In modern computer-aided design (CAD), Boundary Representation (B-rep) is a widely used geometric modeling technique in industrial design and manufacturing. However, existing B-rep generation methods, which rely on tree-based hierarchies to represent and generate B-reps, fail to fully exploit the inherent graph structure of B-reps, resulting in suboptimal efficiency and model quality. To address this issue, we propose BRep-GD, a graph diffusion-based model specifically designed for B-rep generation. Unlike prior methods, BRep-GD treats B-reps as graphs, where nodes represent face elements, and edges represent boundary and vertex elements. By utilizing a continuous topological graph data structure, BRep-GD overcomes the challenges associated with directly applying graph diffusion models to B-rep generation. Specifically, BRep-GD introduces a graph diffusion method tailored to the features of CAD data, generating faces and edges sequentially. During edge generation, continuous topology decoupling is employed to avoid the need for global attention calculations, reducing computational complexity while ensuring geometric consistency and high-quality results. Experimental results demonstrate that BRep-GD outperforms existing state-of-the-art methods in both unconditional and class-conditional generation tasks, particularly in generating watertight solids and handling complex geometries. It significantly reduces isolated or inconsistent geometric components and improves generation efficiency.

Computer-Aided Design (CAD) generative modeling is driving significant innovations across industrial applications. Recent works have shown remarkable progress in creating solid models from various inputs such as point clouds, meshes, and text descriptions. However, these methods fundamentally diverge from traditional industrial workflows that begin with 2D engineering drawings. The automatic generation of parametric CAD models from these 2D vector drawings remains underexplored despite being a critical step in engineering design. To address this gap, our key insight is to reframe CAD generation as a sequence-to-sequence learning problem where vector drawing primitives directly inform the generation of parametric CAD operations, preserving geometric precision and design intent throughout the transformation process. We propose Drawing2CAD, a framework with three key technical components: a network-friendly vector primitive representation that preserves precise geometric information, a dual-decoder transformer architecture that decouples command type and parameter generation while maintaining precise correspondence, and a soft target distribution loss function accommodating inherent flexibility in CAD parameters. To train and evaluate Drawing2CAD, we create CAD-VGDrawing, a dataset of paired engineering drawings and parametric CAD models, and conduct thorough experiments to demonstrate the effectiveness of our method. Code and dataset are available at https://github.com/lllssc/Drawing2CAD.

Performing 3-dimensional computer-aided design (3D-CAD) model classification , retrieval, and reuse is of vital importance in industrial manufacturing, as it considerably shortens the engineering development cycle and reduces development costs. Although existing 3D model classification and retrieval methods achieve satisfactory performance when operating on meshes or point clouds, they cannot be applied directly to 3D-CAD models, which are generally represented by the boundary representation (B-rep). To address this issue, and to fully exploit the topology of B-rep, a graph structure descriptor called B-rep graph is proposed to pre-process B-rep data, and to extract the precise topological and geometric features from 3D-CAD models. Meanwhile, a novel efficient neural network called FuS-GCN, based on graph convolutional networks (GCNs), is designed to handle this graph data. To better extract the graph features and to improve the effect of pooling, the self-attention mechanism and feature fusion are incorporated into the pooling layer, yielding the proposed fusion self-attention graph pooling (FuSPool) algorithm. Finally, we demonstrate the effectiveness of FuS-GCN on 3D-CAD model data, while outperforming alternative 3D shape descriptors such as point clouds, voxels, and meshes.