3D Reconstruction Techniques in the Manufacturing Domain: Applications, Research Opportunities and Use Cases

arXiv cs.CV / 5/1/2026

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Key Points

The article is a comprehensive arXiv review (106 papers) of 3D reconstruction techniques used in manufacturing, covering both traditional pipelines and emerging deep learning methods.
It proposes a classification of methods into three stages—data acquisition, point cloud generation, and post-processing/application—while highlighting a key research gap: unified 3D reconstruction frameworks.
Non-contact sensing dominates manufacturing use, especially structured light scanning and stereo vision, and nearly half of applications focus on quality inspection.
Deep learning is reported to improve reconstruction accuracy and speed, particularly for feature extraction and matching within the processing pipeline.
Despite sub-millimeter accuracy in controlled settings, the review identifies open challenges such as reflective surfaces and dynamic scenes, pointing to hybrid multi-sensor systems as a likely direction.

Abstract

This comprehensive review examines the evolution and the current state of the art in three-dimensional (3D) reconstruction techniques in manufacturing applications. The analysis covers both traditional approaches and emerging deep learning methods, showing a critical research gap in unified 3d reconstruction frameworks. Through systematic review of 106 recent publications, we classify reconstruction techniques into three primary categories: data acquisition, point cloud generation, post-processing and applications. Non-contact methods, particularly structured light scanning and stereo vision, have shown significant adoption in manufacturing, with 47% of surveyed applications focusing on quality inspection. The integration of deep learning has enhanced reconstruction accuracy and processing speed, particularly in feature extraction and matching. Key applications span design and development (13%), machining (8%), process (17%), assembly (22%), and quality inspection (40%). While current technologies achieve sub-millimeter accuracy in controlled environments, challenges persist in handling reflective surfaces and dynamic environments. Our findings indicate a trend toward hybrid systems combining multiple sensor types and processing methods to overcome individual limitations. This survey provides a structured framework for understanding current capabilities and future directions in manufacturing-focused 3D reconstruction.