  We present a new method for estimating the Neural Reflectance Field (NReF) of
an object from a set of posed multi-view images under unknown lighting. NReF
represents 3D geometry and appearance of objects in a disentangled manner, and
are hard to be estimated from images only. Our method solves this problem by
exploiting the Neural Radiance Field (NeRF) as a proxy representation, from
which we perform further decomposition. A high-quality NeRF decomposition
relies on good geometry information extraction as well as good prior terms to
properly resolve ambiguities between different components. To extract
high-quality geometry information from radiance fields, we re-design a new
ray-casting based method for surface point extraction. To efficiently compute
and apply prior terms, we convert different prior terms into different type of
filter operations on the surface extracted from radiance field. We then employ
two type of auxiliary data structures, namely Gaussian KD-tree and octree, to
support fast querying of surface points and efficient computation of surface
filters during training. Based on this, we design a multi-stage decomposition
optimization pipeline for estimating neural reflectance field from neural
radiance fields. Extensive experiments show our method outperforms other
state-of-the-art methods on different data, and enable high-quality free-view
relighting as well as material editing tasks.
