Point Cloud Registration(PCR) is an important step in fields such as robotic manipulation, augmented and virtual reality, SLAM, etc. In the context of computer vision, registration, in general, refers to the process of aligning data obtained from different frames, and as the name suggests PCR is the task of aligning point clouds. The main contribution of this thesis is drawing parallels between PCR and classification which allows us to apply well-studied concepts from classification in PCR. This thesis further shows two applications of drawing such parallels in the context of deep learning-based PCR. We show the use of cross-entropy loss, and discrepancy loss from classification for partial to full PCR, and outlier filtering respectively. We finally show that many of the existing deep learning-based PCR architectures can be easily modified to be trained using the loss functions from classification
Howie Choset (Advisor)
Matthew Travers (Co-advisor)
Zoom Participation. See announcement.