We introduce an approach to visualize stationary 2D vector fields with global uncertainty obtained by considering the transport of local uncertainty in the flow. For this, we extend the concept of vector field topology to uncertain vector fields by considering the vector field as a density distribution function. By generalizing the concepts of stream lines and critical points we obtain a number of density fields representing an uncertain topological segmentation. Their visualization as height surfaces gives insight into both the flow behavior and its uncertainty. We present a Monte Carlo approach where we integrate probabilistic particle paths, which lead to the segmentation of topological features. Moreover, we extend our algorithms to detect saddle points and present efficient implementations. Finally, we apply our technique to a number of real and synthetic test data sets.
The term stroke-based rendering collectively describes techniques where images are generated from elements that are usually larger than a pixel. These techniques lend themselves well for rendering artistic styles such as stippling and hatching. This paper presents a novel approach for stroke-based rendering that exploits multi-agent systems. RenderBots are individual agents each of which in general represents one stroke. They form a multi-agent system and undergo a simulation to distribute themselves in the environment. The environment consists of a source image and possibly additional G-buffers. The final image is created when the simulation is finished by having eachRenderBot execute its painting function. RenderBot classes differ in their physical behavior as well as their way of painting so that different styles can be created in a very flexible way.
We present a technique to visualize global uncertainty in stationary 3D vector fields by a topological approach. We start from an existing approach for 2D uncertain vector field topology and extend this into 3D space. For this a number of conceptional and technical challenges in performance and visual representation arise. In order to solve them, we develop an acceleration for finding sink and source distributions. Having these distributions we use overlaps of their corresponding volumes to find separating structures and saddles. As part of the approach, we introduce uncertain saddle and boundary switch connectors and provide algorithms to extract them. For the visual representation, we use multiple direct volume renderings. We test our method on a number of synthetic and real data sets.
This paper presents a procedural approach to generate furniture arrangements for large virtual indoor scenes. The interiors of buildings in 3D city scenes are often omitted. Our solution creates rich furniture arrangements for all rooms of complex buildings and even for entire cities. The key idea is to only furnish the rooms in the vicinity of the viewer while the user explores a building in real time. In order to compute the object layout we introduce an agent-based solution and demonstrate the flexibility and effectiveness of the agent approach. Furthermore, we describe advanced features of the system, like procedural furniture geometry, persistent room layouts, and styles for high-level control.
The generation of discrete stream surfaces is an important and challenging task in scientific visualization, which can be considered a particular instance of geometric modeling. The quality of numerically integrated stream surfaces depends on a number of parameters that can be controlled locally, such as time step or distance of adjacent vertices on the front line. In addition there is a parameter that cannot be controlled locally: stream surface meshes tend to show high quality, well-shaped elements only if the current front line is "globally" approximately perpendicular to the flow direction. We analyze the impact of this geometric property and present a novel solution -a stream surface integrator that forces the front line to be perpendicular to the flow and that generates quaddominant meshes with well-shaped and well-aligned elements. It is based on the integration of a scaled version of the flow field, and requires repeated minimization of an error functional along the current front line. We show that this leads to computing the 1-dimensional kernel of a bidiagonal matrix: a linear problem that can be solved efficiently. We compare our method with existing stream surface integrators and apply it to a number of synthetic and real world data sets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.