Rendering - Experimental Ideas & Implementations 2018

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https://diglib.eg.org/handle/10.2312/2632569

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Browsing Rendering - Experimental Ideas & Implementations 2018 by Subject "Ray tracing"

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    An Improved Multiple Importance Sampling Heuristic for Density Estimates in Light Transport Simulations
    (The Eurographics Association, 2018) Jendersie, Johannes; Grosch, Thorsten; Jakob, Wenzel and Hachisuka, Toshiya
    Vertex connection and merging (VCM) is one of the most robust light transport simulation algorithms developed so far. It combines bidirectional path tracing with photon mapping using multiple importance sampling (MIS). However, there are scene setups where the current weight computation is not optimal. If different merge events on a single path have roughly the same likelihood to be found, but different photon densities, this leads to high variance samples. We show how to improve the heuristic for density estimation events to overcome this issue by including the photon density into the MIS computation. This leads to a faster convergence in VCM and related techniques. The proposed change is easy to implement and is orthogonal to other improvements of the algorithm.
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    Matrix Bidirectional Path Tracing
    (The Eurographics Association, 2018) Chaitanya, Chakravarty Reddy Alla; Belcour, Laurent; Hachisuka, Toshiya; Premoze, Simon; Pantaleoni, Jacopo; Nowrouzezahrai, Derek; Jakob, Wenzel and Hachisuka, Toshiya
    Sampled paths in Monte Carlo ray tracing can be arbitrarily close to each other due to its stochastic nature. Such clumped samples in the path space tend to contribute little toward an accurate estimate of each pixel. Bidirectional light transport methods make this issue further complicated since connecting paths of sampled subpaths can be arbitrarily clumped again. We propose a matrix formulation of bidirectional light transport that enables stratification (and low-discrepancy sampling) in this connection space. This stratification allows us to distribute computation evenly across contributing paths in the image, which is not possible with standard bidirectional or Markov chain solutions. Each element in our matrix formulation represents a pair of connected eye- and light-subpaths. By carefully reordering these elements, we build a 2D space where equally contributing paths are distributed coherently. We devise an unbiased rendering algorithm that leverages this coherence to effectively sample path space, consistently achieving a 2-3x speedup in radiometrically complex scenes compared to the state-of-the-art.
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    Primary Sample Space Path Guiding
    (The Eurographics Association, 2018) Guo, Jerry Jinfeng; Bauszat, Pablo; Bikker, Jacco; Eisemann, Elmar; Jakob, Wenzel and Hachisuka, Toshiya
    Guiding path tracing in light transport simulation has been one of the practical choices for variance reduction in production rendering. For this purpose, typically structures in the spatial-directional domain are built. We present a novel scheme for unbiased path guiding. Different from existing methods, we work in primary sample space. We collect records of primary samples as well as the luminance that the resulting path contributes and build a multiple dimensional structure, from which we derive random numbers that are fed into the path tracer. This scheme is executed completely outside the rendering kernel. We demonstrate that this method is practical and efficient. We manage to reduce variance and zero radiance paths by only working in the primary sample space.