Non-Rigid Registration Via Intelligent Adaptive Feedback Control

• Published in: IEEE transactions on visualization and computer graphics 2024-08, Vol.30 (8), p.4910-4926
• Main Authors: Tajdari, Farzam, Huysmans, Toon, Song, Yu
• Format: Article
• Language: English
• Subjects: Adaptive control; Adaptive systems; Algorithms; ANFIS predictor; Artificial neural networks; Cost function; Deformation; Feedback control; Fuzzy logic; Geometry; global asymptotic stability; Iterative methods; mesh quality; non-rigid registration; Registration; Shape; shape descriptor; Stiffness; Surface treatment; Three-dimensional displays; Topology
• ISSN: 1077-2626; 1941-0506; 1941-0506
• DOI: 10.1109/TVCG.2023.3283990

Preserving features or local shape characteristics of a mesh using conventional non-rigid registration methods is always difficult, as the preservation and deformation are competing with each other. The challenge is to find a balance between these two terms in the process of the registration, especially in presence of artefacts in the mesh. We present a non-rigid Iterative Closest Points (ICP) algorithm which addresses the challenge as a control problem. An adaptive feedback control scheme with global asymptotic stability is derived to control the stiffness ratio for maximum feature preservation and minimum mesh quality loss during the registration process. A cost function is formulated with the distance term and the stiffness term where the initial stiffness ratio value is defined by an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based predictor regarding the source mesh and the target mesh topology, and the distance between the correspondences. During the registration process, the stiffness ratio of each vertex is continuously adjusted by the intrinsic information, represented by shape descriptors, of the surrounding surface as well as the steps in the registration process. Besides, the estimated process-dependent stiffness ratios are used as dynamic weights for establishing the correspondences in each step of the registration. Experiments on simple geometric shapes as well as 3D scanning datasets indicated that the proposed approach outperforms current methodologies, especially for the regions where features are not eminent and/or there exist interferences between/among features, due to its ability to embed the inherent properties of the surface in the process of the mesh registration.