Refine
Year of publication
Document Type
- In Proceedings (14)
- Article (13)
- In Collection (7)
- ZIB-Report (6)
- Book chapter (4)
- Other (2)
- Poster (1)
Language
- English (47)
Keywords
This work introduces a novel streamline seeding technique based on dual streamlines that are orthogonal to the vector field, instead of tangential. The greedy algorithm presented here produces a net of orthogonal streamlines that is iteratively refined resulting in good domain coverage and a high degree of continuity and uniformity. The algorithm is easy to implement and efficient, and it naturally extends to curved surfaces.
We present a visual approach for the exploration of stress tensor fields. Therefore, we introduce the idea of multiple linked views to tensor visualization. In contrast to common tensor visualization methods that only provide a single view to the tensor field, we pursue the idea of providing various perspectives onto the data in attribute and object space. Especially in the context of stress tensors, advanced tensor visualization methods have a young tradition. Thus, we propose a combination of visualization techniques domain experts are used to with statistical views of tensor attributes. The application of this concept to tensor fields was achieved by extending the notion of shape space. It provides an intuitive way of finding tensor invariants that represent relevant physical properties. Using brushing techniques, the user can select features in attribute space, which are mapped to displayable entities in a three-dimensional hybrid visualization in object space. Volume rendering serves as context, while glyphs encode the whole tensor information in focus regions. Tensorlines can be included to emphasize directionally coherent features in the tensor field. We show that the benefit of such a multi-perspective approach is manifold. Foremost, it provides easy access to the complexity of tensor data. Moreover, including wellknown analysis tools, such as Mohr diagrams, users can familiarize themselves gradually with novel visualization methods. Finally, by employing a focus-driven hybrid rendering, we significantly reduce clutter, which was a major problem of other three-dimensional tensor visualization methods.
We propose a combinatorial algorithm to track critical points of 2D
time-dependent scalar fields. Existing tracking algorithms such as Feature
Flow Fields apply numerical schemes utilizing derivatives of the data,
which makes them prone to noise and involve a large number of computational
parameters. In contrast, our method is robust against noise
since it does not require derivatives, interpolation, and numerical integration.
Furthermore, we propose an importance measure that combines the
spatial persistence of a critical point with its temporal evolution. This
leads to a time-aware feature hierarchy, which allows us to discriminate
important from spurious features. Our method requires only a single,
easy-to-tune computational parameter and is naturally formulated in an
out-of-core fashion, which enables the analysis of large data sets. We apply
our method to a number of data sets and compare it to the stabilized
continuous Feature Flow Field tracking algorithm.
This work is concerned with adaptive screen-space sampling for volume ray-casting. The goal is to reduce the
number of rays being cast into the scene and, thus, the overall number of sampling points. We guarantee reliable
images through explicit error control using an error estimator that is founded in the field of finite element methods
(FEM). FEM theory further provides a well-founded theory to prove the efficiency of the presented algorithm via
convergence analysis. We, therefore, compare the convergence behavior of our method against uniform subdivisions
and a refinement scheme that was presented in the context of CPU volume ray-casting. Minimizing
the number of sampling points is of interest for rendering large datasets where each evaluation might need an expensive
decompression. Furthermore, with increasing screen resolutions high-resolution images are created more
efficiently with our method.