The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Efficient RANSAC for Point‐Cloud Shape Detection
Abstract
In this paper we present an automatic algorithm to detect basic shapes in unorganized point clouds. The algorithm decomposes the point cloud into a concise, hybrid structure of inherent shapes and a set of remaining points. Each detected shape serves as a proxy for a set of corresponding points. Our method is based on random sampling and detects planes, spheres, cylinders, cones and tori. For models with surfaces composed of these basic shapes only, for example, CAD models, we automatically obtain a representation solely consisting of shape proxies. We demonstrate that the algorithm is robust even in the presence of many outliers and a high degree of noise. The proposed method scales well with respect to the size of the input point cloud and the number and size of the shapes within the data. Even point sets with several millions of samples are robustly decomposed within less than a minute. Moreover, the algorithm is conceptually simple and easy to implement. Application areas include measurement of physical parameters, scan registration, surface compression, hybrid rendering, shape classification, meshing, simplification, approximation and reverse engineering.
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Pujol, J. M., Delgado, J., Ramon Sangüesa, A., & Flache, A. (2005) · International Joint Conference on Artificial Intelligence · Cited by 5,771
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Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., & Stuetzle, W. (1992) · Proceedings of the 19th annual conference on Computer graphics and interactive techniques · doi:10.1145/133994.134011 · Cited by 2,730
MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
Torr, P. H. S., & Zisserman, A. (2000) · Computer Vision and Image Understanding · doi:10.1006/cviu.1999.0832 · Cited by 2,396
A survey of the hough transform
Illingworth, J., & Kittler, J. (1988) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(88)80033-1 · Cited by 2,134
Reverse engineering of geometric models—an introduction
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Segmentation through variable-order surface fitting
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Variational shape approximation
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Rusu, R. B., Marton, Z. C., Blodow, N., Dolha, M., & Beetz, M. (2008) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.08.005 · Cited by 1,084
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Hosamo, H. H., & Hosamo, M. H. (2022) · Advances in Civil Engineering · doi:10.1155/2022/2194949 · Cited by 103
Levy, J. J., Davis, M. J., Chacko, R. S., Davis, M. J., Fu, L. J., Goel, T., Pamal, A., Nafi, I., Angirekula, A., Suvarna, A., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-023-00477-7 · Cited by 20
3DP3: 3D Scene Perception via Probabilistic Programming
Gothoskar, N., Cusumano-Towner, M., Zinberg, B., Ghavamizadeh, M., Pollok, F., Garrett, A., Tenenbaum, J. B., Gutfreund, D., & Mansinghka, V. K. (2021) · arXiv · doi:10.48550/arxiv.2111.00312 · Cited by 18
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Metadata
- Authors
- Schnabel, R., Wahl, R., & Klein, R.
- Venue
- Computer Graphics Forum
- Publisher
- Wiley
- Published
- 2007-05-21
- Volume
- 26
- Issue
- 2
- Pages
- 214-226
- ISSN
- 0167-7055 (ISSN-L)
- 1467-8659
- OpenAlex
- W2000018820
- Cited by
- 2,106
- References
- 21
- FWCI
- 26.52
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
