Image2CADSeq: Computer-Aided Design Sequence and Knowledge Inference from Product Images

TL;DR

This paper presents Image2CADSeq, a neural network model that reconstructs CAD sequences from images, enabling reverse engineering of 3D models without needing 3D data, thus offering more flexible and insightful design process understanding.

Contribution

Introduction of a novel neural network approach, Image2CADSeq, for generating CAD sequences from images, facilitating reverse engineering and deeper insight into CAD model construction.

Findings

Model achieves promising results in generating CAD sequences from images.

Multi-level evaluation framework effectively assesses model performance.

Experimental results demonstrate the potential of the approach for practical applications.

Abstract

Computer-aided design (CAD) tools empower designers to design and modify 3D models through a series of CAD operations, commonly referred to as a CAD sequence. In scenarios where digital CAD files are not accessible, reverse engineering (RE) has been used to reconstruct 3D CAD models. Recent advances have seen the rise of data-driven approaches for RE, with a primary focus on converting 3D data, such as point clouds, into 3D models in boundary representation (B-rep) format. However, obtaining 3D data poses significant challenges, and B-rep models do not reveal knowledge about the 3D modeling process of designs. To this end, our research introduces a novel data-driven approach with an Image2CADSeq neural network model. This model aims to reverse engineer CAD models by processing images as input and generating CAD sequences. These sequences can then be translated into B-rep models using a solid modeling kernel. Unlike B-rep models, CAD sequences offer enhanced flexibility to modify individual steps of model creation, providing a deeper understanding of the construction process of CAD models. To quantitatively and rigorously evaluate the predictive performance of the Image2CADSeq model, we have developed a multi-level evaluation framework for model assessment. The model was trained on a specially synthesized dataset, and various network architectures were explored to optimize the performance. The experimental and validation results show great potential for the model in generating CAD sequences from 2D image data.