Publication
Deformable surface reconstruction via Riemannian metric preservation
Journal Article (2024)
Journal
Computer Vision and Image Understanding
Pages
104155
Volume
249
Doc link
https://dx.doi.org/10.1016/j.cviu.2024.104155
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Authors
Projects associated
Abstract
Estimating the pose of an object from a monocular image is a fundamental inverse problem in computer vision. Due to its ill-posed nature, solving this problem requires incorporating deformation priors. In practice, many materials do not perceptibly shrink or extend when manipulated, constituting a reliable and well-known prior. Mathematically, this translates to the preservation of the Riemannian metric. Neural networks offer the perfect playground to solve the surface reconstruction problem as they can approximate surfaces with arbitrary precision and allow the computation of differential geometry quantities. This paper presents an approach for inferring continuous deformable surfaces from a sequence of images, which is benchmarked against several techniques and achieves state-of-the-art performance without the need for offline training. Being a method that performs per-frame optimization, our method can refine its estimates, contrary to those based on performing a single inference step. Despite enforcing differential geometry constraints at each update, our approach is the fastest of all the tested optimization-based methods.
Categories
computer vision, image matching, pose estimation.
Author keywords
Shape-from-template, 3D reconstruction, Deformable surfaces, Differential geometry, Surface parametrization
Scientific reference
O. Barbany, A. Colomé and C. Torras. Deformable surface reconstruction via Riemannian metric preservation. Computer Vision and Image Understanding, 249: 104155, 2024.
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