Date of Degree

2-2018

Document Type

Dissertation

Degree Name

Ph.D.

Program

Computer Science

Advisor

Ioannis Stamos

Committee Members

Yingli Tian

Andrew Rosenberg

Philippos Mordohai

Subject Categories

Artificial Intelligence and Robotics | Computer Sciences

Keywords

machine learning, neural networks, deep learning, computer vision, lidar

Abstract

We address the problem of identifying objects of interest in 3D images as a set of related tasks involving localization of objects within a scene, segmentation of observed object instances from other scene elements, classifying detected objects into semantic categories, and estimating the 3D pose of detected objects within the scene. The increasing availability of 3D sensors motivates us to leverage large amounts of 3D data to train machine learning models to address these tasks in 3D images. Leveraging recent advances in deep learning has allowed us to develop models capable of addressing these tasks and optimizing these tasks jointly to reduce potential errors propagated when solving these tasks independently.

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