In this paper we propose a new framework for capturing light transport data of a real scene, based on the recently developed theory of compressive sensing. Compressive sensing offers a solid mathematical framework to infer a sparse signal from a limited number of non-adaptive measurements. Besides introducing compressive sensing for fast acquisition of light transport to computer graphics, we develop several innovations that address specific challenges for image-based relighting, and which may have broader implications. We develop a novel hierarchical decoding algorithm that improves reconstruction quality by exploiting inter-pixel coherency relations. Additionally, we design new non-adaptive illumination patterns that minimize measurement noise and further improve reconstruction quality. We illustrate our framework by capturing detailed highresolution reflectance fields for image-based relighting.
We have recently become aware of concurrent and independent work performed by Pradeep Sen and Soheil Darabi at the University of New Mexico that applies compressive sensing to Dual Photography. This work tackles the problem of measuring high resolution reflectance fields in a similar manner. The main difference is that they directly apply Bernoulli noise measurement patterns that work well in a Dual Photography setup.
Oops: An unfortunate error crept into both the TOG paper and the technical report. Eq. 15 is obviously incorrect. However, one can still apply Eq. 16 to compute a sparse representation of the difference.
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