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4D Atlas: Statistical Analysis of the Spatiotemporal Variability in Longitudinal 3D Shape Data
Journal article   Peer reviewed

4D Atlas: Statistical Analysis of the Spatiotemporal Variability in Longitudinal 3D Shape Data

Hamid Laga, Marcel Padilla, Ian H. H. Jermyn, Sebastian Kurtek, Mohammed Bennamoun and Anuj Srivastava
IEEE transactions on pattern analysis and machine intelligence, Vol.45(2), pp.1335-1352
2023
DOI: https://doi.org/10.1109/TPAMI.2022.3163720
PMID: 35358041
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Abstract

Dynamic surfaces elastic metric square-root normal field statistical summaries 4D surface Human4D Face4D motion growth
We propose a novel framework to learn the spatiotemporal variability in longitudinal 3D shape data sets, which contain observations of objects that evolve and deform over time. This problem is challenging since surfaces come with arbitrary parameterizations and thus, they need to be spatially registered. Also, different deforming objects, hereinafter referred to as 4D surfaces, evolve at different speeds and thus they need to be temporally aligned. We solve this spatiotemporal registration problem using a Riemannian approach. We treat a 3D surface as a point in a shape space equipped with an elastic Riemannian metric that measures the amount of bending and stretching that the surfaces undergo. A 4D surface can then be seen as a trajectory in this space. With this formulation, the statistical analysis of 4D surfaces can be cast as the problem of analyzing trajectories embedded in a nonlinear Riemannian manifold. However, performing the spatiotemporal registration, and subsequently computing statistics, on such nonlinear spaces is not straightforward as they rely on complex nonlinear optimizations. Our core contribution is the mapping of the surfaces to the space of Square-Root Normal Fields (SRNF) where the L-2 metric is equivalent to the partial elastic metric in the space of surfaces. Thus, by solving the spatial registration in the SRNF space, the problem of analyzing 4D surfaces becomes the problem of analyzing trajectories embedded in the SRNF space, which has a euclidean structure. In this paper, we develop the building blocks that enable such analysis. These include: (1) the spatiotemporal registration of arbitrarily parameterized 4D surfaces even in the presence of large elastic deformations and large variations in their execution rates; (2) the computation of geodesics between 4D surfaces; (3) the computation of statistical summaries, such as means and modes of variation, of collections of 4D surfaces; and (4) the synthesis of random 4D surfaces. We demonstrate the performance of the proposed framework using 4D facial surfaces and 4D human body shapes.

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Collaboration types
Domestic collaboration
International collaboration
Citation topics
1 Clinical & Life Sciences
1.113 Brain Imaging
1.113.460 Advanced Neuroimaging
Web Of Science research areas
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
ESI research areas
Engineering
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