JOURNAL ARTICLE

On reconstruction of non-rigid shapes with intrinsic regularization

Abstract

Shape-from-X is a generic type of inverse problems in computer vision, in which a shape is reconstructed from some measurements. A specially challenging setting of this problem is the case in which the reconstructed shapes are non-rigid. In this paper, we propose a framework for intrinsic regularization of such problems. The assumption is that we have the geometric structure of a shape which is intrinsically (up to bending) similar to the one we would like to reconstruct. For that goal, we formulate a variation with respect to vertex coordinates of a triangulated mesh approximating the continuous shape. The numerical core of the proposed method is based on differentiating the fast marching update step for geodesic distance computation.

Keywords:
Geodesic Computation Regularization (linguistics) Vertex (graph theory) Polygon mesh Computer science Shape optimization Geometric shape Inverse problem Algorithm Iterative reconstruction Inverse Mathematics Computer vision Artificial intelligence Geometry Mathematical analysis Theoretical computer science Physics Finite element method Graph

Metrics

9
Cited By
0.69
FWCI (Field Weighted Citation Impact)
34
Refs
0.72
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

3D Shape Modeling and Analysis
Physical Sciences →  Engineering →  Computational Mechanics
Optical measurement and interference techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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