Abstract: Embodiments of the present disclosure relate to training methods for training mixed-precision neural networks, particularly neural material networks. In an embodiment, a mixed-precision neural material network includes input, hidden, and output layers having different numerical precisions that is trained or fine-tuned to encode and/or decode compact latent code representations.

Abstract: Embodiments of the present disclosure relate to training a neural network to represent a specular material. Learning an overall shape and appearance of a specular lobe is improved by randomly adjusting the directions of at least one of the ray surface incident and exit vectors at a point intersected by the ray within a corresponding volume and shrinking a dimension of the base of the volume as training progresses.

Abstract: Embodiments of the present disclosure relate to robust training methods for small neural networks, particularly neural material networks. High variance and instability in training small networks is reduced by creating multiple instances with distinct parameter sets, training the instances in parallel, and progressively pruning instances with higher loss values.

Abstract: Disclosed are apparatuses, systems, and techniques to render images with global illumination using efficient ray tracing, light source identification, and reservoir resampling that deploys temporal and spatial reservoirs.

Abstract: Apparatuses, systems, and techniques to ray trace caustics in a scene using feedback of photon information between frames. In at least one embodiment, photon tracing determines photon footprints in an individual frame as a result of individual photons interacting with one or more caustic-casting objects in that frame, and uses those photon footprints to facilitate photon tracing in subsequent frames.

Abstract: Disclosed are apparatuses, systems, and techniques to render images with global illumination using efficient ray tracing, light source identification, and reservoir resampling that deploys temporal and spatial reservoirs.

Abstract: Apparatuses, systems, and techniques to ray trace caustics in a scene using feedback of photon information between frames. In at least one embodiment, photon tracing determines photon footprints in an individual frame as a result of individual photons interacting with one or more caustic-casting objects in that frame, and uses those photon footprints to facilitate photon tracing in subsequent frames.

Abstract: Disclosed are apparatuses, systems, and techniques to render images with global illumination using efficient ray tracing, light source identification, and reservoir resampling that deploys temporal and spatial reservoirs.

Abstract: Approaches presented herein provide for the maintaining of fine details that might be removed by a denoiser used to reduce an amount of noise in an image. An input image can be provided to a denoiser, and can also can be simultaneously processed to extract pixel data that may correspond to fine detail or high frequency features. Individual pixels of an image can have a value determined for a material property sampled for that pixel location, and that value can be compared against an average material property value determined for neighboring pixels. The ratio of material values can be multiplied by the value of a corresponding pixel of the denoised image, for any or all pixel locations, to obtain final pixel values for an output image that include less noise than the original image but represent fine detail that may otherwise have been lost during the denoising process.

Abstract: Disclosed are apparatuses, systems, and techniques to render images with global illumination using efficient ray tracing, light source identification, and reservoir resampling that deploys temporal and spatial reservoirs.

Abstract: Disclosed are apparatuses, systems, and techniques to render images with global illumination using efficient ray tracing, light source identification, and reservoir resampling that deploys temporal and spatial reservoirs.