TOWARDS LEARNING LOW-LIGHT INDOOR SEMANTIC SEGMENTATION WITH ILLUMINATION-INVARIANT FEATURES
Semantic segmentation models are often affected by illumination changes, and fail to predict correct labels. Although there has been a lot of research on indoor semantic segmentation, it has not been studied in low-light environments. In this paper we propose a new framework, LISU, for Low-light Ind...
Main Authors: | N. Zhang, F. Nex, N. Kerle, G. Vosselman |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2021-06-01
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Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2021/427/2021/isprs-archives-XLIII-B2-2021-427-2021.pdf |
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