\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
WRP & & 76.44 \% & 50.92 \% & 89.63 \% & 73.69 \% & 1 s / GPU & A. Ganeshan, A. Vallet, Y. Kudo, S. Maeda, T. Kerola, R. Ambrus, D. Park and A. Gaidon: Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle- consistency. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.\\
UJS\_model & & 75.11 \% & 47.71 \% & 89.53 \% & 75.75 \% & 0.26 s / 1 core & L. Yingfeng Cai and Z. Hai Wang: Multi-Target Pan-Class Intrinsic Relevance Driven Model for Improving Semantic Segmentation in Autonomous Driving. IEEE Transactions on Image Processing (TIP) 2021.\\
RoadFormer+ & & 73.13 \% & 45.88 \% & 88.75 \% & 73.46 \% & 0.04 s / 1 core & \\
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.\\
SN\_DN161\_fat\_pyrx8 & & 68.89 \% & 40.45 \% & 87.06 \% & 67.93 \% & 1 s / & P. Bevandić, M. Oršić, I. Grubišić, J. Šarić and S. Šegvić: Multi-domain semantic segmentation with overlapping labels. WACV 2022.\\
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.\\
DANet+SAM & & 62.51 \% & 31.48 \% & 83.29 \% & 58.91 \% & 2 s / 1 core & \\
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.\\
DANet & & 59.63 \% & 28.56 \% & 80.18 \% & 55.62 \% & 0.05 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.\\
MCANet & & 58.52 \% & 24.00 \% & 83.04 \% & 54.06 \% & 0.003 s / & T. Singha, D. Pham and A. Krishna: Multi-encoder Context Aggregation Network for Structured and Unstructured Urban Street Scene Analysis. IEEE Access 2023.\\
SDBNetV2 & & 56.77 \% & 23.11 \% & 81.08 \% & 50.77 \% & 0.004 s / & T. Singha, D. Pham and A. Krishna: Improved Short-term Dense Bottleneck network for efficient scene analysis. Computer Vision and Image Understanding 2023.\\
EPNet & & 54.65 \% & 23.82 \% & 79.34 \% & 51.07 \% & 0.03 s / & \\
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.\\
SDBNet & & 51.80 \% & 18.72 \% & 78.00 \% & 44.46 \% & 0.01 s / & T. Singha, D. Pham and A. Krishna: SDBNet: Lightweight Real-Time Semantic Segmentation Using Short-Term Dense Bottleneck. Proc. DICTA 2022.\\
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.\\
SFRSeg & & 49.27 \% & 17.43 \% & 77.91 \% & 46.88 \% & 0.005 s / & T. Singha, D. Pham and A. Krishna: A real-time semantic segmentation model using iteratively shared features in multiple sub- encoders. Pattern Recognition 2023.\\
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.\\
LIISIESS & & 46.73 \% & 19.82 \% & 76.04 \% & 49.91 \% & NA s / 1 core & L. Sun, J. Bockman and S. Changming: A Framework for Leveraging Inter-image Information in Stereo Images for Enhanced Semantic Segmentation. IEEE Transactions on Instrumentation and Measurement 2023.
\end{tabular}