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A spectral approach for segmentation and deformation estimation in point cloud using shape descriptors

Publication Type : Book Chapter

Publisher : Lecture Notes in Computational Vision and Biomechanics

Source : Lecture Notes in Computational Vision and Biomechanics, p.409-419 (2019)

Url : https://www.researchgate.net/publication/330077255_A_Spectral_Approach_for_Segmentation_and_Deformation_Estimation_

ISBN : 9783030006648

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2019

Abstract : In this paper, we propose a new framework for segmentation and deformation estimation in texture-less point clouds. Given a reference point cloud and a corresponding deformed point cloud, our approach first segments both the point clouds using OBB-LBS (Oriented Bounding Box-Laplace Beltrami Spectral) and estimates the semi-global dense spectral shape descriptors. These coarse descriptors identify the segments which need to be further investigated for localizing the area of deformation at a finer level. © Springer Nature Switzerland AG 2019.

Cite this Research Publication : J. Kalyani, Vaiapury, K., and Parameswaran, L., “A Spectral Approach for Segmentation and Deformation Estimation in Point Cloud Using Shape Descriptors”, in Lecture Notes in Computational Vision and Biomechanics, 2019, pp. 409-419.

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