Point Sampling with General Noise Spectrum
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 samples' 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.
point sampling noise spectrum adaptive sampling