In this paper, we first propose a novel method for transferring material transformations across different scenes. Building on disentangled Neural Radiance Field (NeRF) representations, our approach learns to map Bidirectional Reflectance Distribution Functions (BRDF) from pairs of scenes observed in varying conditions, such as dry and wet. The learned transformations can then be applied to unseen scenes with similar materials, therefore effectively rendering the transformation learned with an arbitrary level of intensity. Extensive experiments on synthetic scenes and real-world objects validate the effectiveness of our approach, showing that it can learn various transformations such as wetness, painting, coating, etc. Our results highlight not only the versatility of our method but also its potential for practical applications in computer graphics. We publish our method implementation, along with our synthetic/real datasets on https://github.com/astra-vision/BRDFTransform

本文提出了一种新颖的方法，用于在不同场景中转移材料变换，填补了现有研究中的空白。该方法基于解耦的神经辐射场（NeRF）表示，能够从不同条件下观测到的场景对中学习双向反射分布函数（BRDF）并将其应用于未知场景。实验结果验证了该方法在执行材料变换方面的有效性，为计算机图形学的实际应用提供了潜在的影响。

基于解耦NeRF表示的材料变换