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We have proposed a new distance metric for 3D geometry data (point clouds and triangle meshes), DirDist. Different from previous metrics, our DirDist concentrates on the implicit fields of the given shapes. We have combine DirDist with various applications, including Template Surface Fitting, Rigid Registration, Non-rigid Registration, Scene Flow Estimation, and Human pose optimization, and get better results than previous distance metrics. See more details in our paper [Arxiv].
As a general distance metric, we think that our DirDist could boost the development of 3D geometry modeling, and we hope that our DirDist could be included in pytorch3d library.