Parallel Reconstruction of Quad Only Meshes from Volume Data

Grosso, R.; Zint, Daniel

doi:10.5220/0008948701020112

Cited by 2 publications

(3 citation statements)

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“…In theses cases, the simplification reduces the total number of elements by 5%. The mesh simplification based on face coloring reduces much more elements than the method presented in [19]. For the skull surface, a total of 352,337 elements with the vertex valence pattern 3-X-3-Y were simplified.…”

Section: Mesh Simplificationmentioning

confidence: 99%

“…A novel dual marching cubes (DMC) algorithm was presented in [19], which does not require a lookup table, generates watertight meshes, and runs in parallel on GPU. The algorithm output is a high-quality quad only mesh which accurately represents the underlying geometry as defined by Eq.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, we implemented parallel simplification methods to reduce vertices and elements with vertex valence pattern 3-X-3-Y, with X,Y ≥ 5 and 3-3-3-3 which commonly appear in DMC meshes computed from volume data, thus improving the distribution of vertex valences in the mesh. For the pattern 3-X-3-Y, we use the face colors to remove a much higher number of irregular elements in comparison to the method proposed in [19]. A side effect of simplification is that some vertices are not positioned exactly on the iso-surface anymore.…”

Section: Introductionmentioning

confidence: 99%

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A parallel dual marching cubes approach to quad only surface reconstruction

Grosso

Zint

2021

Vis Comput

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We present a novel method that reconstructs surfaces from volume data using a dual marching cubes approach without lookup tables. The method generates quad only meshes which are consistent across cell borders, i.e., they are manifold and watertight. Vertices are positioned exactly on the reconstructed surface almost everywhere, leading to higher accuracy than other reconstruction methods. A halfedge data structure is used for storing the meshes which is convenient for further processing. The method processes elements in parallel and therefore runs efficiently on GPU. Due to the transition between layers in volume data, meshes have numerous vertices with valence three. We use simplification patterns for eliminating quads containing these vertices wherever possible which reduces the number of elements and increases quality. We briefly describe a CUDA implementation of our method, which allows processing huge amounts of data on GPU at almost interactive time rates. Finally, we present runtime and quality results of our method on medical and synthetic data sets.

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Section: Mesh Simplificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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