iRBSM: A Deep Implicit 3D Breast Shape Model

2024/12/17 by Maximilian Weiherer, Weiherer, Maximilian, Antonia von Riedheim +7 · 1 voice · 1 citation
Computer Science · Engineering · #3D Shape Modeling and Analysis #AI in cancer detection #Medical Imaging and Analysis #cs.CV

paper · pdf · doi:10.48550/arxiv.2412.13244

openalex publication_date 2024/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• Learning Neural Parametric 3D Breast Shape Models for Metrical Surface Reconstruction From Monocular RGB Videos

Discussions

• Happy to share our latest 3D generative breast model: the *implicit* RBSM, or iRBSM for short. As opposed to its PCA-based predecessor, the iRBSM leverages implicit neural representations, yielding a [bsky, 10 points, 1 comments]

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