An Index of Refraction Adaptive Neural Refractive Radiance Field for Transparent Scenes

Reconstructions and novel view syntheses of transparent object scenes are of great significance in numerous fields such as design, animation, and scientific research. The refraction of transparent objects brings challenges to the reconstruction of the geometry and radiance fields. Traditional photogrammetry methods and Neural Radiance Fields (NeRFs) establish geometric relationships based on the rectilinear propagation of light rays and thus fail in refraction scenes. In addition, different transparent objects have significantly different indexes of refraction (IORs) due to differences in materials, which pose challenges to the training of refraction radiance fields based on ray tracing. We propose a refractive radiance field method for the novel view synthesis of transparent object scenes. The method can reconstruct the geometry and radiance field using only a sequence of scene images of transparent objects, and it can automatically search for the optimal IOR without prior information. Our method consists of three stages, including geometric reconstruction, radiance field training, and IOR optimization. We verified our method on different datasets. In terms of its indicators, it is close to previous refraction radiance fields and other relatively advanced transparent scene rendering methods, while possessing higher flexibility.