MapTree: Recovering multiple solutions in the space of maps

Jing Ren, Simone Melzi, Maks Ovsjanikov, Peter Wonka

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish (US)
Pages (from-to)1-17
Number of pages17
JournalACM Transactions on Graphics
Volume39
Issue number6
DOIs
StatePublished - Nov 26 2020

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