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FF-Net: Feature-Fusion-Based Network for Semantic Segmentation of 3D Plant Point Cloud

Semantic segmentation of 3D point clouds has played an important role in the field of plant phenotyping in recent years. However, existing methods need to down-sample the point cloud to a relatively small size when processing large-scale plant point clouds, which contain more than hundreds of thousa...

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Autores principales: Guo, Xindong, Sun, Yu, Yang, Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181320/
https://www.ncbi.nlm.nih.gov/pubmed/37176925
http://dx.doi.org/10.3390/plants12091867
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author Guo, Xindong
Sun, Yu
Yang, Hua
author_facet Guo, Xindong
Sun, Yu
Yang, Hua
author_sort Guo, Xindong
collection PubMed
description Semantic segmentation of 3D point clouds has played an important role in the field of plant phenotyping in recent years. However, existing methods need to down-sample the point cloud to a relatively small size when processing large-scale plant point clouds, which contain more than hundreds of thousands of points, which fails to take full advantage of the high-resolution of advanced scanning devices. To address this issue, we propose a feature-fusion-based method called FF-Net, which consists of two branches, namely the voxel-branch and the point-branch. In particular, the voxel-branch partitions a point cloud into voxels and then employs sparse 3D convolution to learn the context features, and the point-branch learns the point features within a voxel to preserve the detailed point information. Finally, an attention-based module was designed to fuse the two branch features to produce the final segmentation. We conducted extensive experiments on two large plant point clouds (maize and tomato), and the results showed that our method outperformed three commonly used models on both datasets and achieved the best mIoU of 80.95% on the maize dataset and 86.65% on the tomato dataset. Extensive cross-validation experiments were performed to evaluate the generalization ability of the models, and our method achieved promising segmentation results. In addition, the drawbacks of the proposed method were analyzed, and the directions for future works are given.
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spelling pubmed-101813202023-05-13 FF-Net: Feature-Fusion-Based Network for Semantic Segmentation of 3D Plant Point Cloud Guo, Xindong Sun, Yu Yang, Hua Plants (Basel) Article Semantic segmentation of 3D point clouds has played an important role in the field of plant phenotyping in recent years. However, existing methods need to down-sample the point cloud to a relatively small size when processing large-scale plant point clouds, which contain more than hundreds of thousands of points, which fails to take full advantage of the high-resolution of advanced scanning devices. To address this issue, we propose a feature-fusion-based method called FF-Net, which consists of two branches, namely the voxel-branch and the point-branch. In particular, the voxel-branch partitions a point cloud into voxels and then employs sparse 3D convolution to learn the context features, and the point-branch learns the point features within a voxel to preserve the detailed point information. Finally, an attention-based module was designed to fuse the two branch features to produce the final segmentation. We conducted extensive experiments on two large plant point clouds (maize and tomato), and the results showed that our method outperformed three commonly used models on both datasets and achieved the best mIoU of 80.95% on the maize dataset and 86.65% on the tomato dataset. Extensive cross-validation experiments were performed to evaluate the generalization ability of the models, and our method achieved promising segmentation results. In addition, the drawbacks of the proposed method were analyzed, and the directions for future works are given. MDPI 2023-05-01 /pmc/articles/PMC10181320/ /pubmed/37176925 http://dx.doi.org/10.3390/plants12091867 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guo, Xindong
Sun, Yu
Yang, Hua
FF-Net: Feature-Fusion-Based Network for Semantic Segmentation of 3D Plant Point Cloud
title FF-Net: Feature-Fusion-Based Network for Semantic Segmentation of 3D Plant Point Cloud
title_full FF-Net: Feature-Fusion-Based Network for Semantic Segmentation of 3D Plant Point Cloud
title_fullStr FF-Net: Feature-Fusion-Based Network for Semantic Segmentation of 3D Plant Point Cloud
title_full_unstemmed FF-Net: Feature-Fusion-Based Network for Semantic Segmentation of 3D Plant Point Cloud
title_short FF-Net: Feature-Fusion-Based Network for Semantic Segmentation of 3D Plant Point Cloud
title_sort ff-net: feature-fusion-based network for semantic segmentation of 3d plant point cloud
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181320/
https://www.ncbi.nlm.nih.gov/pubmed/37176925
http://dx.doi.org/10.3390/plants12091867
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