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Journal Articles IEEE Transactions on Pattern Analysis and Machine Intelligence Year : 2022

MORPH-DSLAM: Model Order Reduction for Physics-Based Deformable SLAM

Abstract

We propose a new methodology to estimate the 3D displacement field of deformable objects from video sequences using standard monocular cameras. We solve in real time the complete (possibly visco-)hyperelasticity problem to properly describe the strain and stress fields that are consistent with the displacements captured by the images, constrained by real physics. We do not impose any ad-hoc prior or energy minimization in the external surface, since the real and complete mechanics problem is solved. This means that we can also estimate the internal state of the objects, even in occluded areas, just by observing the external surface and the knowledge of material properties and geometry. Solving this problem in real time using a realistic constitutive law, usually non-linear, is out of reach for current systems. To overcome this difficulty, we solve off-line a parametrized problem that considers each source of variability in the problem as a new parameter and, consequently, as a new dimension in the formulation. Model Order Reduction methods allow us to reduce the dimensionality of the problem, and therefore, its computational cost, while preserving the visualization of the solution in the high-dimensionality space. This allows an accurate estimation of the object deformations, improving also the robustness in the 3D points estimation.
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Dates and versions

hal-04069431 , version 1 (14-04-2023)

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Alberto Badias, Iciar Alfaro, David Gonzalez, Francisco Chinesta, Elias Cueto. MORPH-DSLAM: Model Order Reduction for Physics-Based Deformable SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44 (11), pp.7764-7777. ⟨10.1109/tpami.2021.3118802⟩. ⟨hal-04069431⟩
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