ORCID

http://orcid.org/0000-0002-1628-3792

Date of Award

Spring 5-15-2019

Author's School

School of Engineering & Applied Science

Author's Department

Computer Science & Engineering

Degree Name

Doctor of Philosophy (PhD)

Degree Type

Dissertation

Abstract

In this dissertation, we propose data-driven approaches to reconstruct 3D models for indoor scenes which are represented in a structured way (e.g., a wall is represented by a planar surface and two rooms are connected via the wall). The structured representation of models is more application ready than dense representations (e.g., a point cloud), but poses additional challenges for reconstruction since extracting structures requires high-level understanding about geometries. To address this challenging problem, we explore two common structural regularities of indoor scenes: 1) most indoor structures consist of planar surfaces (planarity), and 2) structural surfaces (e.g., walls and floor) can be represented by a 2D floorplan as a top-down view projection (orthogonality). With breakthroughs in data capturing techniques, we develop automated systems to tackle structured modeling problems, namely piece-wise planar reconstruction and floorplan reconstruction, by learning shape priors (i.e., planarity and orthogonality) from data. With structured representations and production-level quality, the reconstructed models have an immediate impact on many industrial applications.

Language

English (en)

Chair

Tao Ju

Committee Members

Ayan Chakrabarti, Yasutaka Furukawa, Brendan Juba, Ulugbek Kamilov,

Comments

Permanent URL: https://doi.org/7936/mxpa-n449

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