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Precomputing high-quality sample points has been shown to be a useful technique for Monte Carlo integration in rendering; doing so allows optimizing properties of the points without the performance constraints of generating samples during rendering. A particularly useful property to incorporate is stratification across elementary intervals, which has been shown to reduce error in Monte Carlo integration. This is a key property of the recently-introduced progressive multi-jittered, pmj02 and pmj02bn points [Christensen et al. 2018]. For generating such sets of sample points, it is important to be able to efficiently choose new samples that are not in elementary intervals occupied by existing samples. Random search, while easy to implement, quickly becomes infeasible after a few thousand points. We describe an algorithm that efficiently generates 2D sample points that are stratified with respect to sets of elementary intervals. If a total of n sample points are being generated, then for each sample, our algorithm uses O(n^1/2) time to build a data structure that represents the regions where a next sample may be placed. Given this data structure, valid samples can be generated in O(1) time. We demonstrate the utility of our method by generating much larger sets of pmj02bn points than were feasible previously

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Wednesday, February 27, 2019

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