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arXiv:2203.07858 (cs)
[Submitted on 11 Mar 2022 (v1), last revised 16 Mar 2022 (this version, v2)]

Title:A Survey of Non-Rigid 3D Registration

Authors:Bailin Deng, Yuxin Yao, Roberto M. Dyke, Juyong Zhang
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Abstract:Non-rigid registration computes an alignment between a source surface with a target surface in a non-rigid manner. In the past decade, with the advances in 3D sensing technologies that can measure time-varying surfaces, non-rigid registration has been applied for the acquisition of deformable shapes and has a wide range of applications. This survey presents a comprehensive review of non-rigid registration methods for 3D shapes, focusing on techniques related to dynamic shape acquisition and reconstruction. In particular, we review different approaches for representing the deformation field, and the methods for computing the desired deformation. Both optimization-based and learning-based methods are covered. We also review benchmarks and datasets for evaluating non-rigid registration methods, and discuss potential future research directions.
Comments: Accepted to Eurographics 2022 State-of-the-Art Reports
Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG)
Cite as: arXiv:2203.07858 [cs.CV]
  (or arXiv:2203.07858v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2203.07858

Submission history

From: Bailin Deng [view email]
[v1] Fri, 11 Mar 2022 15:54:19 UTC (6,693 KB)
[v2] Wed, 16 Mar 2022 19:13:17 UTC (6,694 KB)
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