View Segmentation

View segmentation aims to accurately partition images or 3D scenes into meaningful regions from multiple perspectives, overcoming challenges like occlusion and viewpoint variations. Current research heavily utilizes neural radiance fields (NeRFs) and deep learning architectures, often incorporating techniques like graph cuts, transformers, and the Segment Anything Model (SAM) to improve segmentation accuracy and efficiency, particularly in scenarios with limited labeled data. These advancements are crucial for applications ranging from autonomous driving (3D point cloud segmentation) to medical image analysis (wound segmentation) and 3D scene editing, enabling more robust and detailed scene understanding.