Work

Multiview Reconstruction of Parametric CAD Models

Year: 2025

Type: article

Abstract: ABSTRACT Computer‐aided design (CAD) serves as an essential and irreplaceable tool for engineers and designers, optimising design workflows and driving innovation across diverse industries. Nevertheless, mastering these sophisticated CAD programmes requires substantial training and expertise from practitioners. To address these challenges, this paper introduces a framework for reconstructing CAD models from multiview. Specifically, we present a novel end‐to‐end neural network capable of directly reconstructing parametric CAD command sequences from multiview. Subsequently, the proposed network addresses the low‐rank bottleneck inherent in traditional attention mechanisms of neural networks. Finally, we present a novel parametric CAD dataset that incorporates multiview for corresponding CAD sequences while eliminating redundant data. Comparative experiments reveal that the proposed framework effectively reconstructs high‐quality parametric CAD models, which are readily editable in collaborative CAD/CAM environments.

Source: IET Collaborative Intelligent Manufacturing

DOI: 10.1049/cim2.70037

Authors: Rubin Fan, Yi Zhang, Fazhi He

Institution: Wuhan University

Language: English

Topic: 3D Shape Modeling and Analysis

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,520

Funder: National Natural Science Foundation of China