Name
Abstract | ||
|---|---|---|
The computation of intrinsic, geodesic distances and geodesic paths on surfaces is a fundamental low-level building block in countless Computer Graphics and Geometry Processing applications. This demand led to the development of numerous algorithms -- some for the exact, others for the approximative computation, some focussing on speed, others providing strict guarantees. Most of these methods are designed for computing distances according to the standard Riemannian metric induced by the surface's embedding in Euclidean space. Generalization to other, especially anisotropic, metrics -- which more recently gained interest in several application areas -- is not rarely hampered by fundamental problems. We explore and discuss possibilities for the generalization and extension of well-known methods to the anisotropic case, evaluate their relative performance in terms of accuracy and speed, and propose a novel algorithm, the Short-Term Vector Dijkstra. This algorithm is strikingly simple to implement and proves to provide practical accuracy at a higher speed than generalized previous methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1111/cgf.12173 | Comput. Graph. Forum |
Keywords | Field | DocType |
novel algorithm,approximative computation,practical anisotropic geodesy,numerous algorithm,fundamental problem,geodesic distance,fundamental low-level building block,practical accuracy,geodesic path,anisotropic case,higher speed | Anisotropy,Computer science,Theoretical computer science,Artificial intelligence,Computer graphics,Computation,Dijkstra's algorithm,Computer vision,Mathematical optimization,Embedding,Geometry processing,Algorithm,Euclidean space,Geodesic | Journal |
Volume | Issue | ISSN |
32 | 5 | 0167-7055 |
Citations | PageRank | References |
13 | 0.49 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcel Campen | 1 | 407 | 23.47 |
| Martin Heistermann | 2 | 13 | 0.49 |
| Leif Kobbelt | 3 | 5783 | 333.35 |