Type
Text
Type
Thesis
Advisor
Samaras, Dimitris | Gu, Xianfeng | Berg, Tamara.
Date
2012-05-01
Keywords
3D reconstruction, AAM, Kinect | Computer science
Department
Department of Computer Science
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/71366
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
A 3D scanner is a device that analyzes a real world object and generates a point cloud describing the surface of such object, possibly including color information as well. However, these devices are expensive, fragile, large, and usually require especially adapted facilities to house them. The advent of inexpensive depth sensors such as Kinect provide new opportunities to bridge the existing gap between systems that offer good scanning quality and systems that are affordable. The objective of this thesis is to use Kinect as a 3D scanner. We achieve this goal by exploring techniques to generate point clouds from depth maps, and triangulation methods to construct meshes from point clouds. However, depth maps are not noise-free. To deal with this noise, we explore different depth map reconstruction and smoothing techniques. We then measure their effectiveness in reducing the noise and enhancing the quality of the generated model. The main contribution of this work is an acquisition and processing pipeline that allows for capture and generation of accurate 3D models whose quality is comparable to those generated by expensive scanner devices. We show that the accuracy of our acquisition system is on par with higher resolution scanners. We also demonstrate applications for our method by capturing a data set of human faces and generating an Active Appearance Model from this data set. | 55 pages
Recommended Citation
Orrego, Carlos, "3D Scanning Using Consumer-Grade Depth Sensors: Methods and Applications" (2012). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 572.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/572