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.

MapTree: Recovering multiple solutions in the space of maps / Ren, J.; Melzi, S.; Ovsjanikov, M.; Wonka, P.. - In: ACM TRANSACTIONS ON GRAPHICS. - ISSN 0730-0301. - 39:6(2020), pp. 1-17. [10.1145/3414685.3417800]

MapTree: Recovering multiple solutions in the space of maps

Melzi S.
;
2020

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.
2020
functional maps; shape matching; spectral methods
01 Pubblicazione su rivista::01a Articolo in rivista
MapTree: Recovering multiple solutions in the space of maps / Ren, J.; Melzi, S.; Ovsjanikov, M.; Wonka, P.. - In: ACM TRANSACTIONS ON GRAPHICS. - ISSN 0730-0301. - 39:6(2020), pp. 1-17. [10.1145/3414685.3417800]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1472993
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