通过解耦的NeRF表示，实现不同场景间材质变化的跨场景迁移。
Material Transforms from Disentangled NeRF Representations
- 基于解耦NeRF学习不同光照条件下材质的BRDF映射关系。
- 可在未见场景中以任意强度渲染湿、涂装、涂层等材质变化。
- 适合计算机图形学中材质编辑与真实感渲染的应用场景。
本文提出一种新颖方法，实现跨场景的材质变换迁移。基于解耦的神经辐射场（NeRF）表示，该方法从不同条件下的场景对（如干/湿）中学习双向反射分布函数（BRDF）的映射关系。所学变换可应用于具有相似材质的未见场景，从而以任意强度有效渲染材质变化。在合成场景和真实物体上的大量实验验证了该方法的有效性，能够学习湿化、涂装、涂层等多种变换。结果表明，该方法兼具通用性与实际应用潜力。代码及合成/真实数据集已公开于 https://github.com/astra-vision/BRDFTransform。
原文摘要 · Abstract (English)
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
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