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Authors: Arul Selvam Periyasamy ; Max Schwarz and Sven Behnke

Affiliation: Autonomous Intelligent Systems, University of Bonn, Germany

Keyword(s): Differentiable Rendering, Deformable Registration, Latent Shape-space Model.

Abstract: For autonomous robotic systems, comprehensive 3D scene parsing is a prerequisite. Machine learning techniques used for 3D scene parsing that incorporate knowledge about the process of 2D image generation from 3D scenes have a big potential. This has sparked an interest in differentiable renderers that provide approximate gradients of the rendered image with respect to scene and object parameters. An efficient differentiable renderer facilitates approaching many 3D scene parsing problems using a render-and-compare framework, where the object and scene parameters are optimized by minimizing the difference between rendered and observed images. In this work, we introduce StilllebenDR, a light-weight scalable differentiable renderer built as an extension to the Stillleben library and use it for 3D deformable registration from single-view RGB images. Our end-to-end differentiable pipeline achieves results comparable to state-of-the-art methods without any training and outperforms the compe ting methods significantly in the presence of pose initialization errors. (More)

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Paper citation in several formats:
Periyasamy, A.; Schwarz, M. and Behnke, S. (2022). Iterative 3D Deformable Registration from Single-view RGB Images using Differentiable Rendering. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 107-116. DOI: 10.5220/0010817100003124

@conference{visapp22,
author={Arul Selvam Periyasamy. and Max Schwarz. and Sven Behnke.},
title={Iterative 3D Deformable Registration from Single-view RGB Images using Differentiable Rendering},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={107-116},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010817100003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Iterative 3D Deformable Registration from Single-view RGB Images using Differentiable Rendering
SN - 978-989-758-555-5
IS - 2184-4321
AU - Periyasamy, A.
AU - Schwarz, M.
AU - Behnke, S.
PY - 2022
SP - 107
EP - 116
DO - 10.5220/0010817100003124
PB - SciTePress

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