|Deep-learning Approaches to Object Recognition from 3D Data|
|Master of Sciences, Case Western Reserve University, 2017, EMC - Mechanical Engineering|
This thesis focuses on deep-learning approaches to recognition and pose estimation of graspable objects using depth information. Recognition and orientation detection from depth-only data is encoded by a carefully designed 2D descriptor from 3D point clouds. Deep-learning approaches are explored from two main directions: supervised learning and semi-supervised learning. The disadvantages of supervised learning approaches drive the exploration of unsupervised pretraining. By learning good representations embedded in early layers, subsequent layers can be trained faster and with better performance. An understanding of learning processes from a probabilistic perspective is concluded, and it paves the way for developing networks based on Bayesian models, including Variational Auto-Encoders. Exploitation of knowledge transfer--re-using parameters learned from alternative training data--is shown to be effective in the present application.
Committee: Wyatt Newman, PhD (Advisor); M. Cenk Çavusoglu, PhD (Committee Member); Roger Quinn, PhD (Committee Member)
Computer Science; Medical Imaging; Nanoscience; Robotics
Keywords: deep learning; 3D object recognition; semi-supervised learning; knowledge transfer