Surface Segmentation

Surface segmentation, the process of partitioning a surface into meaningful regions, is a crucial task across diverse fields, aiming to accurately delineate boundaries and extract relevant features. Current research emphasizes the development of robust and generalizable deep learning models, including convolutional neural networks (CNNs) and vision transformers, often incorporating attention mechanisms and multi-path architectures to improve accuracy and efficiency, particularly in handling complex or noisy data. These advancements are driving progress in applications ranging from medical image analysis (e.g., retinal disease diagnosis and brain surface mapping) to material science (e.g., fracture surface analysis) and robotics (e.g., 3D object assembly). The ultimate goal is to create reliable and efficient segmentation methods applicable to a wide range of surface types and data modalities.