7,469 research outputs found
An image-based approach to interactive crease extraction and rendering
AbstractRidge and valley manifolds are receiving a growing attention in visualization research due to their ability to reveal the shapes of salient structures in numerical datasets across scientific, engineering, and medical applications. However, the methods proposed to date for their extraction in the visualization and image analysis literature are computationally expensive and typically applied in an offline setting. This setup does not properly support a userdriven exploration, which often requires control over various parameters tuned to filter false positives and spurious artifacts and highlight the most significant structures. This paper presents a GPU-based adaptive technique for crease extraction and visualization across scales. Our method combines a scale-space analysis of the data in pre-processing with a ray casting approach supporting a robust and efficient one-dimensional numerical search, and an image-based rendering strategy. This general framework achieves high-quality crease surface representations at interactive frame rates. Results are proposed for analytical, medical, and computational datasets
AlSub: Fully Parallel and Modular Subdivision
In recent years, mesh subdivision---the process of forging smooth free-form
surfaces from coarse polygonal meshes---has become an indispensable production
instrument. Although subdivision performance is crucial during simulation,
animation and rendering, state-of-the-art approaches still rely on serial
implementations for complex parts of the subdivision process. Therefore, they
often fail to harness the power of modern parallel devices, like the graphics
processing unit (GPU), for large parts of the algorithm and must resort to
time-consuming serial preprocessing. In this paper, we show that a complete
parallelization of the subdivision process for modern architectures is
possible. Building on sparse matrix linear algebra, we show how to structure
the complete subdivision process into a sequence of algebra operations. By
restructuring and grouping these operations, we adapt the process for different
use cases, such as regular subdivision of dynamic meshes, uniform subdivision
for immutable topology, and feature-adaptive subdivision for efficient
rendering of animated models. As the same machinery is used for all use cases,
identical subdivision results are achieved in all parts of the production
pipeline. As a second contribution, we show how these linear algebra
formulations can effectively be translated into efficient GPU kernels. Applying
our strategies to , Loop and Catmull-Clark subdivision shows
significant speedups of our approach compared to state-of-the-art solutions,
while we completely avoid serial preprocessing.Comment: Changed structure Added content Improved description
A Local Iterative Approach for the Extraction of 2D Manifolds from Strongly Curved and Folded Thin-Layer Structures
Ridge surfaces represent important features for the analysis of 3-dimensional
(3D) datasets in diverse applications and are often derived from varying
underlying data including flow fields, geological fault data, and point data,
but they can also be present in the original scalar images acquired using a
plethora of imaging techniques. Our work is motivated by the analysis of image
data acquired using micro-computed tomography (Micro-CT) of ancient, rolled and
folded thin-layer structures such as papyrus, parchment, and paper as well as
silver and lead sheets. From these documents we know that they are
2-dimensional (2D) in nature. Hence, we are particularly interested in
reconstructing 2D manifolds that approximate the document's structure. The
image data from which we want to reconstruct the 2D manifolds are often very
noisy and represent folded, densely-layered structures with many artifacts,
such as ruptures or layer splitting and merging. Previous ridge-surface
extraction methods fail to extract the desired 2D manifold for such challenging
data. We have therefore developed a novel method to extract 2D manifolds. The
proposed method uses a local fast marching scheme in combination with a
separation of the region covered by fast marching into two sub-regions. The 2D
manifold of interest is then extracted as the surface separating the two
sub-regions. The local scheme can be applied for both automatic propagation as
well as interactive analysis. We demonstrate the applicability and robustness
of our method on both artificial data as well as real-world data including
folded silver and papyrus sheets.Comment: 16 pages, 21 figures, to be published in IEEE Transactions on
Visualization and Computer Graphic
Part decomposition of 3D surfaces
This dissertation describes a general algorithm that automatically decomposes realworld scenes and objects into visual parts. The input to the algorithm is a 3 D triangle mesh that approximates the surfaces of a scene or object. This geometric mesh completely specifies the shape of interest. The output of the algorithm is a set of boundary contours that dissect the mesh into parts where these parts agree with human perception. In this algorithm, shape alone defines the location of a bom1dary contour for a part. The algorithm leverages a human vision theory known as the minima rule that states that human visual perception tends to decompose shapes into parts along lines of negative curvature minima. Specifically, the minima rule governs the location of part boundaries, and as a result the algorithm is known as the Minima Rule Algorithm. Previous computer vision methods have attempted to implement this rule but have used pseudo measures of surface curvature. Thus, these prior methods are not true implementations of the rule. The Minima Rule Algorithm is a three step process that consists of curvature estimation, mesh segmentation, and quality evaluation. These steps have led to three novel algorithms known as Normal Vector Voting, Fast Marching Watersheds, and Part Saliency Metric, respectively. For each algorithm, this dissertation presents both the supporting theory and experimental results. The results demonstrate the effectiveness of the algorithm using both synthetic and real data and include comparisons with previous methods from the research literature. Finally, the dissertation concludes with a summary of the contributions to the state of the art
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