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- W4308691335 abstract "Semantic segmentation is an important tool for computers to perceive the real world and it is the basis and key to solve high-level vision tasks. However, as the real scene contains a large number of complex objects and is also affected by factors such as occlusion, the performance of the segmentation method based on single-modal data is affected. In order to improve the accuracy of segmentation and reduce the influence of object occlusion, truncation and other factors, a 3D scene semantic segmentation framework based on 2D image features, geometric structure and global context information is proposed. It adopts a heterogeneous network to combine image and depth information effectively, thus solving the problem that the result of using single-modal data is not fine enough. Experimental results show that the proposed method is effective in understanding complex scenes." @default.
- W4308691335 created "2022-11-14" @default.
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- W4308691335 date "2022-10-03" @default.
- W4308691335 modified "2023-09-25" @default.
- W4308691335 title "Semantic Segmentation of 3D Scene based on Global Feature Fusion" @default.
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- W4308691335 doi "https://doi.org/10.1109/iaeac54830.2022.9929852" @default.
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