Point Sampling With General Noise Spectrum
Abstract: Point samples with different spectral noise properties (often deﬁned using color names such as white, blue, green, and red) are important for many science and engineering disciplines including computer graphics. While existing techniques can easily produce white and blue noise samples, relatively little is known for generating other noise patterns. In particular, no single algorithm is available to generate different noise patterns according to user-deﬁned spectra.
In this paper, we describe an algorithm for generating point samples that match a user-deﬁned Fourier spectrum function. Such a spectrum function can be either obtained from a known sampling method, or completely constructed by the user. Our key idea is to convert the Fourier spectrum function into a differential distribution function that describes the samplesí local spatial statistics; we then use a gradient descent solver to iteratively compute a sample set that matches the target differential distribution function. Our algorithm can be easily modiﬁed to achieve adaptive sampling, and we provide a GPU-based implementation. Finally, we present a variety of different sample patterns obtained using our algorithm, and demonstrate suitable applications.
Short Bio: Yahan Zhou is currently a 3rd-year MS/PhD track student in the Department of Computer Science, University of Massachusetts Amherst. He is advised by Prof. Rui Wang in the Computer Vision and Graphics Lab. He has a wide interest in computer graphics, including rendering, sampling and modeling.