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Curve and surface reconstruction fro...

Poon, Sheung-Hung.

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Curve and surface reconstruction from noisy samples.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Curve and surface reconstruction from noisy samples./
作者:	Poon, Sheung-Hung.
面頁冊數:	123 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4676.
Contained By:	Dissertation Abstracts International65-09B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3148581
ISBN:	0496072609

Curve and surface reconstruction from noisy samples.
Poon, Sheung-Hung.

Curve and surface reconstruction from noisy samples. - 123 p.

Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4676.

Thesis (Ph.D.)--Hong Kong University of Science and Technology (People's Republic of China), 2004.

Reconstructing an unknown curve or surface from sample points is an important task in geometric modeling applications. Sample points obtained from real applications are usually noisy. For example, when data sets are obtained by scanning images in the plane or images in three dimensions. In computer graphics, many curve and surface reconstruction algorithms have been developed. However, their common drawback is the lack of theoretical guarantees on the quality of the reconstruction. This motivates computational geometers to propose algorithms that return provably faithful reconstructions. Algorithms of this type are known when there is no noise in the input. This leaves the problem of noise handling open. We propose a probabilistic noise model for the curve reconstruction problem. Based on this model, we design a curve reconstruction algorithm for noisy input points. The reconstruction is faithful with probability approaching 1 as the sampling density increases. Then we extend our approach to surface reconstruction from noisy input points. Not only do we improve the algorithm to make it run faster, we also make the noise model deterministic which extends its applicability and simplifies the analysis of the algorithm. We show that the surface reconstructed is faithful if the input points satisfy the deterministic noise model.

ISBN: 0496072609Subjects--Topical Terms:

626642
Computer Science.

Curve and surface reconstruction from noisy samples.
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502 $a Thesis (Ph.D.)--Hong Kong University of Science and Technology (People's Republic of China), 2004.
520 $a Reconstructing an unknown curve or surface from sample points is an important task in geometric modeling applications. Sample points obtained from real applications are usually noisy. For example, when data sets are obtained by scanning images in the plane or images in three dimensions. In computer graphics, many curve and surface reconstruction algorithms have been developed. However, their common drawback is the lack of theoretical guarantees on the quality of the reconstruction. This motivates computational geometers to propose algorithms that return provably faithful reconstructions. Algorithms of this type are known when there is no noise in the input. This leaves the problem of noise handling open. We propose a probabilistic noise model for the curve reconstruction problem. Based on this model, we design a curve reconstruction algorithm for noisy input points. The reconstruction is faithful with probability approaching 1 as the sampling density increases. Then we extend our approach to surface reconstruction from noisy input points. Not only do we improve the algorithm to make it run faster, we also make the noise model deterministic which extends its applicability and simplifies the analysis of the algorithm. We show that the surface reconstructed is faithful if the input points satisfy the deterministic noise model.
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