iRBSM: A Deep Implicit 3D Breast Shape Model
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910750575951872 |
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| author | Weiherer, Maximilian von Riedheim, Antonia Brébant, Vanessa Egger, Bernhard Palm, Christoph |
| author_facet | Weiherer, Maximilian von Riedheim, Antonia Brébant, Vanessa Egger, Bernhard Palm, Christoph |
| contents | We present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the need for computationally demanding non-rigid registration -- a task that is particularly difficult for feature-less breast shapes. The resulting model, dubbed iRBSM, captures detailed surface geometry including fine structures such as nipples and belly buttons, is highly expressive, and outperforms the RBSM on different surface reconstruction tasks. Finally, leveraging the iRBSM, we present a prototype application to 3D reconstruct breast shapes from just a single image. Model and code publicly available at https://rbsm.re-mic.de/implicit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13244 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | iRBSM: A Deep Implicit 3D Breast Shape Model Weiherer, Maximilian von Riedheim, Antonia Brébant, Vanessa Egger, Bernhard Palm, Christoph Computer Vision and Pattern Recognition We present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the need for computationally demanding non-rigid registration -- a task that is particularly difficult for feature-less breast shapes. The resulting model, dubbed iRBSM, captures detailed surface geometry including fine structures such as nipples and belly buttons, is highly expressive, and outperforms the RBSM on different surface reconstruction tasks. Finally, leveraging the iRBSM, we present a prototype application to 3D reconstruct breast shapes from just a single image. Model and code publicly available at https://rbsm.re-mic.de/implicit. |
| title | iRBSM: A Deep Implicit 3D Breast Shape Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.13244 |