3,987 research outputs found
Scientific Visualization Using the Flow Analysis Software Toolkit (FAST)
Over the past few years the Flow Analysis Software Toolkit (FAST) has matured into a useful tool for visualizing and analyzing scientific data on high-performance graphics workstations. Originally designed for visualizing the results of fluid dynamics research, FAST has demonstrated its flexibility by being used in several other areas of scientific research. These research areas include earth and space sciences, acid rain and ozone modelling, and automotive design, just to name a few. This paper describes the current status of FAST, including the basic concepts, architecture, existing functionality and features, and some of the known applications for which FAST is being used. A few of the applications, by both NASA and non-NASA agencies, are outlined in more detail. Described in the Outlines are the goals of each visualization project, the techniques or 'tricks' used lo produce the desired results, and custom modifications to FAST, if any, done to further enhance the analysis. Some of the future directions for FAST are also described
Segmentation of Unstructured Datasets
Datasets generated by computer simulations and experiments in Computational Fluid Dynamics tend to be extremely large and complex. It is difficult to visualize these datasets using standard techniques like Volume Rendering and Ray Casting. Object Segmentation provides a technique to extract and quantify regions of interest within these massive datasets. This thesis explores basic algorithms to extract coherent amorphous regions from two-dimensional and three-dimensional scalar unstructured grids. The techniques are applied to datasets from Computational Fluid Dynamics and from Finite Element Analysis
Mobile Robot Range Sensing through Visual Looming
This article describes and evaluates visual looming as a monocular range sensing method for mobile robots. The looming algorithm is based on the relationship between the displacement of a camera relative to an object, and the resulting change in the size of the object's image on the focal plane of the camera. We have carried out systematic experiments to evaluate the ranging accuracy of the looming algorithm using a Pioneer I mobile robot equipped with a color camera. We have also performed noise sensitivity for the looming algorithm, obtaining theoretical error bounds on the range estimates for given levels of odometric and visual noise, which were verified through experimental data. Our results suggest that looming can be used as a robust, inexpensive range sensor as a complement to sonar.Defense Advanced Research Projects Agency; Office of Naval Research; Navy Research Laboratory (00014-96-1-0772, 00014-95-1-0409
Mobile Robot Range Sensing through Visual Looming
This article describes and evaluates visual looming as a monocular range sensing method for mobile robots. The looming algorithm is based on the relationship between the displacement of a camera relative to an object, and the resulting change in the size of the object's image on the focal plane of the camera. We have carried out systematic experiments to evaluate the ranging accuracy of the looming algorithm using a Pioneer I mobile robot equipped with a color camera. We have also performed noise sensitivity for the looming algorithm, obtaining theoretical error bounds on the range estimates for given levels of odometric and visual noise, which were verified through experimental data. Our results suggest that looming can be used as a robust, inexpensive range sensor as a complement to sonar.Defense Advanced Research Projects Agency; Office of Naval Research; Navy Research Laboratory (00014-96-1-0772, 00014-95-1-0409
The Topology ToolKit
This system paper presents the Topology ToolKit (TTK), a software platform
designed for topological data analysis in scientific visualization. TTK
provides a unified, generic, efficient, and robust implementation of key
algorithms for the topological analysis of scalar data, including: critical
points, integral lines, persistence diagrams, persistence curves, merge trees,
contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots,
Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due
to a tight integration with ParaView. It is also easily accessible to
developers through a variety of bindings (Python, VTK/C++) for fast prototyping
or through direct, dependence-free, C++, to ease integration into pre-existing
complex systems. While developing TTK, we faced several algorithmic and
software engineering challenges, which we document in this paper. In
particular, we present an algorithm for the construction of a discrete gradient
that complies to the critical points extracted in the piecewise-linear setting.
This algorithm guarantees a combinatorial consistency across the topological
abstractions supported by TTK, and importantly, a unified implementation of
topological data simplification for multi-scale exploration and analysis. We
also present a cached triangulation data structure, that supports time
efficient and generic traversals, which self-adjusts its memory usage on demand
for input simplicial meshes and which implicitly emulates a triangulation for
regular grids with no memory overhead. Finally, we describe an original
software architecture, which guarantees memory efficient and direct accesses to
TTK features, while still allowing for researchers powerful and easy bindings
and extensions. TTK is open source (BSD license) and its code, online
documentation and video tutorials are available on TTK's website
Spherical Transformer: Adapting Spherical Signal to CNNs
Convolutional neural networks (CNNs) have been widely used in various vision
tasks, e.g. image classification, semantic segmentation, etc. Unfortunately,
standard 2D CNNs are not well suited for spherical signals such as panorama
images or spherical projections, as the sphere is an unstructured grid. In this
paper, we present Spherical Transformer which can transform spherical signals
into vectors that can be directly processed by standard CNNs such that many
well-designed CNNs architectures can be reused across tasks and datasets by
pretraining. To this end, the proposed method first uses locally structured
sampling methods such as HEALPix to construct a transformer grid by using the
information of spherical points and its adjacent points, and then transforms
the spherical signals to the vectors through the grid. By building the
Spherical Transformer module, we can use multiple CNN architectures directly.
We evaluate our approach on the tasks of spherical MNIST recognition, 3D object
classification and omnidirectional image semantic segmentation. For 3D object
classification, we further propose a rendering-based projection method to
improve the performance and a rotational-equivariant model to improve the
anti-rotation ability. Experimental results on three tasks show that our
approach achieves superior performance over state-of-the-art methods
- …