Material Transforms from Disentangled NeRF Representations

TL;DR

This paper introduces a novel method for transferring material transformations across different scenes using disentangled NeRF representations, enabling realistic rendering of effects like wetness and coating in unseen scenes.

Contribution

The paper presents a new approach to learn and apply material transformations across scenes using BRDF mapping in disentangled NeRFs, which was not previously possible.

Findings

Effective transfer of material transformations demonstrated on synthetic and real scenes.

Method can learn various transformations such as wetness, painting, and coating.

Results show high versatility and potential for practical graphics applications.

Abstract

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