Multidimensional Adaptive Sampling and Reconstruction for Ray Tracing

1UC San Diego 2University of Virginia 3Harvard University

In ACM Transactions on Graphics (Proceedings of SIGGRAPH), 2008

Teaser
Chess scene showing depth of field. Our technique is able to sample and reconstruct the regions that are out of focus while the Mitchell sampler is noisy in these regions. Even though the out of focus areas are a small part of the image our sampler is able to produce an image with an MSE that is two times lower than the equal time rendered image with Mitchell's adaptive sampler. Due to our anisotropic reconstruction technique we can successfully reconstruct both blurry, out-of-focus regions (red), and sharp, in-focus regions (green). Without anisotropic reconstruction, the MSE of our method more than doubles.

Abstract

We present a new adaptive sampling strategy for ray tracing. Our technique is specifically designed to handle multidimensional sample domains, and it is well suited for efficiently generating images with effects such as soft shadows, motion blur, and depth of field. These effects are problematic for existing image based adaptive sampling techniques as they operate on pixels, which are possibly noisy results of a Monte Carlo ray tracing process. Our sampling technique operates on samples in the multidimensional space given by the rendering equation and as a consequence the value of each sample is noise-free. Our algorithm consists of two passes. In the first pass we adaptively generate samples in the multidimensional space, focusing on regions where the local contrast between samples is high. In the second pass we reconstruct the image by integrating the multidimensional function along all but the image dimensions. We perform a high quality anisotropic reconstruction by determining the extent of each sample in the multidimensional space using a structure tensor. We demonstrate our method on scenes with a 3 to 5 dimensional space, including soft shadows, motion blur, and depth of field. The results show that our method uses fewer samples than Mitchell's adaptive sampling technique while producing images with less noise.

Downloads

Text Reference

Toshiya Hachisuka, Wojciech Jarosz, Richard Peter Weistroffer, Kevin Dale, Greg Humphreys, Matthias Zwicker, Henrik Wann Jensen. Multidimensional Adaptive Sampling and Reconstruction for Ray Tracing. ACM Transactions on Graphics (Proceedings of SIGGRAPH), 27(3):33:1–33:10, August 2008.

BibTex Reference

@article{hachisuka08multidimensional,
    author = "Hachisuka, Toshiya and Jarosz, Wojciech and Weistroffer, Richard Peter and Dale, Kevin and Humphreys, Greg and Zwicker, Matthias and Jensen, Henrik Wann",
    title = "Multidimensional Adaptive Sampling and Reconstruction for Ray Tracing",
    journal = "ACM Transactions on Graphics (Proceedings of SIGGRAPH)",
    volume = "27",
    number = "3",
    month = "aug",
    year = "2008",
    pages = "33:1–33:10",
    doi = "10.1145/1360612.1360632"
}

Copyright Disclaimer

© The Author(s) / ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record is available at doi.acm.org.