In this work, a simple and efficient dual iterative refinement (DIR) method
is proposed for dense correspondence between two nearly isometric shapes. The
key idea is to use dual information, such as spatial and spectral, or local and
global features, in a complementary and effective way, and extract more
accurate information from current iteration to use for the next iteration. In
each DIR iteration, starting from current correspondence, a zoom-in process at
each point is used to select well matched anchor pairs by a local mapping
distortion criterion. These selected anchor pairs are then used to align
spectral features (or other appropriate global features) whose dimension
adaptively matches the capacity of the selected anchor pairs. Thanks to the
effective combination of complementary information in a data-adaptive way, DIR
is not only efficient but also robust to render accurate results within a few
iterations. By choosing appropriate dual features, DIR has the flexibility to
handle patch and partial matching as well. Extensive experiments on various
data sets demonstrate the superiority of DIR over other state-of-the-art
methods in terms of both accuracy and efficiency.

本研究提出了一种基于双重迭代精化（DIR）的解决方案，通过优雅的方法将空间和频谱数据融合起来，从当前迭代中提取更多准确的信息用于下一次迭代，有效地将双重信息结合在一起，从而提供了一种高效而稳健的密集对应解决方案。