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We present a new method for estimating the Neural Reflectance Field (NReF) ofan object from a set of posed multi-view images under unknown lighting. NReFrepresents 3D geometry and appearance of objects in a disentangled manner, andare hard to be estimated from images only. Our method solves this problem byexploiting the Neural Radiance Field (NeRF) as a proxy representation, fromwhich we perform further decomposition. A high-quality NeRF decompositionrelies on good geometry information extraction as well as good prior terms toproperly resolve ambiguities between different components. To extracthigh-quality geometry information from radiance fields, we re-design a newray-casting based method for surface point extraction. To efficiently computeand apply prior terms, we convert different prior terms into different type offilter operations on the surface extracted from radiance field. We then employtwo type of auxiliary data structures, namely Gaussian KD-tree and octree, tosupport fast querying of surface points and efficient computation of surfacefilters during training. Based on this, we design a multi-stage decompositionoptimization pipeline for estimating neural reflectance field from neuralradiance fields. Extensive experiments show our method outperforms otherstate-of-the-art methods on different data, and enable high-quality free-viewrelighting as well as material editing tasks.

Estimating Neural Reflectance Field from Radiance Field using Tree Structures