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Statistical approaches to multi-scal...

Carnegie Mellon University.

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Statistical approaches to multi-scale point cloud processing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical approaches to multi-scale point cloud processing./
作者:	Unnikrishnan, Ranjith.
面頁冊數:	128 p.
附註:	Adviser: Martial Hebert.
Contained By:	Dissertation Abstracts International69-05B.
標題:	Artificial Intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3314666
ISBN:	9780549642886

Statistical approaches to multi-scale point cloud processing.
Unnikrishnan, Ranjith.

Statistical approaches to multi-scale point cloud processing. - 128 p.

Adviser: Martial Hebert.

Thesis (Ph.D.)--Carnegie Mellon University, 2008.

In recent years, 3D geometry has gained increasing popularity as the new form of digital media content. Due to advances in sensor technology, it is now feasible to acquire highly detailed 3D scans of complex scenes to obtain millions of data points at high sampling rates over large spatial extents. This ability to acquire high-resolution depth information brings with it the possibility of using 3D geometric data to construct detailed shape models and of perhaps combining 3D depth with visual appearance from images to address challenging problems in computer vision.

ISBN: 9780549642886Subjects--Topical Terms:

769149
Artificial Intelligence.

Statistical approaches to multi-scale point cloud processing.
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245 1 0 $a Statistical approaches to multi-scale point cloud processing.
300 $a 128 p.
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500 $a Source: Dissertation Abstracts International, Volume: 69-05, Section: B, page: 3112.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2008.
520 $a In recent years, 3D geometry has gained increasing popularity as the new form of digital media content. Due to advances in sensor technology, it is now feasible to acquire highly detailed 3D scans of complex scenes to obtain millions of data points at high sampling rates over large spatial extents. This ability to acquire high-resolution depth information brings with it the possibility of using 3D geometric data to construct detailed shape models and of perhaps combining 3D depth with visual appearance from images to address challenging problems in computer vision.
520 $a However, geometric information represented as a 3D point cloud presents challenges uniquely different from other data modalities such as images or audio. Due to a combination of reasons such as the spatial irregularity of the data and the implicit nature of 3D observations, an easy substitution of traditional signal processing operators from images for processing unorganized 3D points is not possible. Furthermore, traditional estimators from classical statistics are not suitable for processing data in this domain, and new algorithms as well as different criteria for evaluating these algorithms are necessary.
520 $a This dissertation contributes towards the development of two fundamental building blocks for processing point clouds. The first is of geometric model fitting, where we present a class of locally semi-parametric estimators that allows finite-sample analysis of accuracy and also explicitly addresses the problem of support-radius selection in local fitting. The second is of multi-scale filtering operators for point clouds that allow detection of interest regions whose locations as well as spatial extent are completely data-driven. The proposed approaches are distinguished from related work by operating directly in the input 3D space on unorganized points without assuming an available mesh or resorting to an intermediate global 2D parameterization.
520 $a Results are presented for several applications including surface reconstruction, accurate shape descriptor computation and repeatable interest region detection, on synthetic data, as well as outdoor aerial and ground-based data obtained with a laser scanner.
590 $a School code: 0041.
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