AutoRegressive Generation with B-rep Holistic Token Sequence Representation

TL;DR

This paper introduces BrepARG, a novel sequence-based autoregressive model that encodes boundary representations (B-rep) into holistic token sequences, enabling improved generation of geometric models.

Contribution

BrepARG is the first to encode B-rep geometry and topology into a hierarchical token sequence for autoregressive generation using transformer architectures.

Findings

BrepARG achieves state-of-the-art performance in B-rep generation.

The holistic token sequence encoding effectively captures B-rep geometry and topology.

Experiments validate the feasibility of sequence-based B-rep representation.

Abstract

Previous representation and generation approaches for the B-rep relied on graph-based representations that disentangle geometric and topological features through decoupled computational pipelines, thereby precluding the application of sequence-based generative frameworks, such as transformer architectures that have demonstrated remarkable performance. In this paper, we propose BrepARG, the first attempt to encode B-rep's geometry and topology into a holistic token sequence representation, enabling sequence-based B-rep generation with an autoregressive architecture. Specifically, BrepARG encodes B-rep into 3 types of tokens: geometry and position tokens representing geometric features, and face index tokens representing topology. Then the holistic token sequence is constructed hierarchically, starting with constructing the geometry blocks (i.e., faces and edges) using the above tokens, followed by geometry block sequencing. Finally, we assemble the holistic sequence representation for the entire B-rep. We also construct a transformer-based autoregressive model that learns the distribution over holistic token sequences via next-token prediction, using a multi-layer decoder-only architecture with causal masking. Experiments demonstrate that BrepARG achieves state-of-the-art (SOTA) performance. BrepARG validates the feasibility of representing B-rep as holistic token sequences, opening new directions for B-rep generation.