An Overview of Methods and Applications of 3D Reconstruction
DOI:
https://doi.org/10.62051/ijcsit.v3n1.03Keywords:
3D reconstruction, Deep learning, Computer visionAbstract
With the development of hardware equipment and theoretical knowledge, 3D reconstruction technology has played a key role in many fields such as industrial manufacturing, cultural relics protection and augmented reality, and has attracted great attention in the field of computer vision. 3D reconstruction methods can be divided into traditional methods and deep learning methods. Among them, the traditional method technology is mature and widely used at present, while the 3D reconstruction method based on deep learning is developing rapidly, which has the advantages of low dependence on equipment, strong method generalization and strong adaptability to the environment. In order to promote the development of subsequent research, this paper first gives a comprehensive classification and review of the existing 3D reconstruction methods, then analyzes their principles and performance, and finally further analyzes the existing problems and challenges, and explores possible future research directions, with the aim of providing new ideas for more in depth 3D reconstruction tasks.
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Zollhöfer M, Stotko P, Görlitz A, et al. State of the art on 3D reconstruction with RGB-D cameras [C]. Computer graphics forum. 2018, 37(2): 625-652.
Zhang R, Tsai P S, Cryer J E, et al. Shape-from-shading: a survey [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(8): 690-706.
Rosenberger P, Cosgun A, Newbury R, et al. Object-independent human-to-robot handovers using real time robotic vision [J]. IEEE Robotics and Automation Letters, 2020, 6(1): 17-23.
LeCun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521(7553): 436-444.
Voulodimos A, Doulamis N, Doulamis A, et al. Deep learning for computer vision: a brief review [J]. Computational Intelligence and Neuroscience, 2018.
Zollhöfer M, Stotko P, Görlitz A, et al. State of the art on 3D reconstruction with RGB-D cameras [J]. Computer Graphics Forum, 2018, 37(2): 625-652.
Roberts L G. Machine perception of three-dimensional solids [D]. Massachusetts Institute of Technology, 1963.
Horn B K P. Shape from shading: a method for obtaining the shape of a smooth opaque object from one view [J]. 1970.
Kiyasu S, Hoshino H, Yano K, et al. Measurement of the 3-D shape of specular polyhedrons using an M-array coded light source [J]. IEEE Transactions on Instrumentation and Measurement, 1995, 44(3): 775-778.
Daniel P, Durou J D. Creation of real images which are valid for the assumptions made in shape from shading [C]. Proceedings 10th International Conference on Image Analysis and Processing. IEEE, 1999: 418-423.
Snavely N, Seitz S M, Szeliski R. Photo tourism: exploring photo collections in 3D [J]. ACM Transactions on Graphics, 2006: 835-846.
Han J G, Shao L, Xu D, Shotton J. Enhanced computer vision with Microsoft Kinect sensor: a review [J]. IEEE Transactions on Cybernetics, 2013, 43(5): 1318−1334.
Lowe D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision, 2004, 60: 91-110.
Tiwari K K. Formulation of a n-degree polynomial for depth estimation using a single image [J]. arXiv preprint arXiv: 1011. 5694, 2010.
Cui H N. Gao X. Shen S H, et al. HSFM: Hybrid structure-from-motion [C]. 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1212-1221.
Xu H, Jin Y, Wan W. An efficient 3D reconstruction system for Chinese ancient architectures [C]. 2018 International Conference on Audio, Language and Image Processing. IEEE, 2018: 221-225.
Sergeeva A D, Sablina V A. Using structure from motion for monument 3D reconstruction from images with heterogeneous background [C]. Mediterranean Conference on Embedded Computing. IEEE, 2018: 1-4.
Godard C, Mac Aodha O, Firman M, et al. Digging into self-supervised monocular depth estimation [C]. Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 3828-3838.
Lyu X, Liu L, Wang M, et al. Hr-depth: High resolution self-supervised monocular depth estimation [C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(3): 2294-2301.
Zou Y, Ding Y, Qiu X, et al. M ${^ 2} $ Depth: Self-supervised Two-Frame Multi-camera Metric Depth Estimation [J]. arXiv preprint arXiv:2405.02004, 2024.
Yang M, Wen Y, Chen W, et al. Deep optimized priors for 3d shape modeling and reconstruction [C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 3269-3278.
Zhou Z, Tulsiani S. Sparsefusion: Distilling view-conditioned diffusion for 3d reconstruction [C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 12588-12597.
Sawdayee H, Vaxman A, Bermano A H. Orex: Object reconstruction from planar cross-sections using neural fields [C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 20854-20862.
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