Kernel-Based Sampling of Arbitrary Signals. (December 2021)

• Record Type: Journal Article
• Authors: Cammarasana, Simone; Patanè, Giuseppe
• Is Part Of: Computer aided design. Volume 141(2021)
• Publication Date: 2021-12
• Subjects: Kernel-based sampling -- Multi-scale kernel-based sampling -- Adaptive kernel-based sampling -- Data and signal sampling -- Signal approximation -- Radial basis functions; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2021.103103
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: Point sampling is widely used in several Computer Graphics' applications, such as point-based modelling and rendering, image and geometry processing. Starting from the kernel-based sampling, which approximates an input signal on a regular grid as the sum of Gaussian kernels, we introduce a set of additional variables that control the kernels' width and height. These additional variables allow us to improve the quality of the distribution of the samples, and to achieve a higher approximation accuracy and a more accurate feature preservation, with a slightly higher computational cost. To further improve the sampling with respect to the input data, we introduce a sampling initialisation for processing high resolution signals, without incurring in limits for memory allocation, and a sampling optimisation, which adaptively selects the number and location of the samples to achieve the target approximation accuracy, without oversampling the input signal. To show the generality of the proposed approach for unstructured data of arbitrary dimension, we apply our kernel-based sampling to different types of data, such as 2D images, solutions to PDEs on arbitrary domains, and vector fields. Highlights: Kernel-based sampling with high approximation accuracy and feature preservation. Multi-scale kernel-based sampling for processing high-resolution signals. Adaptive kernels' width, height, and centers, according to the target accuracy. General sampling scheme for structured and unstructured data of arbitrary dimension.