
RGB36-Super \cite{CaltagironeEtAl2019} & 92.94 \% & 92.29 \% & 93.14 \% & 92.74 \% & 3.77 \% & 7.26 \% & 0.1 s / 1 core \\
Up-Conv \cite{Oliveira2016IROS} & 92.39 \% & 90.24 \% & 93.03 \% & 91.76 \% & 3.79 \% & 8.24 \% & 0.05 s / GPU \\
ALO-AVG-MM \cite{Reis2019IJCNN2019} & 92.03 \% & 85.64 \% & 90.65 \% & 93.45 \% & 5.31 \% & 6.55 \% & 0.0296 sec / \\
FTP \cite{Laddha2016IV} & 91.61 \% & 90.96 \% & 91.04 \% & 92.20 \% & 5.00 \% & 7.80 \% & 0.28 s / GPU \\
PT-ResNet \cite{fan2019ptresnet} & 91.61 \% & 92.75 \% & 91.60 \% & 91.61 \% & 4.63 \% & 8.39 \% & 0.3 s / GPU \\
HybridCRF \cite{XIAO2018HybridCRF} & 90.81 \% & 86.01 \% & 91.05 \% & 90.57 \% & 4.90 \% & 9.43 \% & 1.5 s / 1 core \\
FCN-LC \cite{Mendes2016ICRA} & 90.79 \% & 85.83 \% & 90.87 \% & 90.72 \% & 5.02 \% & 9.28 \% & 0.03 s / \\
LidarHisto \cite{7989159} & 90.67 \% & 84.79 \% & 93.06 \% & 88.41 \% & 3.63 \% & 11.59 \% & 0.1 s / 1 core \\
HIM \cite{Munoz2010ECCV} & 90.64 \% & 81.42 \% & 91.62 \% & 89.68 \% & 4.52 \% & 10.32 \% & 7 s / >8 cores \\
MixedCRF \cite{Han2017Road} & 90.59 \% & 84.24 \% & 89.11 \% & 92.13 \% & 6.20 \% & 7.87 \% & 6s / 1 core \\
BMCF \cite{wang2016multi} & 89.75 \% & 84.15 \% & 89.02 \% & 90.49 \% & 6.15 \% & 9.51 \% & 2.5 s / 1 core \\
NNP \cite{Chen2015NIPS} & 89.68 \% & 86.50 \% & 89.67 \% & 89.68 \% & 5.69 \% & 10.32 \% & 5 s / 4 cores \\
StixelNet \cite{Levi2015BMVC} & 89.12 \% & 81.23 \% & 85.80 \% & 92.71 \% & 8.45 \% & 7.29 \% & 1 s / GPU \\
CB \cite{Mendes2015ARXIV} & 88.97 \% & 79.69 \% & 89.50 \% & 88.44 \% & 5.71 \% & 11.56 \% & 2 s / 1 core \\
FusedCRF \cite{Xiao2015IV} & 88.25 \% & 79.24 \% & 83.62 \% & 93.44 \% & 10.08 \% & 6.56 \% & 2 s / 1 core \\
MAP \cite{Laddha2016IV} & 87.80 \% & 89.96 \% & 86.01 \% & 89.66 \% & 8.04 \% & 10.34 \% & 0.28s / \\
ProbBoost \cite{Vitor2014ICRAWORK} & 87.78 \% & 77.30 \% & 86.59 \% & 89.01 \% & 7.60 \% & 10.99 \% & 2.5 min / >8 cores \\
SPRAY \cite{Kuehnl2012ITSC} & 87.09 \% & 91.12 \% & 87.10 \% & 87.08 \% & 7.10 \% & 12.92 \% & 45 ms / \\
multi-task CNN \cite{Oeljeklaus18} & 86.81 \% & 82.15 \% & 78.26 \% & 97.47 \% & 14.92 \% & 2.53 \% & 25.1 ms / GPU \\
RES3D-Velo \cite{Shinzato2014IV} & 86.58 \% & 78.34 \% & 82.63 \% & 90.92 \% & 10.53 \% & 9.08 \% & 0.36 s / 1 core \\
GRES3D+VELO \cite{Shinzato2015} & 86.07 \% & 84.34 \% & 82.16 \% & 90.38 \% & 10.81 \% & 9.62 \% & 60 ms / 4 cores \\
PGM-ARS \cite{Passani15IV} & 85.69 \% & 73.83 \% & 82.34 \% & 89.33 \% & 10.56 \% & 10.67 \% & 0.05 s / i74700MQ \\
geo+gpr+crf \cite{doi10.11771729881417717058} & 85.56 \% & 74.21 \% & 82.81 \% & 88.50 \% & 10.12 \% & 11.50 \% & 30 s / 1 core \\
GRES3D+SELAS \cite{Shinzato2015} & 85.09 \% & 86.86 \% & 82.27 \% & 88.10 \% & 10.46 \% & 11.90 \% & 110 ms / 4 core \\
SCRFFPFHGSP \cite{Gheorghe2015} & 84.93 \% & 76.31 \% & 85.37 \% & 84.49 \% & 7.98 \% & 15.51 \% & 5 s / 8 cores \\
HistonBoost \cite{GioIV14} & 83.92 \% & 73.75 \% & 82.24 \% & 85.66 \% & 10.19 \% & 14.34 \% & 2.5 min / >8 cores \\
BM \cite{Wang2014IVWORK} & 83.47 \% & 72.23 \% & 75.90 \% & 92.72 \% & 16.22 \% & 7.28 \% & 2 s / 2 cores \\
SRF \cite{Xiao2016IJARS} & 82.44 \% & 87.37 \% & 80.60 \% & 84.36 \% & 11.18 \% & 15.64 \% & 0.2 s / 1 core \\
RES3D-Stereo \cite{Shinzato2014ITSC} & 81.08 \% & 81.68 \% & 78.14 \% & 84.24 \% & 12.98 \% & 15.76 \% & 0.7 s / 1 core \\
ARSL-AMI \cite{Passani2014ITSC} & 80.36 \% & 70.23 \% & 83.24 \% & 77.67 \% & 8.61 \% & 22.33 \% & 0.05 s / 4 cores \\
SPlane + BL \cite{Einecke2014IV} & 79.63 \% & 83.90 \% & 72.59 \% & 88.17 \% & 18.34 \% & 11.83 \% & 2 s / 1 core \\
CN \cite{Alvarez2012ECCV} & 79.02 \% & 78.80 \% & 76.64 \% & 81.55 \% & 13.69 \% & 18.45 \% & 2 s / 1 core \\
SPlane \cite{Einecke2014IV} & 78.69 \% & 77.16 \% & 71.96 \% & 86.80 \% & 18.63 \% & 13.20 \% & 2 s / 1 core \\
ANN \cite{Vitor2013IV} & 67.70 \% & 52.50 \% & 54.19 \% & 90.17 \% & 41.98 \% & 9.83 \% & 3 s / 1 core
\end{tabular}