In this paper, we introduce a new method for the spacetime registration of a growing plant that is based on matching the plant at different geometric scales. The proposed method starts with the creation of a topological skeleton of the plant at each time step. This skeleton is then used to segment the plant into parts that we call branches. Then these branches are further divided into smaller segments that possess a simple geometric structure. These segments are matched between two time steps using a random forest classifier based on their topological and geometric features. Then, for each pair of segments matched, a point-wise registration is devised using a non-rigid registration method based on a local ICP.We applied our method to various types of plants, including arabidopsis, tomato plant and maize. We established three different metrics for 3D point-wise shape correspondence to test the accuracy, continuity, and cycle consistency of the mapping. We then compared our method with the state-of-the-art. Our results show that our approach achieves better or similar results with a shorter running time.
Accurate simultaneous semantic and instance segmentation of a plant 3D point cloud is critical for automatic plant phenotyping. Classically, each organ of the plant is detected based on the local geometry of the point cloud, but the consistency of the global structure of the plant is rarely assessed. We propose a two-level, graph-based approach for the automatic, fast and accurate segmentation of a plant into each of its organs with structural guarantees. We compute local geometric and spectral features on a neighbourhood graph of the points to distinguish between linear organs (main stem, branches, petioles) and two-dimensional ones (leaf blades) and even 3-dimensional ones (apices). Then a quotient graph connecting each detected macroscopic organ to its neighbors is used both to refine the labelling of the organs and to check the overall consistency of the segmentation. A refinement loop allows to correct segmentation defects. The method is assessed on both synthetic and real 3D point-cloud data sets of Chenopodium album (wild spinach) and Solanum lycopersicum (tomato plant).
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