Automatically Discovering Urban Features for 3D City Modeling

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Automatically Discovering Urban Features for 3D City Modeling"

By

Mr. Peng ZHAO


Abstract

Growing 3D map services drives tremendous demand for photo-realistic 
modeling of cities from images captured at ground level. This modeling of 
cities reduces de facto to that of building facades. The accurate 
extraction and partition of individual facades from urban scenes and the 
semantic analysis of each individual facade are two main challenges.

The key to solve these problems is using special features existing in 
urban environment: rectilinearity and symmetry. First, a joint 2D-3D 
segmentation methods assuming rectilinear boundary of facade parses the 
environment into buildings, the ground, and the sky; for the first time, 
buildings are further partitioned into individual facades using the 
proposed dynamic programming optimization. The next step detects and 
segments structural elements within individual facade by exploiting the 
information redundancy of repetition. We propose a dual image- and 
transform-space optimization method based on the formulation of Markov 
random field (MRF), capable of simultaneously discovering multiple 
interfering repetitions. After that, we extend the MRF formulation to the 
detection of per-pixel symmetry, and then develop a learning-based 
segmentation method that can extract symmetry objects, which are 
recognized as architecture elements, from background walls. Extensive 
evaluation on large-scale data sets of cities demonstrates both 
quantitative and qualitative improvements of our detection and 
segmentation methods over the state-of-the-art, especially dealing with 
multiple interfering symmetries, low-count symmetries, and architecture 
element extraction, etc.

The extracted architecture elements are re-assembled into a set of newly 
invented computer-generated architecture (CGA) grammar rules with contain 
rules. Given the facade analysis results and the learnt grammar rules, we 
develop a 3D city modeling method which is capable of generating detailed 
geometry models with refined textures. Besides, instead of creating 3D 
models from scratch, we make use of the existing approximate models of 
buildings, together with the analysis results of both 3D geometry and 2D 
textures, to generate finer models of the same buildings. The performance 
of our modeling and remodeling methods is demonstrated on several 
challenging data sets and both analytical and perceptual improvements are 
achieved.


Date:			Monday, 27 August 2012

Time:			2:00pm – 4:00pm

Venue:			Room 3494
 			Lifts 25/26

Chairman:		Prof. David Banfield (LIFS)

Committee Members:	Prof. Long Quan (Supervisor)
 			Prof. Chiew-Lan Tai
 			Prof. Chi-Keung Tang
 			Prof. Ajay Joneja (IELM)
                      	Prof. Jiaya Jia (Comp. Sci. & Engg, CUHK)
 			Prof. Peter Sturm (INRIA Grenoble)


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