用少量图像实现高保真半透明材质重建，大幅减少真实拍摄需求。
DIAMOND-SSS: Diffusion-Augmented Multi-View Optimization for Data-efficient SubSurface Scattering
- 基于扩散模型生成逼真补全图像，仅需10张输入图
- 通过几何一致性损失稳定稀疏条件下的重建效果
- 适合需要低数据采集成本的3D材质建模场景
半透明材质（如蜡、玉石、大理石和皮肤）具有柔和阴影、颜色渗透和漫反射光泽等特征。神经渲染中建模这些效果仍具挑战，因光传输复杂且需密集多视角、多光源数据集（通常超过100视角和112 OLATs）。我们提出DIAMOND-SSS，一种从极稀疏监督中实现高保真半透明重建的数据高效框架，即使仅使用十张图像也可完成。通过微调扩散模型进行新视角合成与再光照，以估计的几何结构为条件，训练数据不足原数据集的7%，生成的逼真增广图像可替代高达95%缺失的采集数据。为在稀疏或合成监督下稳定重建，引入光照无关的几何先验：多视角轮廓一致性损失与深度一致性损失。在所有稀疏程度下，DIAMOND-SSS在可再光照高斯渲染中达到最优质量，相比SSS-3DGS将真实采集需求降低最多达90%。
原文摘要 · Abstract (English)
Subsurface scattering (SSS) gives translucent materials -- such as wax, jade, marble, and skin -- their characteristic soft shadows, color bleeding, and diffuse glow. Modeling these effects in neural rendering remains challenging due to complex light transport and the need for densely captured multi-view, multi-light datasets (often more than 100 views and 112 OLATs). We present DIAMOND-SSS, a data-efficient framework for high-fidelity translucent reconstruction from extremely sparse supervision -- even as few as ten images. We fine-tune diffusion models for novel-view synthesis and relighting, conditioned on estimated geometry and trained on less than 7 percent of the dataset, producing photorealistic augmentations that can replace up to 95 percent of missing captures. To stabilize reconstruction under sparse or synthetic supervision, we introduce illumination-independent geometric priors: a multi-view silhouette consistency loss and a multi-view depth consistency loss. Across all sparsity regimes, DIAMOND-SSS achieves state-of-the-art quality in relightable Gaussian rendering, reducing real capture requirements by up to 90 percent compared to SSS-3DGS.
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