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| <!DOCTYPE html> | ||
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| content="Cloth-Splatting: 3D State Estimation from RGB Supervision for Deformable Objects"> | ||
| <meta name="keywords" content="Robotics, Computer Vision, Machine Learning, 3D State Estimation, Gaussian Splatting, Vision-based Tracking, De- | ||
| formable Objects"> | ||
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| <title>Cloth-Splatting: 3D State Estimation from RGB Supervision for Deformable Objects</title> | ||
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| <h1 class="title is-1 publication-title">Cloth-Splatting: 3D State Estimation from RGB Supervision for Deformable Objects</h1> | ||
| <div class="is-size-5 publication-authors"> | ||
| <span class="author-block"> | ||
| <a href="https://albilo17.github.io/">Alberta Longhini</a><sup>1</sup>, | ||
| </span> | ||
| <span class="author-block"> | ||
| <a href="https://buesma.github.io/">Marcel Büsching</a><sup>1</sup>, | ||
| </span> | ||
| <span class="author-block"> | ||
| <a href="https://www.bart-ai.com/">Bardienus P. Duisterhof </a><sup>2</sup>, | ||
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| <span class="author-block"> | ||
| <a href="http://jenslundell.ai/">Jens Lundell</a><sup>1</sup>, | ||
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| </br> | ||
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| <a href="https://ichnow.ski/">Jeffrey Ichnowski</a><sup>2</sup>, | ||
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| <a href="https://www.kth.se/profile/celle">Mårten Björkman</a><sup>1</sup>, | ||
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| <span class="author-block"> | ||
| <a href="https://www.kth.se/profile/dani">Danica Kragic</a><sup>1</sup> | ||
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| <span class="author-block"><sup>1</sup>KTH Royal Institute of Technology </span> | ||
| <span class="author-block"><sup>2</sup>Carnegie Mellon University </span> | ||
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| <span>Data</span> | ||
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| High-quality state estimation of highly deformable objects can enable new applications in robotics while aiding existing imitation learning approaches. Precise 3D state estimation remains a significant challenge for deformable objects like cloths due to their infinite-dimensional state space and complex, often self-occluding, configurations. | ||
| <br><br> | ||
| We propose Cloth-Splatting, a novel method for estimating the 3D state of cloth from sparse RGB observations by combining a learned 3D model of the cloth with Gaussian Splatting. Our key insight is that by integrating Gaussian Splatting with the 3D state predictions of a learned cloth model, we can leverage Gaussian Splatting as a differentiable map between 3D state representations and image observations. This differentiable property enables the refinement of inaccurate state predictions using only RGB supervision. The experiments suggest that Cloth-Splatting outperforms state-of-the-art 3D tracking baselines in state estimation accuracy. Additionally, Cloth-Splatting reduces convergence time by ~85% over the state-of-the-art. | ||
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| <h2 class="title">BibTeX</h2> | ||
| <pre><code> | ||
| @misc{longhini2024cloth, | ||
| title = {Cloth-Splatting: 3D State Estimation from RGB Supervision for Deformable Objects}, | ||
| author = {Alberta Longhini and Marcel Büsching and Bardienus Pieter Duisterhof and Jens Lundell and Jeffrey Ichnowski and Mårten Björkman and Danica Kragic }, | ||
| year = {2024}, | ||
| eprint = {ADD ME}, | ||
| archivePrefix = {arXiv}, | ||
| }</code></pre> | ||
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| This work was supported by the Swedish Research Council; the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation; the European Research Council (ERC-884807); and the Center for Machine Learning and Health (CMLH). The computations were enabled by the the Pittsburgh Supercomputing Center and by the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the Swedish National Supercomputer Centre. | ||
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