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5D Covariance tracing for efficient defocus and motion blur

Authors:

Laurent Belcour,

Nicolas Holzschuch,

Fredo DurandAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 32, Issue 3

Article No.: 31, Pages 1 - 18

https://doi.org/10.1145/2487228.2487239

Published: 04 July 2013 Publication History

Abstract

The rendering of effects such as motion blur and depth-of-field requires costly 5D integrals. We accelerate their computation through adaptive sampling and reconstruction based on the prediction of the anisotropy and bandwidth of the integrand. For this, we develop a new frequency analysis of the 5D temporal light-field, and show that first-order motion can be handled through simple changes of coordinates in 5D. We further introduce a compact representation of the spectrum using the covariance matrix and Gaussian approximations. We derive update equations for the 5 × 5 covariance matrices for each atomic light transport event, such as transport, occlusion, BRDF, texture, lens, and motion. The focus on atomic operations makes our work general, and removes the need for special-case formulas. We present a new rendering algorithm that computes 5D covariance matrices on the image plane by tracing paths through the scene, focusing on the single-bounce case. This allows us to reduce sampling rates when appropriate and perform reconstruction of images with complex depth-of-field and motion blur effects.

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Index Terms

5D Covariance tracing for efficient defocus and motion blur
1. Computing methodologies
  1. Computer graphics
    1. Rendering
      1. Ray tracing

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 32, Issue 3

June 2013

129 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2487228

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Copyright © 2013 ACM.

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Publication History

Published: 04 July 2013

Accepted: 01 February 2013

Revised: 01 November 2012

Received: 01 January 2012

Published in TOG Volume 32, Issue 3

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https://dl.acm.org/doi/10.1145/3687934
Zhang SLin DKettunen MYuksel CWyman C(2024)Area ReSTIR: Resampling for Real-Time Defocus and AntialiasingACM Transactions on Graphics10.1145/365821043:4(1-13)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3658210
Yu ZZhang CNowrouzezahrai DDong ZZhao S(2022)Efficient Differentiation of Pixel Reconstruction Filters for Path-Space Differentiable RenderingACM Transactions on Graphics10.1145/3550454.355550041:6(1-16)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1145/3550454.3555500
Kivi PMakitalo MZadnik JIkkala JVadakital VJaaskelainen P(2022)Real-Time Rendering of Point Clouds With Photorealistic Effects: A SurveyIEEE Access10.1109/ACCESS.2022.314676810(13151-13173)Online publication date: 2022
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