Spatio-temporal registration of plants non-rigid 3-D structure

Abstract

Monitoring plants growth dynamics requires continuously tracing their evolution over time. When using point cloud data, such a process requires associating the individual organs among scans, spatially aligning them, and accounting for their evolution, decay, or split. It is common to address this challenge by abstracting the point cloud into its skeletal form and defining point correspondence by Euclidean measures. As the paper demonstrates, standard skeletonization approaches do not capture the actual plant topology, and Euclidean measures do not document its evolving form. To address this alignment challenge, we propose in this paper a registration model that traces high-degree deformations and accommodates the complex plant topology. We develop an embedded deformation graph-based solution and introduce manifold measures to trace the plant non-isometric development. We demonstrate how a path-seeking strategy and invariant features capture the plant topological form, and then use a probabilistic linear assignment solution to associate organs across scans. By minimizing deviations from rigidity, our registration form maintains elasticity, and by solving locally rigid transformations, regularized by structure-related constraints, we secure smoothness and optimality. We also demonstrate how data arrangement and linear path-finding models make our solution computationally efficient. Our model is applied on high quality laser triangulation data, commonly tested in 4-D plant registration studies, but is also verified on low resolution and noisy pointsets reconstructed from a limited number of images by multiview stereo (MVS). Our results demonstrate 0.1mm levels of accuracy when applied to plant species exhibiting complex geometric structures. They improve by tenfold or more state-of-the-art results and transform correctly the plants form. Paper related resources are available at PLANT4D.