Abstract: Systems, methods and articles of manufacture for rendering three-dimensional virtual environments using reversible jumps are disclosed herein. In one embodiment, mappings from random numbers to light paths are modeled as an explicit iterative random walk. Inverses of path construction techniques are employed to turn light transport paths back into the random numbers that produced them. In particular, such inverses may be used to extend the Multiplexed Metropolis Light Transport (MMLT) technique to perform path-invariant perturbations that produce a new path sample using a different path construction technique but preserve the path's geometry.

Abstract: Systems, methods and articles of manufacture for rendering three-dimensional virtual environments using reversible jumps are disclosed herein. In one embodiment, mappings from random numbers to light paths are modeled as an explicit iterative random walk. Inverses of path construction techniques are employed to turn light transport paths back into the random numbers that produced them. In particular, such inverses may be used to extend the Multiplexed Metropolis Light Transport (MMLT) technique to perform path-invariant perturbations that produce a new path sample using a different path construction technique but preserve the path's geometry.

Abstract: The disclosure provides an approach for denoising (also referred to as “filtering”) rendered images. In one embodiment, a denoising application takes as input rendered images and feature buffers that encode image information such as surface positions, surface depths, surface normals, surface albedos, and distances to the camera. For each pixel in a received image, the denoising application performs a first-order regression in a predefined neighborhood of the pixel to find a linear combination of pixel features that fits pixel colors in the predefined neighborhood. In such a first-order regression, the local regression weight of each pixel in the neighborhood may be determined using a metric which computes distances based on color values in patches around pixels being compared. In another embodiment, collaborative filtering may be performed in which filtered output from the first-order regression in each neighborhood is averaged with filtered output from overlapping neighborhoods to obtain a final output.

Abstract: The disclosure provides an approach for denoising (also referred to as “filtering”) rendered images. In one embodiment, a denoising application takes as input rendered images and feature buffers that encode image information such as surface positions, surface depths, surface normals, surface albedos, and distances to the camera. For each pixel in a received image, the denoising application performs a first-order regression in a predefined neighborhood of the pixel to find a linear combination of pixel features that fits pixel colors in the predefined neighborhood. In such a first-order regression, the local regression weight of each pixel in the neighborhood may be determined using a metric which computes distances based on color values in patches around pixels being compared. In another embodiment, collaborative filtering may be performed in which filtered output from the first-order regression in each neighborhood is averaged with filtered output from overlapping neighborhoods to obtain a final output.