HiFi-BRep: High-Fidelity Latent Representation for Robust B-Rep Generation

Boundary representation (B-rep) generation is a fundamental task in Computer-Aided Design (CAD), enabling automated modeling of 3D geometries. However, the direct synthesis of valid and high-quality B-reps remains a major challenge.Existing deep generative methods suffer from brittle representation and generation paradigms, due to: (1) representation noise from padding variable-length sequences and feature contamination between distant primitives, and (2) fragile generation pipelines marked by cascaded decoding error propagation and a train-inference mismatch from deferred validity enforcement.To address this, we propose HiFi-Brep. Our core insight is that robust, high-validity generation requires: first, building upon a compact and high-fidelity latent representation; and second, reformulating validity constraints as differentiable inductive biases within a single-stage generation process, enabling mutual guidance between geometry and topology.We implement this through a topology-aware encoder that yields a high-fidelity latent representation by eliminating padding noise via query-based pooling and preventing feature contamination with topology-guided attention. Our single-stage decoder then jointly generates geometry and topology, embedding core manifold constraints into a differentiable learning objective to ensure topological validity and sidestep cascaded errors. The resulting latent space supports both unconditional synthesis and conditional generation from various inputs, such as class labels, point clouds, or images.Experiments demonstrate that HiFi-Brep outperforms state-of-the-art approaches in both model validity and geometric quality.