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.

Ren, J., Melzi, S., Ovsjanikov, M., Wonka, P. (2020). MapTree: Recovering multiple solutions in the space of maps. ACM TRANSACTIONS ON GRAPHICS, 39(6), 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.
Articolo in rivista - Articolo scientifico
functional maps; shape matching; spectral methods;
English
1
17
17
Ren, J., Melzi, S., Ovsjanikov, M., Wonka, P. (2020). MapTree: Recovering multiple solutions in the space of maps. ACM TRANSACTIONS ON GRAPHICS, 39(6), 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/10281/350572
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