Shape Representation

Shape representation in computer vision and graphics aims to efficiently and accurately capture the geometry of 3D objects for various tasks like rendering, analysis, and generation. Current research emphasizes learning-based approaches, employing neural networks to represent shapes implicitly (e.g., using signed distance functions or neural fields) or explicitly (e.g., through meshes or point clouds), often incorporating techniques like Fourier descriptors or geometric operators to enhance feature extraction. These advancements are driving progress in diverse applications, including medical image analysis, robotics, and digital content creation, by enabling more robust and efficient processing of complex 3D data.