CVGRNov 27, 2025

BrepGPT: Autoregressive B-rep Generation with Voronoi Half-Patch

arXiv:2511.22171v16 citations
Originality Incremental advance
AI Analysis

This work addresses the challenge of efficient and accurate CAD model generation for industrial design, though it appears incremental as it builds on existing autoregressive and VQ-VAE methods.

The authors tackled the problem of generating boundary representation (B-rep) CAD models, which are complex due to coupled geometric and topological elements, by introducing BrepGPT, a single-stage autoregressive framework that achieves state-of-the-art performance in unconditional generation and demonstrates versatility in conditional tasks.

Boundary representation (B-rep) is the de facto standard for CAD model representation in modern industrial design. The intricate coupling between geometric and topological elements in B-rep structures has forced existing generative methods to rely on cascaded multi-stage networks, resulting in error accumulation and computational inefficiency. We present BrepGPT, a single-stage autoregressive framework for B-rep generation. Our key innovation lies in the Voronoi Half-Patch (VHP) representation, which decomposes B-reps into unified local units by assigning geometry to nearest half-edges and sampling their next pointers. Unlike hierarchical representations that require multiple distinct encodings for different structural levels, our VHP representation facilitates unifying geometric attributes and topological relations in a single, coherent format. We further leverage dual VQ-VAEs to encode both vertex topology and Voronoi Half-Patches into vertex-based tokens, achieving a more compact sequential encoding. A decoder-only Transformer is then trained to autoregressively predict these tokens, which are subsequently mapped to vertex-based features and decoded into complete B-rep models. Experiments demonstrate that BrepGPT achieves state-of-the-art performance in unconditional B-rep generation. The framework also exhibits versatility in various applications, including conditional generation from category labels, point clouds, text descriptions, and images, as well as B-rep autocompletion and interpolation.

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