Accurate, Fast and Controllable Image and Point Cloud Registration

Abstract

Registration is the process of establishing spatial correspondences between two objects. Many downstream tasks, e.g, in image analysis, shape animation, can make use of these spatial correspondences. A variety of registration approaches have been developed over the last decades, but only recently registration approaches have been developed that make use of and can easily process the large data samples of the big data era. On the one hand, traditional optimization-based approaches are too slow and cannot take advantage of very large data sets. On the other hand, registration users expect more controllable and accurate solutions since most downstream tasks, e.g., facial animation and 3D reconstruction, increasingly rely on highly precise spatial correspondences. In recent years, deep network registration approaches have become popular as learning-based approaches are fast and can benefit from large-scale data during network training. However, how to make such deep-learning-based approached accurate and controllable is still a challenging problem that is far from being completely solved. This thesis explores fast, accurate and controllable solutions for image and point cloud registration. Specifically, for image registration, we first improve the accuracy of deep-learning-based approaches by introducing a general framework that consists of affine and non-parametric registration for both global and local deformation. We then design a more controllable image registration approach that image regions could be regularized differently according to their local attributes. For point cloud registration, existing works either are limited to small-scale problems, hardly handle complicated transformations or are slow to solve. We thus develop fast, accurate and controllable solutions for large-scale real-world registration problems via integrating optimal transport with deep geometric learning.