Shape Encoding

Shape encoding focuses on representing 3D shapes in a computationally efficient and informative manner, aiming to capture essential geometric features while minimizing data redundancy. Current research emphasizes learning-based approaches, utilizing architectures like autoencoders, diffusion models, and implicit neural representations (e.g., occupancy fields and octrees) to achieve robust and compact shape encodings. These advancements are impacting diverse fields, including medical image analysis (e.g., fracture detection), computer-aided design (e.g., generating user-defined shapes), and computer vision (e.g., object recognition and retrieval), by enabling more efficient and accurate processing of 3D data.