首个基于深度隐式表示的女性乳房3D建模方法，可从单张图像重建精细乳房形状。
iRBSM: A Deep Implicit 3D Breast Shape Model
- 采用隐式神经表示替代传统PCA，直接处理原始3D扫描数据
- 无需耗时的非刚性配准，能精确还原乳头、肚脐等细节结构
- 支持单图3D重建，适合医学影像与个性化设计应用
我们提出了首个基于深度隐式表示的女性乳房3D形状模型，基于并改进了近期提出的雷根斯堡乳房形状模型（RBSM）。相较于基于PCA的前代模型，本模型采用隐式神经表示，可直接在原始3D乳房扫描数据上训练，避免了对计算量大的非刚性配准的需求——这一任务对缺乏特征的乳房形状尤为困难。所提出的iRBSM模型能够捕捉包括乳头和肚脐在内的精细表面几何结构，表达能力更强，在多种表面重建任务中表现优于RBSM。最后，基于iRBSM，我们实现了一个原型应用，仅需单张图像即可完成乳房3D形状重建。模型与代码已公开于https://rbsm.re-mic.de/implicit。
原文摘要 · Abstract (English)
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.
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