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arXiv · 2501.01715

Cloth-Splatting: 3D Cloth State Estimation from RGB Supervision

Alberta Longhini·Marcel Büsching·Bardienus P. Duisterhof·Jens Lundell·Jeffrey Ichnowski·Mårten Björkman·Danica Kragic
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Abstract

We introduce Cloth-Splatting, a method for estimating 3D states of cloth from RGB images through a prediction-update framework. Cloth-Splatting leverages an action-conditioned dynamics model for predicting future states and uses 3D Gaussian Splatting to update the predicted states. Our key insight is that coupling a 3D mesh-based representation with Gaussian Splatting allows us to define a differentiable map between the cloth state space and the image space. This enables the use of gradient-based optimization techniques to refine inaccurate state estimates using only RGB supervision. Our experiments demonstrate that Cloth-Splatting not only improves state estimation accuracy over current baselines but also reduces convergence time.

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Alberta Longhini, Marcel Büsching, Bardienus P. Duisterhof, Jens Lundell, Jeffrey Ichnowski, Mårten Björkman, Danica Kragic. 2025-01-03. Cloth-Splatting: 3D Cloth State Estimation from RGB Supervision. https://arxiv.org/abs/2501.01715

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