  The variance reduction speed of physically-based rendering is heavily
affected by the adopted importance sampling technique. In this paper we propose
a novel online framework to learn the spatial-varying density model with a
single small neural network using stochastic ray samples. To achieve this task,
we propose a novel closed-form density model called the normalized anisotropic
spherical gaussian mixture, that can express complex irradiance fields with a
small number of parameters. Our framework learns the distribution in a
progressive manner and does not need any warm-up phases. Due to the compact and
expressive representation of our density model, our framework can be
implemented entirely on the GPU, allowing it produce high quality images with
limited computational resources.
