Convex Solutions to S <i>f</i> T and NRS <i>f</i> M Under Algebraic Deformation Models

We present nonlinear formulations to Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) faithfully exploiting the isometric, conformal and equiareal deformation models. Existing work uses relaxations such as inextensibility or requires knowing the optic flow field around the correspondences, an impractical assumption. In contrast, the proposed formulations only require point correspondences and resolve all ambiguities using the notions of maximal depth and maximal isometry heuristics. We propose solution methods using Semi-Definite Programming (SDP) for all formulations. We show that straightforward SDP models conflict with the usual maximal depth heuristic and propose an adapted opposite-depth parameterisation demonstrating a lesser relaxation gap. Experimental results on many real-world benchmark datasets demonstrate superior accuracy over existing methods.

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Publication Details

Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
2025-11-21
DOI
https://doi.org/10.1109/tpami.2025.3635039
Primary Topic
Advanced Vision and Imaging
Type
article
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article

Convex Solutions to S f T and NRS f M Under Algebraic Deformation Models

Adrien Bartoli, Agniva Sengupta
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced Vision and Imaging
article

Convex Solutions to S f T and NRS f M Under Algebraic Deformation Models

Adrien Bartoli, Agniva Sengupta
article en

Abstract

We present nonlinear formulations to Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) faithfully exploiting the isometric, conformal and equiareal deformation models. Existing work uses relaxations such as inextensibility or requires knowing the optic flow field around the correspondences, an impractical assumption. In contrast, the proposed formulations only require point correspondences and resolve all ambiguities using the notions of maximal depth and maximal isometry heuristics. We propose solution methods using Semi-Definite Programming (SDP) for all formulations. We show that straightforward SDP models conflict with the usual maximal depth heuristic and propose an adapted opposite-depth parameterisation demonstrating a lesser relaxation gap. Experimental results on many real-world benchmark datasets demonstrate superior accuracy over existing methods.

IEEE Transactions on Pattern Analysis and Machine IntelligenceVol. 48(3)
Centre National de la Recherche Scientifique (FR), University of Clermont Auvergne (FR), Clermont Université (FR), École Nationale Supérieure d'Architecture de Clermont-Ferrand (FR)
Openalex Percentile: Top 63%
Advanced Vision and Imaging
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