OptCtrlPoints: Finding the Optimal Control Points for Biharmonic 3D Shape Deformation

TL;DR

OptCtrlPoints introduces a data-driven method to identify sparse, optimally distributed control points for biharmonic 3D shape deformation, improving shape fitting accuracy and computational efficiency.

Contribution

The paper presents a reformulation of biharmonic computation and an efficient search algorithm to find optimal control points, reducing complexity and computation time.

Findings

Achieves better shape fit than FPS, random, and neural methods.

Reduces computation time from days to about 3 minutes.

Demonstrates effectiveness on multiple 3D datasets.

Abstract

We propose OptCtrlPoints, a data-driven framework designed to identify the optimal sparse set of control points for reproducing target shapes using biharmonic 3D shape deformation. Control-point-based 3D deformation methods are widely utilized for interactive shape editing, and their usability is enhanced when the control points are sparse yet strategically distributed across the shape. With this objective in mind, we introduce a data-driven approach that can determine the most suitable set of control points, assuming that we have a given set of possible shape variations. The challenges associated with this task primarily stem from the computationally demanding nature of the problem. Two main factors contribute to this complexity: solving a large linear system for the biharmonic weight computation and addressing the combinatorial problem of finding the optimal subset of mesh vertices. To overcome these challenges, we propose a reformulation of the biharmonic computation that reduces the matrix size, making it dependent on the number of control points rather than the number of vertices. Additionally, we present an efficient search algorithm that significantly reduces the time complexity while still delivering a nearly optimal solution. Experiments on SMPL, SMAL, and DeformingThings4D datasets demonstrate the efficacy of our method. Our control points achieve better template-to-target fit than FPS, random search, and neural-network-based prediction. We also highlight the significant reduction in computation time from days to approximately 3 minutes.