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Non-rigid point set registration: recent trends and challenges

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Abstract

Non-rigid point set registration has been used in a wide range of computer vision applications such as human movement tracking, medical image analysis, three dimensional (3D) object reconstruction and is a very challenging task. It has two fundamental tasks. One is to find correspondences between two or more point sets and another is to transform a point set so that it aligns with other point sets. There has been significant progress in the past two decades in the non-rigid registration field but it still has major challenges and is an active research area in the computer vision and pattern recognition community. In this review, we present a survey of non-rigid point set registration. Unlike recent surveys, we focus on the mathematical foundations of non-rigid registration methods, categorize the methods from several perspectives, and discuss open challenges. We categorize the methods according to correspondence models, motivations, and challenges such as deformation, data degradation, computational efficiency, and different constraints used in the methods to achieve accurate registration results. We present the publicly available data sets and different evaluation techniques employed in the methods. Further, we discuss open challenges, recent trends, and potential directions for future work in this area.

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  1. University of North Texas, Denton, TX, USA

    Xiaohui Yuan & Amar Maharjan

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  1. Xiaohui Yuan
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  2. Amar Maharjan
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Correspondence to Xiaohui Yuan.

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Yuan, X., Maharjan, A. Non-rigid point set registration: recent trends and challenges. Artif Intell Rev 56, 4859–4891 (2023). https://doi.org/10.1007/s10462-022-10292-4

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