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A survey on nonrigid 3D shape analysis

Laga, H.ORCID: 0000-0002-4758-7510 (2018) A survey on nonrigid 3D shape analysis. In: Chellappa, R. and Theodoridis, S., (eds.) Academic Press Library in Signal Processing, Volume 6. Academic Press, pp. 261-304.

Link to Published Version: https://doi.org/10.1016/B978-0-12-811889-4.00007-5
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Abstract

Shape is an important physical property of natural and manmade 3D objects that characterizes their external appearances. Understanding differences between shapes and modeling the variability within and across shape classes, hereinafter referred to as shape analysis, are fundamental problems to many applications, ranging from computer vision and computer graphics to biology and medicine. This chapter provides an overview of some of the recent techniques that studied the shape of 3D objects that undergo nonrigid deformations including bending and stretching. Recent surveys that covered some aspects such as classification, retrieval, recognition, and rigid or nonrigid registration, focused on methods that use shape descriptors. Descriptors, however, provide abstract representations that do not enable the exploration of shape variability. In this chapter, we focus on recent techniques that treated the shape of 3D objects as points in some high dimensional space where paths describe deformations. Equipping the space with a suitable metric enables the quantification of the range of deformations of a given shape, which in turn enables (1) comparing and classifying 3D objects based on their shape, (2) computing smooth deformations, i.e., geodesics, between pairs of objects, and (3) modeling and exploring continuous shape variability in a collection of 3D models. This chapter surveys and classifies recent developments in this field, outlines fundamental issues, discusses their potential applications in computer vision and graphics, and highlights opportunities for future research. Our primary goal is to bridge the gap between various techniques that have been often independently proposed by different communities including mathematics and statistics, computer vision and graphics, and medical image analysis.

Item Type: Book Chapter
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Academic Press
Copyright: © 2018 Elsevier Ltd.
URI: http://researchrepository.murdoch.edu.au/id/eprint/42342
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