A Riemannian Approach for Spatiotemporal Analysis and Generation of 4D Tree-shaped Structures

• URL: http://arxiv.org/abs/2408.12443v1
• Date: Thu, 22 Aug 2024 14:39:30 GMT
• Title: A Riemannian Approach for Spatiotemporal Analysis and Generation of 4D Tree-shaped Structures
• Authors: Tahmina Khanam, Hamid Laga, Mohammed Bennamoun, Guanjin Wang, Ferdous Sohel, Farid Boussaid, Guan Wang, Anuj Srivastava,
• Abstract summary: We present the first comprehensive approach for modeling and analyzingtemporal shape variability in tree-like 4D objects. Our key contribution is the representation of tree-like 3D shapes using Square Root Velocity Trees (SFT) This reduces the problem of modeling and analyzing 4D tree-like shapes to that of modeling and analyzing elastic trajectories in the SRVFT space.
• Score: 38.04447518666407
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose the first comprehensive approach for modeling and analyzing the spatiotemporal shape variability in tree-like 4D objects, i.e., 3D objects whose shapes bend, stretch, and change in their branching structure over time as they deform, grow, and interact with their environment. Our key contribution is the representation of tree-like 3D shapes using Square Root Velocity Function Trees (SRVFT). By solving the spatial registration in the SRVFT space, which is equipped with an L2 metric, 4D tree-shaped structures become time-parameterized trajectories in this space. This reduces the problem of modeling and analyzing 4D tree-like shapes to that of modeling and analyzing elastic trajectories in the SRVFT space, where elasticity refers to time warping. In this paper, we propose a novel mathematical representation of the shape space of such trajectories, a Riemannian metric on that space, and computational tools for fast and accurate spatiotemporal registration and geodesics computation between 4D tree-shaped structures. Leveraging these building blocks, we develop a full framework for modelling the spatiotemporal variability using statistical models and generating novel 4D tree-like structures from a set of exemplars. We demonstrate and validate the proposed framework using real 4D plant data.

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