Rendering and Sampling

Summary: We present gradient-domain path reusing, a novel unbiased Monte-Carlo rendering technique, which merges the concept of path reusing with gradient-domain rendering. It outperforms conventional and gradient-domain path, and traditional path reusing tracing by up to almost an order of magnitude.

Author(s): Pablo Bauszat, TU Braunschweig, TU Delft
Elmar Eisemann, TU Delft
Victor Petitjean, TU Delft

Speaker(s): Victor Petitjean, TU Delft, The Netherlands

Summary: We present a direct-to-indirect transport technique that enables accurate real- time rendering of indirect illumination in mostly static scenes of complexity on par with modern games while supporting fully dynamic lights, cameras and diffuse surface materials.

Author(s): Ari Silvennoinen, Aalto University
Jaakko Lehtinen, NVIDIA Research, Aalto University

Speaker(s): Ari Silvennoinen, Aalto University, Remedy Entertainment

Summary: We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks.

Author(s): Simon Kallweit, Disney Research, ETH Zurich
Thomas Müller, Disney Research, ETH Zurich
Brian McWilliams, Disney Research
Markus Gross, Disney Research, ETH Zurich
Jan Novák, Disney Research

Speaker(s): Simon Kallweit, Disney Research / ETH Zürich

Summary: We present a fast, precomputation-based algorithm for rendering both single and multiple scattering in fabrics with repeating structure illuminated by directional and spherical Gaussian lights. We show how to decompose the problem and pick the right approximations to achieve very high accuracy, with significant performance gains over path tracing

Author(s): Pramook Khungurn, Cornell University
Rundong Wu, Cornell University
James Noeckel, Cornell University
Steve Marschner, Cornell University
Kavita Bala, Cornell University

Speaker(s): Pramook Khungurn, Cornell University

Summary: We propose an adaptive version of Lloyd's optimization method that distributes points based on Voronoi Diagrams. Our inspiration comes from the Linde-Buzo-Gray-Algorithm in Vector quantization, which dynamically splits and merges Voronoi cells until a given goal is reached.

Author(s): Marc Spicker, University of Konstanz
Oliver Deussen, University of Konstanz
Qian Zheng, University of Konstanz

Speaker(s): Marc Spicker, University of Konstanz, Germany