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Disc: Approximative Nearest Neighbor Search using Ellipsoids for Photon Mapping on GPUs

Marcus Bergholm, Viktor Kronvall
KTH, School of Computer Science and Communication (CSC)
KTH, 2016
@article{bergholm2016disc,

   title={Disc: Approximative Nearest Neighbor Search using Ellipsoids for Photon Mapping on GPUs},

   author={Bergholm, Marcus and Kronvall, Viktor},

   year={2016}

}

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Recent development in Graphics Processing Units (GPUs) has enabled inexpensive high-performance computing for general-purpose applications. The K-Nearest Neighbors problem is widely used in applications ranging from classification to gathering of photons in the Photon Mapping algorithm. Using the euclidean distance measure when gathering photons can cause false bleeding of colors between surfaces. Ellipsoidical search boundaries for photon gathering are shown to reduce artifacts due to this false bleeding. Shifted Sorting has been found to yield high performance on GPUs while simultaneously retaining a high approximation rate. This study presents an algorithm for approximatively solving the K-Nearest Neighbors problem modified to use a distance measure creating an ellipsoidical search boundary. The ellipsoidical search boundary is used to alleviate the issue of false bleeding of colors between surfaces in Photon Mapping. The Approximative K-Nearest Neighbors algorithm presented is a modification of the Shifted Sorting algorithm. The algorithm is found to be highly parallelizable and performs to a factor of 86% queries processed per millisecond compared to a reference implementation using spherical search boundaries implied by the euclidean distance. The rate of compression from spherical to ellipsoidical search boundary is appropriately chosen in the range 3.0 to 7.0. The algorithm is found to scale well in respect to increases in both number of data points and number of query points.
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