Year: 2025
Type: conference-paper
Abstract: This paper presents a multimodal deep learning approach for precise 3D reconstruction of mechanical parts from images and textual descriptions, offering a cost-effective alternative to traditional methods. By combining high-resolution, multi-angle images with technical text data, the model generates accurate 3D representations. A ResNet-based CNN extracts visual features, while BERT encodes textual descriptions, with a depth estimation module enhancing spatial accuracy. The features are fused to produce a 3D point cloud and mesh. The results demonstrate good performance in capturing the overall shape of the mechanical parts; however, further improvements are needed to enhance the precision of the metric parameters.
Source: MATEC Web of Conferences
Institutions: Tunis University, National Engineering School of Tunis, ENSIT
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414744817