制造领域中的3D重建技术：应用、研究机会与用例
3D Reconstruction Techniques in the Manufacturing Domain: Applications, Research Opportunities and Use Cases
AI总结：
本文综述了制造领域3D重建技术的发展现状，分析了传统方法与深度学习方法的差异，指出统一框架的不足，并总结了质量检测等关键应用及未来研究方向。
摘要：
本文全面回顾了制造领域3D重建技术的发展历程和当前技术现状。分析涵盖了传统方法与新兴深度学习方法，揭示了统一3D重建框架中的关键研究缺口。通过系统回顾106篇近期文献，将重建技术分为三大类别：数据采集、点云生成、后处理与应用。非接触方法，特别是结构光扫描和立体视觉，在制造领域显示出显著的应用，其中47%的调研应用集中在质量检测。深度学习的集成提高了重建精度和处理速度，特别是在特征提取和匹配方面。关键应用涵盖设计与开发（13%）、加工（8%）、工艺（17%）、装配（22%）和质量检测（40%）。尽管当前技术在受控环境中可实现亚毫米精度，但在处理反光表面和动态环境方面仍存在挑战。我们的发现表明，趋势朝着结合多种传感器类型和处理方法的混合系统发展，以克服单一限制。本文提供了理解当前能力和未来方向的结构化框架。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
