Multi Modal Semantic Segmentation

Multi-modal semantic segmentation aims to improve scene understanding by combining information from multiple data sources, such as RGB images, depth maps, point clouds, and thermal imagery, to generate accurate pixel-wise semantic labels. Current research emphasizes efficient fusion techniques within various network architectures, including Siamese networks and Transformers, often incorporating semi-supervised learning to address the limitations of labeled data. This field is crucial for advancing applications like autonomous driving and remote sensing, where robust and reliable scene interpretation in challenging conditions is paramount.