Abstract
We present a novel fully-automated approach to non-rigid registration for high-resolution facial scans using conformal harmonic maps. The novelty of this paper is its use of applied deep learning models to prepare data for geometric algorithms to compute non-rigid registration. We use facial detection to both constrain the boundary of the face and provide a mechanism to manipulate the input mesh. We use conformal harmonic maps[7] to map a dense 3D point cloud to the closed unit disc D1(0) and optimize the weights of each edge. Our experiments show the effectiveness of this approach.
Year
5-2023
Document Type
Thesis
Keywords
Registration, Non-Rigid Registration, Deep Learning, Conformal Harmonic Map
Degree Name
Master of Science (MS)
Department
Computer Science
Advisor
Xianfeng Gu
Recommended Citation
Billmann, Daniel, "Non-Rigid Registration with Deep Learning and Conformal Harmonic Maps" (2023). Electronic Dissertations and Theses. 28.
https://commons.library.stonybrook.edu/electronic-disserations-theses/28