@inbook{df7cff9be2e6462aa5c79de5e366f0f3,
title = "Partial Single- and Multishape Dense Correspondence Using Functional Maps",
abstract = "Shape correspondence is a fundamental problem in computer graphics and vision, with applications in various problems including animation, texture mapping, robotic vision, medical imaging, archaeology and many more. In settings where the shapes are allowed to undergo nonrigid deformations and only partial views are available, the problem becomes very challenging. In this chapter we describe recent techniques designed to tackle such problems. Specifically, we explain how the renown functional maps framework can be extended to tackle the partial setting. We then present a further extension to the multipart case in which one tries to establish correspondence between a collection of shapes. Finally, we focus on improving the technique efficiency, by disposing of its spatial ingredient and thus keeping the computation in the spectral domain. Extensive experimental results are provided along with the theoretical explanations, to demonstrate the effectiveness of the described methods in these challenging scenarios.",
keywords = "65D18, 68U05, Computational geometry, Functional maps, Shape analysis",
author = "Or Litany and Emanuele Rodol{\`a} and Alex Bronstein and Michael Bronstein and Daniel Cremers",
note = "Publisher Copyright: {\textcopyright} 2018 Elsevier B.V.",
year = "2018",
doi = "10.1016/bs.hna.2018.09.002",
language = "English",
isbn = "9780444642059",
series = "Handbook of Numerical Analysis",
publisher = "Elsevier B.V.",
pages = "55--90",
editor = "Ron Kimmel and Xue-Cheng Tai",
booktitle = "Processing, Analyzing and Learning of Images, Shapes, and Forms",
}