Stitch-A-Shape: Bottom-up Learning for B-Rep Generation

Boundary representation (B-Rep) models serve as the primary representation format in modern CAD systems for describing 3D shapes. While deep learning has achieved success with various geometric representations, B-Reps remain challenging due to their hybrid nature of combining continuous geometry with discrete topological relationships. In this paper, we present Stitch-A-Shape, a B-Rep generation framework that directly models both topology and geometry. This strategy departs from prior work that focuses on either topology or geometry while recovering the other through post-processing. Our method consists of a geometry module that determines the spatial configuration of geometric elements (vertices, curves, and surface control points) and a topology module that establishes connectivity relationships and identifies boundary structures, including outer and inner loops. Our approach leverages a sequential "stitching" representation that mirrors the native data structure and inherent bottom-up organization of B-Rep, assembling geometric entities from vertices through curves to faces. We validate that our framework can handle topological and geometric ambiguities, as well as open surfaces and compound solids. Experiments show that Stitch-A-Shape achieves superior generation quality and computational efficiency compared to existing approaches in unconditional generation tasks, while exhibiting effective capabilities in class-conditional generation and B-Rep autocompletion applications.