In this paper, we propose a novel method for accelerating the computation of geodesic distances over arbitrary manifold triangulated surfaces. The method is based on a preprocessing step where we build a data structure. This allows to store arbitrary complex distance metrics. We show that, by exploiting the precomputed data, the proposed method is significantly faster than the classical Dijkstra algorithm for the computation of point to point distances. Moreover, as we precompute exact geodesic distances, the proposed approach can be more accurate than state-of-the-art approximations.
CITATION STYLE
Aiello, R., Banterle, F., Pietroni, N., Malomo, L., Cignoni, P., & Scopigno, R. (2015). Compression and querying of arbitrary geodesic distances. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9279, pp. 282–293). Springer Verlag. https://doi.org/10.1007/978-3-319-23231-7_26
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