Surface Representation

Surface representation in 3D computer vision and graphics focuses on efficiently and accurately modeling the shapes of objects, enabling tasks like object detection, pose estimation, and reconstruction from images or point clouds. Current research emphasizes learning-based approaches, employing neural networks (e.g., implicit surface representations like signed distance functions and neural radiance fields, explicit representations like meshes and deformation fields) to capture complex geometries and handle various data types (RGB images, point clouds, depth maps). These advancements are significantly impacting fields like medical imaging (e.g., anatomical shape modeling), robotics (interaction transfer), and computer-aided design, improving the accuracy and efficiency of 3D modeling and analysis.