Variable Shared Template for Consistent Non-rigid ICP

摘要

Non-rigid registration of 3D shape collections using a template mesh is essential for constructing 3D datasets. Traditional non-rigid Iterative Closest Point (ICP) methods rely on manually selected template meshes, which can result in inconsistent registrations when applied to diverse shape collections. This inconsistency arises particularly when the template lacks common shape features with the input instances or when landmark annotations are sparse. To overcome this limitation, we propose a novel ICP framework that jointly optimizes a shared template shape and its instance-wise deformations. Our joint optimization framework assigns distinct roles to the shared template and instance-wise deformations: the template captures common shape features, while instance-wise deformations handle residual registration errors. We use stronger smoothness regularization on the instance-wise deformations in early iterations to prioritize the accumulation of common details on the template. Additionally, a distortion alignment energy minimizes interinstance map distortions, promoting consistent instance-wise deformations. On challenging 3D datasets with large shape variations, our method achieves state-of-the-art fitting accuracy and consistent results in shape averaging and deformation transfer. By removing the need for a carefully selected preset template, our method extends the capability of extrinsic non-rigid registration frameworks, offering a more robust and flexible solution for challenging registration scenarios.