Oral Defense of Doctoral Dissertation – Computational Sciences and Informatics – Self-Similar Spin Images for Point Cloud Matching – Daniel Pulido

Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Sciences and Informatics
Department of Computational and Data Sciences
College of Science
George Mason University

Daniel Pulido
Bachelor of Science, Boston University, 1998
Master of Science, Worcester Polytechnic Institute, 2003

Self-Similar Spin Images for Point Cloud Matching

Friday, October 13, 2017, 10:00 a.m.
Research Hall, Room 162

All are invited to attend.

Anthony Stefanidis, Dissertation Director
Estela Blaisten-Barojas
Arie Croitoru
Juan Cebral

The rapid growth of Light Detection And Ranging (Lidar) technologies that collect, process, and disseminate 3D point clouds have allowed for increasingly accurate spatial modeling and analysis of the real world. Lidar sensors can generate massive 3D point clouds of a collection area that provide highly detailed spatial and radiometric information. Simultaneously, the growth of other forms of geospatial data (e.g., crowdsourced Web 2.0 data) have provided researchers with a wealth of freely available data sources that cover a variety of geographic areas. However, combining data from disparate sources requires overcoming numerous technical challenges in order to generate products that mitigate their respective disadvantages and combine their advantages.

Therefore, this dissertation addresses the problem of fusing two point clouds from potentially different sources by considering two specific problems: scale matching and feature matching. To address the problem of feature matching we develop a novel feature descriptor referred to as “Self-Similar Spin Images” which combine the concept of local self-similarity with the descriptive power of Spin Images. To address the problem of scale matching we develop a novel scale detection metric referred to as “Self-Similar Keyscale” which analyzes the self-similarity of two point clouds to identify a characteristic scale to match them. Finally, we develop a novel change detection method as a sample use case of the developed matching techniques.