Three-Dimensional Object Search, Understanding, and Pose Estimation with Low-Cost Sensors

With the recent development of low-cost depth sensors, an entirely new type of 3D data is being generated rapidly by regular consumers. Traditionally, 3D data is produced by a small number of professional designers (i.e., the Computer Aided Design (CAD) model); however, 3D data from massive consumer-level sensors has the potential of introducing many new applications, such as user-captured 3D warehouse and search engines, robots with 3D sensing capability, and customized 3D printing. Nevertheless, the low-cost sensors used by general consumers also pose new technological challenges. First, they have relatively high levels of sensor noise. Second, the use of such consumer devices is often in uncontrolled settings, resulting in challenging conditions, such as poor lighting, cluttered scenes, and object occlusion. To address such emerging opportunities and associated challenges, this dissertation is dedicated to the development of novel algorithms and systems for 3D data understanding and processing, using input from a consumer-level 3D sensor. In particular, the key problems of 3D shape retrieval, scene understanding, and pose recognition are explored in order to present a comprehens