Consistent as-similar-as-possible non-isometric surface registration

Abstract

Non-isometric surface registration, aiming to align two surfaces with different sizes and details, has been widely used in computer animation industry. Various existing surface registration approaches have been proposed for accurate template fitting; nevertheless, two challenges remain. One is how to avoid the mesh distortion and fold over of surfaces during transformation. The other is how to reduce the amount of landmarks that have to be specified manually. To tackle these challenges simultaneously, we propose a consistent as-similar-as-possible (CASAP) surface registration approach. With a novel defined energy, it not only achieves the consistent discretization for the surfaces to produce accurate result, but also requires a small number of landmarks with little user effort only. Besides, CASAP is constrained as-similar-as-possible so that angles of triangle meshes are preserved and local scales are allowed to change. Extensive experimental results have demonstrated the effectiveness of CASAP in comparison with the state-of-the-art approaches.

Huber-$$L_1$$-based non-isometric surface registration

“…However, the discretized energy they adopted is not a consistent discretization of a continuous energy and thus may produce foldover and distortion. To tackle this issue, Jiang et al [8] propose a consistent as-similar-as-possible surface registra-tion method. They apply the L 2 -norm regularization on both the transformation variation and position difference (L 2 -L 2 ), which results in consistent discretization for surfaces and improves the quality of the surface deformation and registration.…”

“…However, it is not capable of handling shapes with different sizes since it tries to preserve the length of edges. To address this limitation, similar registration approaches [8,29] introduce a scale factor into each local transformation and formulate as an as-similar-as-possible (ASAP) energy, which allows local scalability to handle the size difference.…”

“…For the accuracy and robustness of registration, the transformation variation and position difference constraints are usually formulated as a smoothness term and a data term, respectively, to measure the smoothness of the neighboring transformation and the closeness of registration shapes, respectively. Most works [1,8,10] use the classic squared L 2norm on both constraints (L 2 -L 2 ). However, the smoothness term in L 2 -norm tends to penalize large transformation variation.…”

3D content creation exploiting 2D character animation

“…For the body parts for which the user has provided multiple perspectives, to maintain all the character's features visible from different perspectives, a novel non-isometric surface registration method is proposed, inspired by the work of [Jiang et al 2017].…”

Surface Registration with Eigenvalues and Eigenvectors

“…Traditional landmark-based methods usually detect relevant corresponding points or curves in two shapes, i.e., landmarking is essential in many shape registration and mapping applications [4], [5], [6], [7], [8], [9]. There are two drawbacks in this type of methods.…”