Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework
We investigate the problem of 3D point clouds semantic segmentation. Recently, a large amount of research work has focused on local feature aggregation. However, the foundational framework of semantic segmentation of 3D point clouds has been neglected, where the majority of current methods default t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224006678 |
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| author | Ziyin Zeng Qingyong Hu Zhong Xie Bijun Li Jian Zhou Yongyang Xu |
| author_facet | Ziyin Zeng Qingyong Hu Zhong Xie Bijun Li Jian Zhou Yongyang Xu |
| author_sort | Ziyin Zeng |
| collection | DOAJ |
| description | We investigate the problem of 3D point clouds semantic segmentation. Recently, a large amount of research work has focused on local feature aggregation. However, the foundational framework of semantic segmentation of 3D point clouds has been neglected, where the majority of current methods default to the U-Net framework. In this study, we present U-Next, a small but mighty framework designed specifically for point cloud semantic segmentation. The key innovation of this framework is to capture multi-scale hierarchical features. Specifically, we construct the U-Next by stacking multiple U-Net L1 sub-networks in a dense arrangement to diminish the semantic gap. Concurrently, it integrates feature maps across various scales to proficiently restore intricate fine-grained details. Additionally, a multi-level deep supervision mechanism is introduced for smoothing gradient propagation and facilitating network optimization. We conduct extensive experiments on benchmarks, including the indoor S3DIS dataset, the LiDAR-based outdoor Toronto3D dataset, and the urban-scale photogrammetry-based SensatUrban dataset, demonstrate the superiority of U-Next. The U-Next framework consistently exhibits significant performance enhancements across various benchmarks and baselines, demonstrating its considerable potential as a versatile point-based framework for future endeavors. The code has been released at https://github.com/zeng-ziyin/U-Next. |
| format | Article |
| id | doaj-art-b13bdfa89b964da8b611d1ca42271932 |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-b13bdfa89b964da8b611d1ca422719322025-08-20T03:11:57ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-0113610430910.1016/j.jag.2024.104309Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next frameworkZiyin Zeng0Qingyong Hu1Zhong Xie2Bijun Li3Jian Zhou4Yongyang Xu5School of Computer Science, China University of Geosciences, Wuhan, China; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaAcademy of Military Sciences, Beijing, ChinaSchool of Computer Science, China University of Geosciences, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaSchool of Computer Science, China University of Geosciences, Wuhan, China; Corresponding author.We investigate the problem of 3D point clouds semantic segmentation. Recently, a large amount of research work has focused on local feature aggregation. However, the foundational framework of semantic segmentation of 3D point clouds has been neglected, where the majority of current methods default to the U-Net framework. In this study, we present U-Next, a small but mighty framework designed specifically for point cloud semantic segmentation. The key innovation of this framework is to capture multi-scale hierarchical features. Specifically, we construct the U-Next by stacking multiple U-Net L1 sub-networks in a dense arrangement to diminish the semantic gap. Concurrently, it integrates feature maps across various scales to proficiently restore intricate fine-grained details. Additionally, a multi-level deep supervision mechanism is introduced for smoothing gradient propagation and facilitating network optimization. We conduct extensive experiments on benchmarks, including the indoor S3DIS dataset, the LiDAR-based outdoor Toronto3D dataset, and the urban-scale photogrammetry-based SensatUrban dataset, demonstrate the superiority of U-Next. The U-Next framework consistently exhibits significant performance enhancements across various benchmarks and baselines, demonstrating its considerable potential as a versatile point-based framework for future endeavors. The code has been released at https://github.com/zeng-ziyin/U-Next.http://www.sciencedirect.com/science/article/pii/S1569843224006678Point cloudSemantic segmentationNetwork architectureDeep supervision |
| spellingShingle | Ziyin Zeng Qingyong Hu Zhong Xie Bijun Li Jian Zhou Yongyang Xu Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework International Journal of Applied Earth Observations and Geoinformation Point cloud Semantic segmentation Network architecture Deep supervision |
| title | Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework |
| title_full | Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework |
| title_fullStr | Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework |
| title_full_unstemmed | Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework |
| title_short | Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework |
| title_sort | small but mighty enhancing 3d point clouds semantic segmentation with u next framework |
| topic | Point cloud Semantic segmentation Network architecture Deep supervision |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224006678 |
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