Approximating Reflectance Functions using Neural Networks

摘要

We present a new representation for the storage and reconstruction of arbitrary reflectance functions. This non-linear representation, based on a neural network model, accurately captures the spectral and spatial variation of these functions. It is both computationally efficient and concise, yet expressive. We reconstruct the subtle reflection characteristics of an analytic reflection model as well as measured and simulated reflection data