| dc.description.abstract | Monte Carlo path tracing is a popular 3D rendering technique in computer graphics, but it often requires a costly tradeoff between the amount of noise in the image and computation time. Therefore, it is useful to attempt to “smooth out” a noisy image, typically by constructing new data between the samples or applying filters to the image. In this work, we investigate the feasibility of training a neural network to implicitly represent the radiance of a fixed-viewpoint scene as a continuous function. We implement the neural network using a multilayer perceptron network and train it on a sparsely sampled image that is generated by an offline Monte Carlo renderer. This training data uses the (x, y) coordinate of each sample on the image plane as inputs and the RGB color of the sample as outputs. Additionally, we provide the network with the surface normal, depth, and albedo of the first ray intersection as extra inputs alongside the pixel coordinates. These extra input dimensions improve the quality of the implicit representation by helping the network account for changes in depth, normal, and diffuse color. Once the network is trained on the sparsely sampled scene, we can densely sample the network many times per pixel to create the final denoised image. We find that this network can quickly learn and denoise images in scenes with soft lighting and glossy reflections, and it can easily handle discontinuities in depth, normal, and diffuse color with just a small amount of training. | |
