通過學習輻射傳輸梯度的方式，使用次表面散射進行神經照明。

Neural Relighting with Subsurface Scattering by Learning the Radiance Transfer Gradient

June 15, 2023

作者: Shizhan Zhu, Shunsuke Saito, Aljaz Bozic, Carlos Aliaga, Trevor Darrell, Christop Lassner

cs.AI

摘要

在不同照明條件下重建和重新照明物體和場景具有挑戰性：現有的神經渲染方法通常無法處理材料和光線之間的複雜交互作用。將預先計算的輻射傳輸技術納入可以實現全局照明，但仍然難以應對具有次表面散射效應的材料。我們提出了一個新穎的框架，通過體積渲染學習輻射傳輸場，並利用各種外觀線索來端對端地優化幾何。該框架擴展了重新照明和重建能力，以以數據驅動的方式處理更廣泛範圍的材料。所產生的模型在現有和新的條件下產生合理的渲染結果。我們將公開發布代碼和一個包含具有次表面散射效應的物體的新穎光線舞台數據集。

English

Reconstructing and relighting objects and scenes under varying lighting conditions is challenging: existing neural rendering methods often cannot handle the complex interactions between materials and light. Incorporating pre-computed radiance transfer techniques enables global illumination, but still struggles with materials with subsurface scattering effects. We propose a novel framework for learning the radiance transfer field via volume rendering and utilizing various appearance cues to refine geometry end-to-end. This framework extends relighting and reconstruction capabilities to handle a wider range of materials in a data-driven fashion. The resulting models produce plausible rendering results in existing and novel conditions. We will release our code and a novel light stage dataset of objects with subsurface scattering effects publicly available.