Name
Abstract | ||
|---|---|---|
In this paper we compute the distance transform of a 3D triangle mesh. A volumetric voxel representation is defined over the mesh to evaluate the distance transform. Optimizations are described to efficiently manipulate the volumetric data structure that represents the mesh. A new method for adaptive filtering of the distance transform is introduced to smooth and reduce the noise on the meshes that were reconstructed from scanned data acquired with a 3D scanner. A modified version of the Marching Cube algorithm is presented to correctly reconstruct the final mesh of the filtered distance transform defined with the voxel representation. The new filtering method is feature preserving and it is more versatile than previous algorithms described in the literature. Results show that this method outperforms previous ones in term of an error metric comparison. Future works are discussed to improve the new method and its computing performances. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1174429.1174497 | GRAPHITE |
Keywords | Field | DocType |
volumetric data structure,filtered distance,final mesh,scanned data,voxel representation,volumetric voxel representation,triangle mesh,new method,previous algorithm,marching cube algorithm,denoising triangle mesh,data structure,marching cube,distance transform,adaptive filtering,adaptive filter | Computer vision,Laplacian smoothing,Polygon mesh,Computer science,Marching cubes,Filter (signal processing),Theoretical computer science,Smoothing,Distance transform,Artificial intelligence,Adaptive filter,Triangle mesh | Conference |
ISBN | Citations | PageRank |
1-59593-564-9 | 8 | 0.59 |
References | Authors | |
21 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marc Fournier | 1 | 11 | 1.99 |
| Jean-michel Dischler | 2 | 386 | 34.69 |
| dominique bechmann | 3 | 258 | 29.25 |