Premium
Generalized Stochastic Sampling Method for Visualization and Investigation of Implicit Surfaces
Author(s) -
Tanaka Satoshi,
Shibata Akihiro,
Yamamoto Hiroaki,
Kotsuru Hisakiyo
Publication year - 2001
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/1467-8659.00528
Subject(s) - generalization , visualization , computer science , constraint (computer aided design) , surface (topology) , sample (material) , sampling (signal processing) , stochastic differential equation , boundary (topology) , mathematics , algorithm , artificial intelligence , mathematical analysis , geometry , computer vision , chemistry , filter (signal processing) , chromatography
Recently we proposed the stochastic sampling method (SSM), which can numerically generate sample points on complicated implicit surfaces quickly and uniformly. In this paper we generalize the method in two aspects: (1) We introduce two kinds of boundary conditions, so that we can sample a finite part of an open surface spreading infinitely. (2) We generalize the stochastic differential equation used in the SSM, so that its solutions can satisfy plural constraint conditions simultaneously. The first generalization enables us to visualize cut views of open surfaces. The second generalization enables us to visualize intersections of static and moving implicit surfaces, which leads to detailed investigation of intersections and other interesting applications such as visualization of contour maps.