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

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.