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

Problem
This paper addresses the gap in unified frameworks for three-dimensional (3D) reconstruction techniques specifically within the manufacturing domain. Despite the proliferation of both traditional and deep learning methods, there is a lack of comprehensive reviews that systematically categorize and analyze these techniques. The authors highlight the need for a structured understanding of current capabilities and future research opportunities, particularly as existing literature does not adequately cover the integration of various methods and their applications in manufacturing. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors conducted a systematic review of 106 recent publications, classifying 3D reconstruction techniques into three primary categories: data acquisition, point cloud generation, and post-processing. They examined both non-contact methods, such as structured light scanning and stereo vision, and emerging deep learning approaches. The review emphasizes the integration of deep learning to enhance reconstruction accuracy and processing speed, particularly in feature extraction and matching. The paper also discusses the trend towards hybrid systems that combine multiple sensor types and processing methods to address the limitations of individual techniques.

Results
The review reveals that non-contact methods are prevalent in manufacturing, with 47% of applications focusing on quality inspection. The authors report that current technologies achieve sub-millimeter accuracy in controlled environments. Key application areas identified include design and development (13%), machining (8%), process (17%), assembly (22%), and quality inspection (40%). The integration of deep learning has been shown to significantly improve both accuracy and processing speed, although specific quantitative results comparing these methods to traditional approaches are not provided in the abstract.

Limitations
The authors acknowledge several limitations, including the challenges of handling reflective surfaces and dynamic environments, which can adversely affect reconstruction accuracy. They also note that while the review provides a comprehensive overview, it may not capture all emerging techniques or applications due to the rapid evolution of the field. Additionally, the lack of quantitative benchmarks in the review limits the ability to assess the performance of various methods against each other.

Why it matters
This review is significant for researchers and engineers in the manufacturing sector as it consolidates existing knowledge and identifies critical research opportunities in 3D reconstruction. By highlighting the integration of deep learning with traditional methods, the paper encourages further exploration of hybrid systems that could enhance reconstruction capabilities. The structured framework provided can serve as a foundation for future studies aimed at overcoming current limitations and advancing the state of the art in manufacturing-focused 3D reconstruction.

Authors: Chialoon Cheng, Kaijun Liu, Zhiyang Liu, Marcelo H Ang
Source: arXiv:2604.28064
URL: https://arxiv.org/abs/2604.28064v1