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Accurate 3D Model Acquisition from Imagery Data
by
Zhuoliang Kang
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
August 2015
Copyright 2015 Zhuoliang Kang
Object Description
| Title | Accurate 3D model acquisition from imagery data |
| Author | Kang, Zhuoliang |
| Author email | zkang@usc.edu;zhuoliangkang@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2015-04-03 |
| Date submitted | 2015-06-18 |
| Date approved | 2015-06-18 |
| Restricted until | 2015-06-18 |
| Date published | 2015-06-18 |
| Advisor (committee chair) |
Medioni, Gerard G. Medioni, Gérard G. |
| Advisor (committee member) |
Li, Hao Sawchuk, Alexander A. (Sandy) |
| Abstract | Acquisition of geometric 3D models from 2D imagery has been essential for various applications. In particular, this dissertation investigates two important application scenarios: city‐scale 3D reconstruction from aerial imagery and general 3D model acquisition with a commodity camera. ❧ The first part of this dissertation explores an online solution to the problem. We propose an approach to solve camera pose estimation and dense reconstruction from Wide Area Aerial Surveillance (WAAS) videos captured by an airborne platform. Our approach solves them in an online fashion: it incrementally updates a sparse 3D map and estimates the camera pose as each new frame arrives; depth maps of selected key frames are computed using a variational method and integrated to produce a full 3D model via volumetric reconstruction. In practice, aerial imagery is usually captured using a multi‐camera system. We propose an approach for camera pose estimation of multi‐camera aerial imagery which is parallelized on multiple GPUs for efficiency. The approach is also extended for progressive 3D model acquisition with a hand‐held camera. ❧ In many scenarios, online approach is not a necessity and accuracy has higher priority over efficiency. In the second part, we present MeshRecon, a mesh‐based offline system composed of three modules: a dense point cloud is generated using multi‐resolution plane sweep method; an initial mesh model is extracted from the point cloud via global optimization considering visibility information of all images; the mesh model is then iteratively refined to capture structural details by optimizing the photometric consistency and spatial regularization. The major processes are parallelized on GPU for efficiency. For the aerial imagery case, we evaluate our system on several real-world multi‐camera aerial imagery datasets, each covering an urban scenario of several square kilometers. Quantitative result shows that the reconstructed geometric 3D model is highly accurate with error smaller than 1 meter over the entire city. Besides aerial imagery, we also evaluate its performance on general geometric 3D model acquisition of real‐world objects. Result shows that the system is robust and flexible for various types of objects at different scales in both indoor and outdoor environments. Based on city 3D models reconstructed at different times, we present a system for city‐scale geometry change detection by performing comparisons at the 3D geometry level. Our system is able to detect geometry changes at different scales, ranging from a building cluster to small‐scale vegetation changes, with high accuracy. In the end, we conclude the dissertation with contributions and future work. |
| Keyword | computer vision; 3D reconstruction; urban reconstruction |
| Language | English |
| Format (imt) | application/pdf |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Contributing entity | University of Southern California |
| Rights | Kang, Zhuoliang |
| Physical access | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-KangZhuoli-3475.pdf |
| Archival file | Volume2/etd-KangZhuoli-3475.pdf |
Description
| Title | Page 1 |
| Repository email | cisadmin@lib.usc.edu |
| Full text | Accurate 3D Model Acquisition from Imagery Data by Zhuoliang Kang A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful llment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) August 2015 Copyright 2015 Zhuoliang Kang |
