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Differentiable Subdivision Surface Fitting


Differentiable Subdivision Surface Fitting

Xie, Tianhao (2022) Differentiable Subdivision Surface Fitting. Masters thesis, Concordia University.

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In this paper we present a powerful differentiable surface fitting technique to derive a compact surface representation for a given dense point cloud or mesh, with application in the domains of graphics and CAD/CAM. We have chosen the Loop subdivision surface, which in the limit yields the smooth surface underlying the point cloud, and can handle complex surface topology better than other popular compact representations, such as NURBS(Non-uniform rational basis spline). The principal idea is to fit the Loop subdivision surface not directly to the point cloud, but to the IMLS (Implicit moving least squares) surface defined over the point cloud. As both Loop subdivision and IMLS have analytical expressions, we are able to formulate the problem as an unconstrained minimization problem of a completely differentiable function that can be solved with standard numerical solvers. Differentiability enables us to integrate the subdivision surface into any deep learning method for point clouds or meshes. We demonstrate the versatility and potential of this approach by using it in conjunction with a differentiable renderer to robustly reconstruct compact surface representations of spatial-temporal sequences of dense meshes.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Xie, Tianhao
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:8 August 2022
Thesis Supervisor(s):Tiberiu, Popa
ID Code:990913
Deposited By: Tianhao Xie
Deposited On:27 Oct 2022 14:15
Last Modified:27 Oct 2022 14:15
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