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

arXiv: https://arxiv.org/abs/2604.28064 · HTML: https://mlsanity.com/paper/2604.28064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 199 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Atlas: End-to-End 3D Scene Reconstruction from Posed Images](https://mlsanity.com/paper/2003.10432/) (2020)
- [Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects](https://mlsanity.com/paper/2110.14217/) (2021)
- [Grasp Pose Detection in Point Clouds](https://mlsanity.com/paper/1706.09911/) (2017)
- [Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter](https://mlsanity.com/paper/2101.01132/) (2021)
- [Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review](https://mlsanity.com/paper/2405.03417/) (2024)
- [Point Cloud Completion by Learning Shape Priors](https://mlsanity.com/paper/2008.00394/) (2020)
- [RGBGrasp: Image-based Object Grasping by Capturing Multiple Views during Robot Arm Movement with Neural Radiance Fields](https://mlsanity.com/paper/2311.16592/) (2023)
- [U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation](https://mlsanity.com/paper/2004.03466/) (2020)
- [CFVS: Coarse-to-Fine Visual Servoing for 6-DoF Object-Agnostic Peg-In-Hole Assembly](https://mlsanity.com/paper/2209.08864/) (2022)
- [A comparison of extended object tracking with multi-modal sensors in indoor environment](https://mlsanity.com/paper/2411.18476/) (2024)

## Abstract (arXiv)

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.