It is generally accepted that Poisson disk sampling provides great properties in various applications in computer graphics. We present KD-tree based randomized tiling (KDRT), an efficient method to generate maximal Poisson-disk samples by replicating and conquering tiles clipped from a pattern of very small size. Our method is a two-step process: first, randomly clipping tiles from an MPS(Maximal Poisson-disk Sample) pattern, and second, conquering these tiles together to form the whole sample plane. The results showed that this method can efficiently generate maximal Poisson-disk samples with very small trade-off in bias error. There are two main contributions of this paper: First, a fast and robust Poisson-disk sample generation method is presented; Second, this method can be used to combine several groups of independently generated sample patterns to form a larger one, thus can be applied as a general parallelization scheme of any MPS methods.
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