  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.
