DE-Net: A Dual-Encoder Network for Local and Long-Distance Context Information Extraction in Semantic Segmentation of Large-Scale Scene Point Clouds
Semantic segmentation of large-scale point clouds is essential for applications such as autonomous driving and high-definition mapping. However, this task remains challenging due to the imbalanced distribution of categories in large-scale point cloud data and the similarity in local geometric struct...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10652235/ |
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| author | Zhipeng He Jing Liu Shuai Yang |
| author_facet | Zhipeng He Jing Liu Shuai Yang |
| author_sort | Zhipeng He |
| collection | DOAJ |
| description | Semantic segmentation of large-scale point clouds is essential for applications such as autonomous driving and high-definition mapping. However, this task remains challenging due to the imbalanced distribution of categories in large-scale point cloud data and the similarity in local geometric structures. Most current deep learning–based methods concentrate on designing local feature extraction modules while neglecting the significance of long-distance contextual information. Nevertheless, this contextual information is crucial for accurate object segmentation in large-scale scenes. To address this limitation, we propose a dual-encoder segmentation network called DE-Net. DE-Net effectively learns both the local and long-distance contextual information for each point to achieve accurate point segmentation. DE-Net consists of two main components: dual-encoder modules (DEMs) and gradient-aware pooling modules (GAPM). DEMs extract local geometry and long-distance contextual information for each point using positional and trigonometric encoding to distinguish complex geometric features. GAPMs aggregate global information effectively using dual-distance and <italic>xy</italic> gradient information. In addition, a prediction jitter module was introduced during training to address the issue of class imbalance and improve the network's prediction results. The experimental results on three public benchmarks demonstrate that DE-Net outperforms existing state-of-the-art methods, achieving mean intersection over union scores of 83.5%, 61.8%, and 63.9% on Toronto-3D, WHU-MLS, and S3DIS datasets, respectively. |
| format | Article |
| id | doaj-art-eab0f0f0405f4af8b64491655a3fba4e |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-eab0f0f0405f4af8b64491655a3fba4e2025-08-20T02:55:56ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117159141592610.1109/JSTARS.2024.345070810652235DE-Net: A Dual-Encoder Network for Local and Long-Distance Context Information Extraction in Semantic Segmentation of Large-Scale Scene Point CloudsZhipeng He0https://orcid.org/0009-0008-5465-3468Jing Liu1https://orcid.org/0000-0001-5207-7614Shuai Yang2Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, ChinaKey Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, China31682 Troop of People's Liberation Army, Lanzhou, ChinaSemantic segmentation of large-scale point clouds is essential for applications such as autonomous driving and high-definition mapping. However, this task remains challenging due to the imbalanced distribution of categories in large-scale point cloud data and the similarity in local geometric structures. Most current deep learning–based methods concentrate on designing local feature extraction modules while neglecting the significance of long-distance contextual information. Nevertheless, this contextual information is crucial for accurate object segmentation in large-scale scenes. To address this limitation, we propose a dual-encoder segmentation network called DE-Net. DE-Net effectively learns both the local and long-distance contextual information for each point to achieve accurate point segmentation. DE-Net consists of two main components: dual-encoder modules (DEMs) and gradient-aware pooling modules (GAPM). DEMs extract local geometry and long-distance contextual information for each point using positional and trigonometric encoding to distinguish complex geometric features. GAPMs aggregate global information effectively using dual-distance and <italic>xy</italic> gradient information. In addition, a prediction jitter module was introduced during training to address the issue of class imbalance and improve the network's prediction results. The experimental results on three public benchmarks demonstrate that DE-Net outperforms existing state-of-the-art methods, achieving mean intersection over union scores of 83.5%, 61.8%, and 63.9% on Toronto-3D, WHU-MLS, and S3DIS datasets, respectively.https://ieeexplore.ieee.org/document/10652235/Deep learningdual-encodersemantic segmentation3-D point cloud |
| spellingShingle | Zhipeng He Jing Liu Shuai Yang DE-Net: A Dual-Encoder Network for Local and Long-Distance Context Information Extraction in Semantic Segmentation of Large-Scale Scene Point Clouds IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning dual-encoder semantic segmentation 3-D point cloud |
| title | DE-Net: A Dual-Encoder Network for Local and Long-Distance Context Information Extraction in Semantic Segmentation of Large-Scale Scene Point Clouds |
| title_full | DE-Net: A Dual-Encoder Network for Local and Long-Distance Context Information Extraction in Semantic Segmentation of Large-Scale Scene Point Clouds |
| title_fullStr | DE-Net: A Dual-Encoder Network for Local and Long-Distance Context Information Extraction in Semantic Segmentation of Large-Scale Scene Point Clouds |
| title_full_unstemmed | DE-Net: A Dual-Encoder Network for Local and Long-Distance Context Information Extraction in Semantic Segmentation of Large-Scale Scene Point Clouds |
| title_short | DE-Net: A Dual-Encoder Network for Local and Long-Distance Context Information Extraction in Semantic Segmentation of Large-Scale Scene Point Clouds |
| title_sort | de net a dual encoder network for local and long distance context information extraction in semantic segmentation of large scale scene point clouds |
| topic | Deep learning dual-encoder semantic segmentation 3-D point cloud |
| url | https://ieeexplore.ieee.org/document/10652235/ |
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