Differentiable Mesh State Estimation via Factor Graph Inference for Deformable Object Reconstruction

• URL: http://arxiv.org/abs/2609.16686v1
• Date: Tue, 15 Sep 2026 06:07:15 GMT
• Title: Differentiable Mesh State Estimation via Factor Graph Inference for Deformable Object Reconstruction
• Abstract summary: Estimating deformable object states remains a fundamental challenge in robotics and simulation.<n>We propose a novel factor graph-based framework for probabilistic mesh state estimation of deformable objects.
• Score: 35.217259059423775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimating deformable object states remains a fundamental challenge in robotics and simulation. We propose a novel factor graph-based framework for probabilistic mesh state estimation of deformable objects. The method directly updates a tetrahedral mesh, a rich and physically-grounded representation of an environment, by combining physics priors, noisy sensor measurements, and temporal smoothness constraints within a unified probabilistic formulation. The estimation problem is posed as a nonlinear least-squares optimization and solved using Levenberg-Marquardt. Ex vivo central-airway obstruction experiments and simulations on deforming cube models demonstrate reliable and accurate reconstruction under both rigid motion and deformation, highlighting the potential of this probabilistic approach for principled, measurement-driven mesh state estimation in deformable object reconstruction.

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