Abstract
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.
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Aiello, R., Banterle, F., Pietroni, N., Malomo, L., Cignoni, P., Scopigno, R. (2015). Compression and Querying of Arbitrary Geodesic Distances. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9279. Springer, Cham. https://doi.org/10.1007/978-3-319-23231-7_26
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DOI: https://doi.org/10.1007/978-3-319-23231-7_26
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