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

• URL: http://arxiv.org/abs/2604.28064v1
• Date: Thu, 30 Apr 2026 16:11:02 GMT
• Title: 3D Reconstruction Techniques in the Manufacturing Domain: Applications, Research Opportunities and Use Cases
• Abstract summary: This review examines the evolution and the current state of the art in three-dimensional (3D) reconstruction techniques in manufacturing applications.<n>Non-contact methods, particularly structured light scanning and stereo vision, have shown significant adoption in manufacturing.<n>The integration of deep learning has enhanced reconstruction accuracy and processing speed, particularly in feature extraction and matching.
• Score: 7.501305546365941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Hybrid Machine Learning Framework for Predicting Geometric Deviations from 3D Surface Metrology [0.46085106405479537]
  We present a methodology that employs a high-resolution 3D scanner to acquire multi-angle surface data from 237 components produced across different batches.<n>The data were processed through precise alignment, noise reduction, and merging techniques to generate accurate 3D representations.<n>The proposed system achieved a prediction accuracy of 0.012 mm at a 95% confidence level, representing a 73% improvement over conventional statistical process control methods.
  arXiv  Detail & Related papers  (2025-08-09T05:59:35Z)
• Advances in Feed-Forward 3D Reconstruction and View Synthesis: A Survey [171.72616707259306]
  3D reconstruction and view synthesis are foundational problems in computer vision, graphics, and immersive technologies such as augmented reality (AR), virtual reality (VR), and digital twins.<n>Recent advances in feed-forward approaches, driven by deep learning, have revolutionized this field by enabling fast and generalizable 3D reconstruction and view synthesis.
  arXiv  Detail & Related papers  (2025-07-19T06:13:25Z)
• AI-powered Contextual 3D Environment Generation: A Systematic Review [49.1574468325115]
  This study performs a systematic review of existing generative AI techniques for 3D scene generation.<n>By examining state-of-the-art approaches, it presents key challenges such as scene authenticity and the influence of textual inputs.
  arXiv  Detail & Related papers  (2025-06-05T15:56:28Z)
• Marker-Based Extrinsic Calibration Method for Accurate Multi-Camera 3D Reconstruction [0.23749905164931198]
  In this paper, we introduce an iterative extrinsic calibration method that leverages the geometric constraints provided by a three-dimensional marker.<n>We validate our method comprehensively in both controlled environments and practical real-world settings within the Tech4Diet project.<n> Experimental results demonstrate substantial reductions in alignment errors, facilitating accurate and reliable 3D reconstructions.
  arXiv  Detail & Related papers  (2025-05-05T10:21:41Z)
• A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond [15.48852238904361]
  Plant phenotyping plays a pivotal role in understanding plant traits and their interactions with the environment.<n>3D reconstruction technologies have emerged as powerful tools for capturing detailed plant morphology and structure.<n>This paper provides a review of the 3D reconstruction techniques for plant phenotyping.
  arXiv  Detail & Related papers  (2025-04-30T02:04:23Z)
• A 3D Facial Reconstruction Evaluation Methodology: Comparing Smartphone Scans with Deep Learning Based Methods Using Geometry and Morphometry Criteria [60.865754842465684]
  Three-dimensional (3D) facial shape analysis has gained interest due to its potential clinical applications.<n>High cost of advanced 3D facial acquisition systems limits their widespread use, driving the development of low-cost acquisition and reconstruction methods.<n>This study introduces a novel evaluation methodology that goes beyond traditional geometry-based benchmarks by integrating morphometric shape analysis techniques.
  arXiv  Detail & Related papers  (2025-02-13T15:47:45Z)
• Human as Points: Explicit Point-based 3D Human Reconstruction from Single-view RGB Images [71.91424164693422]
  We introduce an explicit point-based human reconstruction framework called HaP.<n>Our approach is featured by fully-explicit point cloud estimation, manipulation, generation, and refinement in the 3D geometric space.<n>Our results may indicate a paradigm rollback to the fully-explicit and geometry-centric algorithm design.
  arXiv  Detail & Related papers  (2023-11-06T05:52:29Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)
• Predictive modeling approaches in laser-based material processing [59.04160452043105]
  This study aims to automate and forecast the effect of laser processing on material structures. The focus is centred on the performance of representative statistical and machine learning algorithms. Results can set the basis for a systematic methodology towards reducing material design, testing and production cost.
  arXiv  Detail & Related papers  (2020-06-13T17:28:52Z)
• Data-driven surrogate modelling and benchmarking for process equipment [1.8395181176356432]
  A suite of computational fluid dynamics (CFD) simulations geared toward chemical process equipment modeling has been developed. Various regression-based active learning strategies are explored with these CFD simulators in-the-loop under the constraints of a limited function evaluation budget.
  arXiv  Detail & Related papers  (2020-03-13T18:22:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.