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.

该论文提出了一种计算 3D 形状之间多个高质量近等距密集对应的新方法，利用基于频谱映射表达的紧凑树结构和有效的初始点映射方法实现对目标形状的对应和对称性检测，从而实现更精确的形状匹配。