Semantic segmentation of 3D point cloud based on contextual attention CNN |
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Authors: | Jun YANG Jisheng DANG |
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Affiliation: | School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China |
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Abstract: | Aiming at the under-segmentation of 3D point cloud semantic segmentation caused by the lack of contextual fine-grained information of the point cloud,an algorithm based on contextual attention CNN was proposed for 3D point cloud semantic segmentation.Firstly,the fine-grained features in local area of the point cloud were mined through the attention coding mechanism.Secondly,the contextual features between multi-scale local areas were captured by the contextual recurrent neural network coding mechanism and compensated with the fine-grained local features.Finally,the multi-head mechanism was used to enhance the generalization ability of the network.Experiments show that the mIoU of the proposed algorithm on the three standard datasets of ShapeNet Parts,S3DIS and vKITTI are 85.4%,56.7% and 38.1% respectively,which has good segmentation performance and good generalization ability. |
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Keywords: | 3D point cloud semantic segmentation contextual attention convolution layer convolutional neural network deep learning |
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