Learning Neural Parametric 3D Breast Shape Models for Metrical Surface Reconstruction from Monocular RGB Videos - 25.54
Title:
Learning Neural Parametric 3D Breast Shape Models for Metrical Surface Reconstruction from Monocular RGB Videos
Authors:
Maximilian WEIHERER 1,2, Antonia VON RIEDHEIM 3, Vanessa BREBANT 3, Bernhard EGGER 1, Christoph PALM 2
1 Visual Computing Erlangen, Friedrich-Alexander-Universitaet Erlangen-Nuernberg, Germany;
2 Regensburg Medical Image Computing (ReMIC), OTH Regensburg, Germany;
3 Department for Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg, Germany
Keywords:
3D Reconstruction, Shape Modeling
Abstract:
We present a neural parametric 3D breast shape model and, based on this model, introduce a low-cost and accessible 3D surface reconstruction pipeline capable of recovering accurate breast geometry from a monocular RGB video. In contrast to widely used, commercially available yet prohibitively expensive 3D breast scanning solutions and existing low-cost alternatives, our method requires neither specialized hardware nor proprietary software and can be used with any device that is able to record RGB videos.
The key building blocks of our pipeline are a state-of-the-art, off-the-shelf Structure-from-motion pipeline, paired with a parametric breast model for robust and metrically correct surface reconstruction.
Our model, similarly to the recently proposed implicit Regensburg Breast Shape Model (iRBSM), leverages implicit neural representations to model breast shapes. However, unlike the iRBSM, which employs a single global neural signed distance function (SDF), our approach -- inspired by recent state-of-the-art face models -- decomposes the implicit breast domain into multiple smaller regions, each represented by a local neural SDF anchored at anatomical landmark positions. When incorporated into our surface reconstruction pipeline, the proposed model, dubbed liRBSM (short for localized iRBSM), significantly outperforms the iRBSM in terms of reconstruction quality, yielding more detailed surface reconstruction than its global counterpart. Overall, we find that the introduced pipeline is able to recover high-quality 3D breast geometry within an error margin of less than 2 mm. Our method is fast (requires less than six minutes), fully transparent and open-source, and - together with the model - publicly available.
Abstract:
Presentation:
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How to Cite (MLA):
Weiherer, Maximilian, et al., "Learning Neural Parametric 3D Breast Shape Models for Metrical Surface Reconstruction from Monocular RGB Videos", Proceedings of 3DBODY.TECH 2025 - 16th International Conference and Expo on 3D/4D Body Scanning, Data and Processing Technologies, Lugano, Switzerland, 21-22 Oct. 2025, #54
Details:
Proceedings: 3DBODY.TECH 2025, 21-22 Oct. 2025, Lugano, Switzerland
Paper/Presentation: #54
DOI: -
License/Copyright notice
Proceedings: © Hometrica Consulting - Dr. Nicola D'Apuzzo, Switzerland, hometrica.ch.
Authors retain all rights to individual papers, which are licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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