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.
Highlights
Photorealistic effects such as depth-of-field and motion blur require heavy computation because they involve intricate integrals over a 5D domain composed of the image, lens, and time
Recent work has leveraged the frequency content of radiance for the faster rendering of individual effects such as depth-of-field [Soler et al 2009], motion blur [Egan et al 2009], soft shadows [Egan et al 2011b] and directional occlusion [Egan et al 2011a]
We introduce a new approach that can predict the frequency effect of most aspects of light transport in dynamic scenes in a unified manner
Summary
Photorealistic effects such as depth-of-field and motion blur require heavy computation because they involve intricate integrals over a 5D domain composed of the image, lens, and time. Recent work has leveraged the frequency content of radiance for the faster rendering of individual effects such as depth-of-field [Soler et al 2009], motion blur [Egan et al 2009], soft shadows [Egan et al 2011b] and directional occlusion [Egan et al 2011a]. These solutions are limited in scope because the general derivation of spectrum prediction equations is hard.
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