Feature fusion network based on attention mechanism for 3D semantic segmentation of point clouds
| Autor: | Yongbin Gao, Cengsi Zhong, Heng Zhou, Zhijun Fang, Ruoxi Shang, Bo Huang |
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| Rok vydání: | 2020 |
| Předmět: |
Feature fusion
Parsing Computer science business.industry Point cloud Pattern recognition 02 engineering and technology computer.software_genre 01 natural sciences Artificial Intelligence 0103 physical sciences Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Computer Vision and Pattern Recognition Artificial intelligence Invariant (mathematics) 010306 general physics business computer Software |
| Zdroj: | Pattern Recognition Letters. 133:327-333 |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2020.03.021 |
| Popis: | 3D scene parsing has always been a hot topic and point clouds are efficient data format to represent scenes. The semantic segmentation of point clouds is critical to the 3D scene, which is a challenging problem due to the unordered structure of point clouds. The max-pooling operation is typically used to obtain the order invariant features, while the point-wise features are destroyed after the max-pooling operation. In this paper, we propose a feature fusion network that fuses point-wise features and local features by attention mechanism to compensate for the loss caused by max-pooling operation. By incorporating point-wise features into local features, the point-wise variation is preserved to obtain a refined segmentation accuracy, and the attention mechanism is used to measure the importance of the point-wise features and local features for each 3D point. Extensive experiments show that our method achieves better performances than other prestigious methods. |
| Databáze: | OpenAIRE |
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