4D Feet: Registering Walking Foot Shapes Using Attention Enhanced Dynamic-Synchronized Graph Convolutional LSTM Network

4D scans of dynamic deformable human body parts help researchers have a better understanding of spatiotemporal features. However, reconstructing 4D scans based on multiple asynchronous cameras encounters two main challenges: 1) finding the dynamic correspondences among different frames captured by each camera at the timestamps of the camera in terms of dynamic feature recognition, and 2) reconstructing 3D shapes from the combined point clouds captured by different cameras at asynchronous timestamps in terms of multi-view fusion. In this paper, we introduce a generic framework that is able to 1) find and align dynamic features in the 3D scans captured by each camera using the nonrigid iterative closest-farthest points algorithm; 2) synchronize scans captured by asynchronous cameras through a novel ADGC-LSTM-based network, which is capable of aligning 3D scans captured by different cameras to the timeline of a specific camera; and 3) register a high-quality template to synchronized scans at each timestamp to form a high-quality 3D mesh model using a non-rigid registration method. With a newly developed 4D foot scanner, we validate the framework and create the first open-access data-set, namely the 4D feet. It includes 4D shapes (15 fps) of the right and left feet of 58 participants (116 feet in total, including 5147 3D frames), covering significant phases of the gait cycle. The results demonstrate the effectiveness of the proposed framework, especially in synchronizing asynchronous 4D scans using the proposed ADGC-LSTM network

Parameterization of tubular surfaces on the cylinder

In this paper we develop a method to parameterize tubular surfaces onto the cylinder. The cylinder can be seen as the natural parameterization domain for tubular surfaces since they share the same topology. Most present algorithms are designed to parameterize disc-like surfaces onto the plane. Surfaces with a different topology are cut into disc-like patches and the patches are parameterized separately. This introduces discontinuities and constrains the parameterization. Also the semantics of the surface are lost. We avoid this by parameterizing tubular surfaces on, their natural domain, the cylinder. Since the cylinder is locally isometric to the plane we can do calculations on the cylinder without loosing efficiency. For speeding up the calculation we use a progressive parameterization technique, as suggested in recent literature. Together, this results in a robust, efficient, continuous, and semantics preserving parameterization method for arbitrary tubular surfaces

Non-rigid registration via intelligent adaptive feedback control

Abstract: 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

Segmentation of the human trachea using deformable statistical models of tubular shapes

Abstract. In this work, we present two active shape models for the seg-mentation of tubular objects. The first model is built using cylindrical parameterization and minimum description length to achieve correct cor-respondences. The other model is a multidimensional point distribution model built from the centre line and related information of the training shapes. The models are used to segment the human trachea in low-dose CT scans of the thorax and are compared in terms of compactness of rep-resentation and segmentation effectiveness and efficiency. Leave-one-out tests were carried out on real CT data.