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Efficient and accurate BRDF acquisition of real world materials is achallenging research problem that requires sampling millions of incident lightand viewing directions. To accelerate the acquisition process, one needs tofind a minimal set of sampling directions such that the recovery of the fullBRDF is accurate and robust given such samples. In this paper, we formulateBRDF acquisition as a compressed sensing problem, where the sensing operator isone that performs sub-sampling of the BRDF signal according to a set of optimalsample directions. To solve this problem, we propose the Fast and RobustOptimal Sampling Technique (FROST) for designing a provably optimalsub-sampling operator that places light-view samples such that the recoveryerror is minimized. FROST casts the problem of designing an optimalsub-sampling operator for compressed sensing into a sparse representationformulation under the Multiple Measurement Vector (MMV) signal model. Theproposed reformulation is exact, i.e. without any approximations, hence itconverts an intractable combinatorial problem into one that can be solved withstandard optimization techniques. As a result, FROST is accompanied by strongtheoretical guarantees from the field of compressed sensing. We perform athorough analysis of FROST-BRDF using a 10-fold cross-validation with publiclyavailable BRDF datasets and show significant advantages compared to thestate-of-the-art with respect to reconstruction quality. Finally, FROST issimple, both conceptually and in terms of implementation, it producesconsistent results at each run, and it is at least two orders of magnitudefaster than the prior art.

FROST-BRDF: A Fast and Robust Optimal Sampling Technique for BRDF Acquisition