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MapTree: Recovering Multiple Solutions in the Space of Maps

Abstract : 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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Contributor : Christophe Lino Connect in order to contact the contributor
Submitted on : Tuesday, December 8, 2020 - 1:55:33 PM
Last modification on : Wednesday, December 9, 2020 - 3:39:27 AM

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  • HAL Id : hal-03046434, version 1
  • ARXIV : 2006.02532



Jing Ren, Simone Melzi, Maks Ovsjanikov, Peter Wonka. MapTree: Recovering Multiple Solutions in the Space of Maps. 2020. ⟨hal-03046434⟩



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