Functional maps based dense 3D human body correspondence from single view point clouds

Wei Li; Kangkan Wang; Huayu Zheng

doi:10.1117/12.2626548

20 December 2021 Functional maps based dense 3D human body correspondence from single view point clouds

Wei Li, Kangkan Wang, Huayu Zheng

Proceedings Volume 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021); 1215508 (2021) https://doi.org/10.1117/12.2626548
Event: International Conference on Computer Vision, Application, and Design (CVAD 2021), 2021, Sanya, China

Abstract

We propose a new method based on Functional Maps to estimate the dense point-pair relationship between single-view 3D human point clouds and template point clouds. At present, most of the relevant work is based on triangular information to estimate the correspondence, and our innovation is to process directly on the point clouds. Because the single-perspective point clouds don’t have the full human body information, these methods cannot effectively find out the correspondence. Firstly, the template is used to complete the missing human body information to obtain the full human structure, so that the Laplace-Beltrami operator (LBO) can be calculated effectively. Then, features are extracted based on the deep learning method, and geometric information was converted to spatial information. Finally, the linear function mapping is calculated to characterize the dense point-to-point correspondence.

Citation Download Citation

Wei Li, Kangkan Wang, and Huayu Zheng "Functional maps based dense 3D human body correspondence from single view point clouds", Proc. SPIE 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021), 1215508 (20 December 2021); https://doi.org/10.1117/12.2626548

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