Pose imagery and automated three-dimensional modeling of urban environments
Name
42333503-MIT.pdf
Description
Full printable version
Size
9.98 MB
Format
Adobe PDF
Checksum (MD5)
a48c962ec33e996b036d2cc1765077c7
Author(s)
Coorg, Satyan R
Advisor(s)
Seth Teller.
Date Issued
1998
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
Includes bibliographical references (p. 113-121).
Subjects
Electrical Engineering and Computer Science
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
Persistent DSpace Link
