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Article Dans Une Revue ACM Transactions on Graphics Année : 2020

MapTree: Recovering Multiple Solutions in the Space of Maps

Résumé

In this paper we propose an approach for computing multiple high-quality near-isometric dense correspondences between a pair of 3D shapes. Our method is fully automatic and does not rely on user-provided landmarks or descriptors. This allows us to analyze the full space of maps and extract multiple diverse and accurate solutions, rather than optimizing for a single optimal correspondence as done in most previous approaches. To achieve this, we propose a compact tree structure based on the spectral map representation for encoding and enumerating possible rough initializations, and a novel efficient approach for refining them to dense pointwise maps. This leads to a new method capable of both producing multiple high-quality correspondences across shapes and revealing the symmetry structure of a shape without a priori information. In addition, we demonstrate through extensive experiments that our method is robust and results in more accurate correspondences than state-of-the-art for shape matching and symmetry detection.

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Informatique
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Dates et versions

hal-04479801 , version 1 (27-02-2024)

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Jing Ren, Simone Melzi, Maks Ovsjanikov, Peter Wonka. MapTree: Recovering Multiple Solutions in the Space of Maps. ACM Transactions on Graphics, 2020, 39 (6), pp.1-17. ⟨10.1145/3414685.3417800⟩. ⟨hal-04479801⟩
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