  Recently, deep learning-based denoising approaches have led to dramatic
improvements in low sample-count Monte Carlo rendering. These approaches are
aimed at path tracing, which is not ideal for simulating challenging light
transport effects like caustics, where photon mapping is the method of choice.
However, photon mapping requires very large numbers of traced photons to
achieve high-quality reconstructions. In this paper, we develop the first deep
learning-based method for particle-based rendering, and specifically focus on
photon density estimation, the core of all particle-based methods. We train a
novel deep neural network to predict a kernel function to aggregate photon
contributions at shading points. Our network encodes individual photons into
per-photon features, aggregates them in the neighborhood of a shading point to
construct a photon local context vector, and infers a kernel function from the
per-photon and photon local context features. This network is easy to
incorporate in many previous photon mapping methods (by simply swapping the
kernel density estimator) and can produce high-quality reconstructions of
complex global illumination effects like caustics with an order of magnitude
fewer photons compared to previous photon mapping methods.
