Autor: |
Dilong Li, Shenghong Zheng, Ziyi Chen, Xiang Li, Lanying Wang, Jixiang Du |
Jazyk: |
angličtina |
Rok vydání: |
2024 |
Předmět: |
|
Zdroj: |
International Journal of Applied Earth Observations and Geoinformation, Vol 129, Iss , Pp 103813- (2024) |
Druh dokumentu: |
article |
ISSN: |
1569-8432 |
DOI: |
10.1016/j.jag.2024.103813 |
Popis: |
Transformer networks have demonstrated remarkable performance in point cloud analysis. However, achieving a balance between local regional context and global long-range context learning remains a significant challenge. In this paper, we propose a Hierarchical Local Global Transformer Network (LGTNet), designed to capture local and global contexts in a hierarchical manner. Specifically, we employ serial local and global Transformers to learn the inner-group and cross-group self-attention, respectively. Besides, we propose a geometric moment-based position encoding for local Transformer, enabling the embedding of comprehensive local geometric relationship. Additionally, we also introduce a global feature pooling module that extracts the global features from each encoder layers. Extensive experimental results demonstrate that LGTNet achieves state-of-the-art performance on ShapeNetPart and ScanObjectNN datasets. This approach effectively enhances the understanding of point cloud scenes, thereby facilitating the use of point cloud data in remote sensing applications. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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