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A graph-based approach for simultaneous semantic and instance segmentation of plant 3D point clouds

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...

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Autores principales: Mirande, Katia, Godin, Christophe, Tisserand, Marie, Charlaix, Julie, Besnard, Fabrice, Hétroy-Wheeler, Franck
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691340/
https://www.ncbi.nlm.nih.gov/pubmed/36438118
http://dx.doi.org/10.3389/fpls.2022.1012669
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author Mirande, Katia
Godin, Christophe
Tisserand, Marie
Charlaix, Julie
Besnard, Fabrice
Hétroy-Wheeler, Franck
author_facet Mirande, Katia
Godin, Christophe
Tisserand, Marie
Charlaix, Julie
Besnard, Fabrice
Hétroy-Wheeler, Franck
author_sort Mirande, Katia
collection PubMed
description 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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spelling pubmed-96913402022-11-25 A graph-based approach for simultaneous semantic and instance segmentation of plant 3D point clouds Mirande, Katia Godin, Christophe Tisserand, Marie Charlaix, Julie Besnard, Fabrice Hétroy-Wheeler, Franck Front Plant Sci Plant Science 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). Frontiers Media S.A. 2022-11-10 /pmc/articles/PMC9691340/ /pubmed/36438118 http://dx.doi.org/10.3389/fpls.2022.1012669 Text en Copyright © 2022 Mirande, Godin, Tisserand, Charlaix, Besnard and Hétroy-Wheeler https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Mirande, Katia
Godin, Christophe
Tisserand, Marie
Charlaix, Julie
Besnard, Fabrice
Hétroy-Wheeler, Franck
A graph-based approach for simultaneous semantic and instance segmentation of plant 3D point clouds
title A graph-based approach for simultaneous semantic and instance segmentation of plant 3D point clouds
title_full A graph-based approach for simultaneous semantic and instance segmentation of plant 3D point clouds
title_fullStr A graph-based approach for simultaneous semantic and instance segmentation of plant 3D point clouds
title_full_unstemmed A graph-based approach for simultaneous semantic and instance segmentation of plant 3D point clouds
title_short A graph-based approach for simultaneous semantic and instance segmentation of plant 3D point clouds
title_sort graph-based approach for simultaneous semantic and instance segmentation of plant 3d point clouds
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691340/
https://www.ncbi.nlm.nih.gov/pubmed/36438118
http://dx.doi.org/10.3389/fpls.2022.1012669
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