\begin{tabular}{c | c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf IoU class} & {\bf iIoU class} & {\bf IoU category} & {\bf iIoU category} & {\bf Runtime} & {\bf Environment}\\ \hline
VideoProp-LabelRelax & & 72.82 \% & 48.68 \% & 88.99 \% & 75.26 \% & n s / GPU & B. Yi Zhu*: Improving Semantic Segmentation via Video Propagation and Label Relaxation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
D-Seg & & 72.69 \% & 48.28 \% & 88.62 \% & 74.78 \% & 0.8 s / 1 core & \\
Seg\_LD & & 70.85 \% & 47.21 \% & 85.47 \% & 72.39 \% & 0.02 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
DL-seg & & 63.90 \% & 32.53 \% & 83.50 \% & 60.35 \% & 0.2 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SN\_RN152pyrx8\_RVC & & 63.89 \% & 31.68 \% & 84.39 \% & 63.23 \% & 1 s / & M. Orsic, I. Kreso, P. Bevandic and S. Segvic: In defense of pre-trained imagenet architectures for real-time semantic segmentation of road-driving images. Proceedings of the IEEE conference on computer vision and pattern recognition 2019.\\
MSeg1080\_RVC & & 62.64 \% & 31.62 \% & 86.59 \% & 68.05 \% & 0.49 s / 1 core & J. Lambert, Z. Liu, O. Sener, J. Hays and V. Koltun: MSeg: A Composite Dataset for Multi- domain Semantic Segmentation. Computer Vision and Pattern Recognition (CVPR) 2020.\\
SJTU\_HHW & & 60.53 \% & 28.21 \% & 84.64 \% & 60.45 \% & n s / GPU & \\
Chroma UDA & & 60.36 \% & 31.70 \% & 80.73 \% & 61.91 \% & 0.4 s / GPU & O. Erkent and C. Laugier: Semantic Segmentation with Unsupervised Domain Adaptation Under Varying Weather Conditions for Autonomous Vehicles. IEEE Robotics and Automation Letters 2020.\\
mdv3+ & & 59.86 \% & 25.87 \% & 82.96 \% & 56.29 \% & 0.2 s / GPU & \\
IfN-DomAdap-Seg & & 59.50 \% & 30.28 \% & 81.57 \% & 61.91 \% & 1 s / GPU & J. Bolte, M. Kamp, A. Breuer, S. Homoceanu, P. Schlicht, F. Hüger, D. Lipinski and T. Fingscheidt: Unsupervised Domain Adaptation to Improve Image Segmentation Quality Both in the Source and Target Domain. Proc. of CVPR - Workshops 2019.\\
SegStereo & & 59.10 \% & 28.00 \% & 81.31 \% & 60.26 \% & 0.6 s / & G. Yang, H. Zhao, J. Shi, Z. Deng and J. Jia: SegStereo: Exploiting Semantic Information for Disparity Estimation. ECCV 2018.\\
SGDepth & & 53.04 \% & 24.36 \% & 78.65 \% & 55.95 \% & 0.1 s / GPU & M. Klingner, J. Termöhlen, J. Mikolajczyk and T. Fingscheidt: Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance. ECCV 2020.\\
SDNet & & 51.14 \% & 17.74 \% & 79.62 \% & 50.45 \% & 0.2 s / GPU & M. Ochs, A. Kretz and R. Mester: SDNet: Semantic Guided Depth Estimation Network. German Conference on Pattern Recognition (GCPR) 2019.\\
APMoE\_seg\_ROB & & 47.96 \% & 17.86 \% & 78.11 \% & 49.17 \% & 0.2 s / GPU & S. Kong and C. Fowlkes: Pixel-wise Attentional Gating for Parsimonious Pixel Labeling. arxiv 1805.01556 2018.\\
SPSSN & & 41.29 \% & 14.69 \% & 71.91 \% & 43.96 \% & .001 s / GPU &
\end{tabular}