arXiv subjects

Tahmina Khanam

Publications and source records attributed to Tahmina Khanam.

3 recordsLinked to original sources

arXiv2026

A Riemannian Framework for the Elastic Analysis of the Spatiotemporal Variability in the Shape and Structure of Tree-like 4D Objects

This paper introduces a novel computational framework for modeling and analyzing the spatiotemporal shape variability of tree-like 4D structures whose shapes deform and evolve over time. Tree-like 3D objects, such as botanical trees and plants, deform and grow at different rates. In this process, they bend and stretch their branches and change their branching structure, making their spatiotemporal registration challenging. We address this problem within a Riemannian framework that represents tree-like 3D objects as points in a tree-shape space endowed with a proper elastic metric that quantifies branch bending, stretching, and topological changes. With this setting, a 4D tree-like object becomes a trajectory in the tree-shape space. Thus, the problem of modeling and analyzing the spatiotemporal variability in tree-like 4D objects reduces to the analysis of trajectories within this tree-shape space. However, performing spatiotemporal registration and subsequently computing geodesics and statistics in the nonlinear tree-shape space is inherently challenging, as these tasks rely on complex nonlinear optimizations. Our core contribution is the mapping of the tree-like 3D objects to the space of the Extended Square Root Velocity Field, where the complex elastic metric is reduced to the L2 metric. By solving spatial registration in the ESRVF space, analyzing tree-like 4D objects can be reformulated as the problem of analyzing elastic trajectories in the ESRVF space. Based on this formulation, we develop a comprehensive framework for analyzing the spatiotemporal dynamics of tree-like objects, including registration under large deformations and topological differences, geodesic computation, statistical summarization through mean trajectories and modes of variation, and the synthesis of new, random tree-like 4D shapes.

cs.CG↗

arXiv2026

TreeSRNF: Square-Root Normal Fields for Generative Modelling of the Geometric and Structural Variability in Tree-like 3D Objects

We introduce a novel mathematical framework for analyzing and generating complex tree-shaped 3D objects, such as botanical trees and plants, which deform both in their 3D geometry and branching structure. Unlike previous works, which either consider only the skeletal structure of tree-like objects or approximate their 3D geometry using branch thickness, the proposed framework accurately models both the 3D geometry of the tree branches and the way they are interconnected. In this paper, we first generalize the Square Root Normal Fields (SRNF) representation, originally proposed for the statistical analysis of genus-0 surfaces, to tree-shaped 3D objects. We then treat tree-shaped 3D objects as points on a novel Riemannian tree-shape space equipped with a novel Riemannian metric that measures the amount of surface bending and stretching, and structural changes one needs to apply to one 3D tree-shape to align it with another. This way, deformations become trajectories in this novel tree-shape space. We analyze the theoretical properties of this novel tree-shape space and the corresponding metric and develop algorithms for computing point-wise and branch-wise correspondences and geodesic paths between complex 3D trees. We finally show how to use these building blocks for (1) computing statistical summaries, \ie means and modes of variation, of collections of tree-shaped 3D objects, and (2) synthesizing novel tree-shaped 3D objects by sampling from probability distributions fitted to a population of tree-shaped 3D objects. We demonstrate the performance and utility of the proposed framework on real and synthetic plants and botanical trees and show that it significantly outperforms the state-of-the-art.

cs.CV↗

arXiv2024

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

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.

cs.CV↗