Please use this identifier to cite or link to this item:
Authors: YEW ZI JIAN
Keywords: point cloud, registration, deep learning
Issue Date: 4-Jan-2022
Citation: YEW ZI JIAN (2022-01-04). LEARNING POINT CLOUD REGISTRATION. ScholarBank@NUS Repository.
Abstract: Point cloud registration refers to the task of finding the spatial transformation that aligns two or more point clouds. It is an important problem with many applications in robotics and virtual or augmented reality. In this thesis comprising four works, we ­­­propose several ways to learn from data to improve point cloud registration performance. We first propose RPM-Net, which learns to refine the alignment between two partially overlapping point clouds in an iterative manner using learned hybrid features. The second work, 3DFeat-Net, addresses the problem of learning local features for correspondence-based registration approaches in a weakly supervised manner, through the use of approximate GPS/INS poses. Next, we propose REGTR, which is a RANSAC-free correspondence-based registration method.­­ Finally, we tackle the problem of registration of more than two point clouds by designing a learned view-graph optimization approach using recurrent graph neural networks.
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
YewZJ.pdf17.58 MBAdobe PDF



Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.