通过学习表面像素响应，实现高精度次表面散射建模与任意光照重渲染。
Neural Acquisition & Representation of Subsurface Scattering
- 基于U-Net的CNN学习表面各点像素足迹响应。
- 重建密集像素足迹，支持任意高分辨率投影光场重渲染。
- 模型可泛化至未见材质，适用于多物体多视角训练。
我们提出一种方法，通过学习物体表面每一点的像素足迹响应，以高精度获取和估计光传输中的次表面散射特性。重建过程以3D扫描数据为输入，利用相位移条纹投影（PSP）的双目投影-相机系统高效采集多种散射物体的数据。重建得到的密集像素足迹可用于任意高分辨率投影图案的重光照。最终输出为重光照后的颜色图像。与真实世界捕获图像的定性和定量对比显示，预测的足迹几乎与实际响应一致。同一模型在多个物体、多视角上训练，使学习到的表征可泛化至未见过的次表面散射材料。
原文摘要 · Abstract (English)
We present a method to acquire and estimate the sub-surface scattering properties of light transport at a highly detailed level by learning the pixel footprint response at each point on the object surface. The reconstruction leverages 3D scanning techniques as input to a U-Net CNN. A stereo projector-camera setup using phase-shifted profilometry (PSP) patterns efficiently captures the data for a variety of scattering objects. Reconstructing dense pixel footprints allows for relighting with arbitrary high-resolution projector patterns. The final output is a relit color image. Qualitative and quantitative comparison against illuminated real-world captured images demonstrate that the predicted footprints are almost identical to the actual responses. The same model is trained for multiple views across multiple objects such that the learned representations can be used to generalize to unseen sub-surface scattering materials as well.
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