The Community for Technology Leaders
Green Image
Issue No. 10 - October (2011 vol. 33)
ISSN: 0162-8828
pp: 2122-2128
Jiamin Bai , University of California, Berkeley, Berkeley
Manmohan Chandraker , University of California, Berkeley, Berkeley
Tian-Tsong Ng , Institute for Infocomm Research, Singapore
Ravi Ramamoorthi , University of California, Berkeley, Berkeley
ABSTRACT
Inverse light transport seeks to undo global illumination effects, such as interreflections, that pervade images of most scenes. This paper presents the theoretical and computational foundations for inverse light transport as a dual of forward rendering. Mathematically, this duality is established through the existence of underlying Neumann series expansions. Physically, it can be shown that each term of our inverse series cancels an interreflection bounce, just as the forward series adds them. While the convergence properties of the forward series are well known, we show that the oscillatory convergence of the inverse series leads to more interesting conditions on material reflectance. Conceptually, the inverse problem requires the inversion of a large light transport matrix, which is impractical for realistic resolutions using standard techniques. A natural consequence of our theoretical framework is a suite of fast computational algorithms for light transport inversion—analogous to finite element radiosity, Monte Carlo and wavelet-based methods in forward rendering—that rely at most on matrix-vector multiplications. We demonstrate two practical applications, namely, separation of individual bounces of the light transport and fast projector radiometric compensation, to display images free of global illumination artifacts in real-world environments.
INDEX TERMS
Light transport, rendering equation, inverse light transport, duality theory, interreflections, radiometric compensation.
CITATION
Jiamin Bai, Manmohan Chandraker, Tian-Tsong Ng, Ravi Ramamoorthi, "On the Duality of Forward and Inverse Light Transport", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 2122-2128, October 2011, doi:10.1109/TPAMI.2011.124
101 ms
(Ver )