Title
5D Covariance tracing for efficient defocus and motion blur
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.
Year
DOI
Venue
2013
10.1145/2487228.2487239
ACM Trans. Graph.
Keywords
Field
DocType
atomic operation,complex depth-of-field,covariance matrix,first-order motion,adaptive sampling,efficient defocus,new frequency analysis,new rendering algorithm,motion blur effect,atomic light transport event,motion blur
Computer vision,Fourier analysis,Adaptive sampling,Motion blur,Artificial intelligence,Global illumination,Rendering (computer graphics),Tracing,Mathematics,Covariance,Computation
Journal
Volume
Issue
ISSN
32
3
0730-0301
Citations 
PageRank 
References 
27
0.84
46
Authors
5
Name
Order
Citations
PageRank
Laurent Belcour1729.33
Cyril Soler252831.97
Kartic Subr319915.28
Nicolas Holzschuch460740.15
Frédo Durand58625414.94