Shape Abstraction

Shape abstraction aims to simplify complex 3D shapes into more manageable representations, typically using basic geometric primitives like cuboids or superquadrics, while preserving essential features. Current research focuses on developing deep learning models, including neural radiance fields and transformer architectures, often combined with differentiable rendering and optimization techniques, to learn these abstractions from various input modalities such as images and point clouds. This field is significant for its potential to improve efficiency in tasks like scene understanding, robotics, and 3D modeling by providing compact and interpretable shape representations. The development of robust and generalizable methods for shape abstraction remains a key challenge.