3 accepted papers
For autonomous systems operating in degraded visual environments, robust 3D perception depends on accurate, metric-scale depth prediction. Yet, thermal depth completion—despite being well-suited for such conditions—remains relatively underexplored. Conventional depth completion approaches, which fus
In autonomous driving, thermal image semantic segmentation has emerged as a critical research area, owing to its ability to provide robust scene understanding under adverse visual conditions. In particular, unsupervised domain adaptation (UDA) for thermal image segmentation can be an efficient solut
Recently, transformers have been widely adopted for various computer vision tasks and show promising results due to their ability to encode long-range spatial dependencies in an image effectively. However, very few studies on adopting transformers in self-supervised depth estimation have been conduc