Abstract

This thesis presents novel algorithms of architectural modeling, a crucial computer vision task to understand architectures by parsing visual input such as image or sensor data into digital representations. Compelling modeling algorithms serve as the fundamental block for a wide range of applications, including augmented reality, digital mapping, virtual simulation. While numerous handcrafted approaches have been proposed, the problem remains challenging in various difficult cases, such as occlusions and imperfect input. This dissertation studies four novel data-driven methods to perform high quality modeling. I first propose two geometric architectural modeling algorithms to recover the geometric primitives from aerial and facade views. Then, I design two algorithms for photometric architectural modeling, focusing on the illumination attributes of interior scenes.

Committee Chair

Tao Ju Yasutaka Furukawa

Committee Members

Ayan Chakrabarti, Brendan Juba, Ulugbek Kamilov,

Degree

Doctor of Philosophy (PhD)

Author's Department

Computer Science & Engineering

Author's School

McKelvey School of Engineering

Document Type

Dissertation

Date of Award

Spring 5-15-2021

Language

English (en)

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