2026-09-24T20:47:59Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/96302021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Seth Teller.
Coorg, Satyan R
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-08-19T19:03:23Z
2005-08-19T19:03:23Z
1998
1998
http://hdl.handle.net/1721.1/9630
42333503
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
Includes bibliographical references (p. 113-121).
Three-dimensional (3-D) modeling of urban environments has numerous applications, including virtual environments, urban planning, and physical simulation. Constructing 3-D models from photographs (images) is thus an important area of research in computer vision, and increasingly, computer graphics. However, despite many years of research, a system that automatically recovers realistic 3-D models remains elusive; most practical systems require significant human input. Unlike automatic algorithms, human-assisted systems are not scalable, both in terms of the number of images processed and the complexity of the generated 3-D model. This thesis describes novel techniques to automatically extract textured 3-D models of urban environments from pose imagery, i.e., images annotated with camera position and orientation in a single global coordinate system. Physical instruments (e.g., surveying, Global Positioning System (GPS), inertial sensors, etc.) are used to provide accurate initial pose estimates to the proposed algorithms. As these estimates are not perfect, I first describe two optimization techniques that refine pose estimates using information present in the images: spherical mosaicing recovers relative rotations between images taken from a single position, and mosaic registration accurately locates mosaics in a global coordinate system. Next, I describe an algorithm that extracts vertical facades from mosaics annotated with accurate pose. The algorithm employs horizontal line segments to detect likely facade orientations and locates these facades using a space-sweep technique. Textures are robustly computed for the facades by combining information from several mosaics using median statistics. I present results for a large pose image dataset ( consisting of about four thousand images taken from eighty-one positions) of an urban office complex. These techniques were successful in recovering all significant vertical facades in the complex, as well as several neighboring facades.
by Satyan R. Coorg.
Ph.D.
121 p.
10461867 bytes
10461620 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science
Pose imagery and automated three-dimensional modeling of urban environments
Thesis
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