Implicit Shape

Implicit shape modeling uses neural networks to represent 3D shapes implicitly as functions, often signed distance functions (SDFs), rather than explicitly as meshes or point clouds. Current research focuses on improving the accuracy and efficiency of these representations, particularly through advancements in neural network architectures like multi-scale networks, adversarial training methods, and part-based models that decompose complex shapes into simpler components. This field is significant because it enables efficient 3D shape reconstruction, manipulation, and simulation from limited data, with applications ranging from robotics and computer graphics to medical image analysis and scientific visualization.