Semantic Scene Segmentation

Semantic scene segmentation aims to assign semantic labels (e.g., car, pedestrian, building) to each pixel in an image or point cloud, enabling computers to understand the content of a scene. Current research focuses on improving accuracy and robustness in challenging conditions (e.g., low light, rain) through techniques like sensor fusion (camera-radar, RGB-D), self-supervised pre-training, and novel deep learning architectures designed for efficiency and fairness. These advancements are crucial for applications such as autonomous driving, robotics, and medical image analysis, where reliable scene understanding is paramount for safe and effective operation.