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Sunkel, Martin

Statistical Part-based Models for Object Detection in Large 3D Scans

Universität des Saarlandes, September, 2013, 121 pages
Saarländische Universitäts- und Landesbibliothek (SULB)

                        3D scanning technology has matured to a point where very large scale acquisition of high resolution geometry has become feasible. However, having large quantities of 3D data poses new technical challenges. Many applications of practical use require an understanding of semantics of the acquired geometry. Consequently scene understanding plays a key role for many applications. This thesis is concerned with two core topics: 3D object detection and semantic alignment. We address the problem of efficiently detecting large quantities of objects in 3D scans according to object categories learned from sparse user annotation. Objects are modeled by a collection of smaller sub-parts and a graph structure representing part dependencies. The thesis introduces two novel approaches: A part-based chain structured Markov model and a general part-based full correlation model. Both models come with efficient detection schemes which allow for interactive run-times.

3D object detection, 3D scan, point cloud, part-based, statistical model, Markov model, dynamic programming, machine learning
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Prof. Dr. Hans-Peter Seidel
Dr. Michael Wand
Dr. Michael Wand
Statistical Part-based Models for Object Detection in Large 3D Scans
Campus E14, Room 019
Prof. Dr. Philipp Slusallek
Max-Planck-Institut für Informatik
Computer Graphics Group
Statistical Geometry Processing Group
MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort

BibTeX Entry:
AUTHOR = {Sunkel, Martin},
TITLE = {Statistical Part-based Models for Object Detection in Large 3D Scans},
PUBLISHER = {Saarländische Universitäts- und Landesbibliothek (SULB)},
SCHOOL = {Universit{\"a}t des Saarlandes},
YEAR = {2013},
TYPE = {Doctoral dissertation}
PAGES = {121},
ADDRESS = {Saarbr{\"u}cken},
MONTH = {September},

Entry last modified by Marc Schmitt, 01/30/2014
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09/26/2013 04:11:40 PM
Marc Schmitt
Marc Schmitt
Martin Sunkel
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Martin Sunkel
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