计算机工程与应用2024，Vol.60Issue(10)：217-226,10.DOI:10.3778/j.issn.1002-8331.2301-0230
可学习动态分组卷积神经网络的大规模点云分割
Large-Scale Point Cloud Segmentation by Learnable Dynamic Grouping Convolutional Neural Network
摘要
Abstract
There exists too much redundant interference information when large-scale point cloud semantic segmentation algorithms extract features,which results in the poor segmentation performance of neural networks.To solve this prob-lem,a learnable dynamic grouping convolutional neural network architecture is proposed to efficiently realize large-scale point cloud segmentation.Firstly,the algorithm extracts local geometric features from the input point cloud in a grouped manner and reduces the interference of useless feature information on neural network feature recognition by dynamically filtering and pruning redundant feature channels,while improving the accuracy of semantic segmentation.Secondly,a positional encoding module is built to map the position feature of the point cloud to the high-dimensional frequency domain space,so that the neural network can fully mine the feature information of the point cloud and enhance the richness of features.Finally,the extracted local geometric feature and position feature are fused,while building a learnable dynamic grouping convolutional neural network to get the final segmentation result.Experimental results show that the mIoU of this algorithm on large-scale point cloud segmentation datasets S3DIS and SemanticKITTI is 69.6%and 58.3%,respec-tively.Compared with existing point cloud semantic segmentation methods,the proposed network model has higher segmentation accuracy and fewer network parameters.关键词
大规模点云/语义分割/可学习动态分组卷积/位置编码Key words
large-scale point cloud/semantic segmentation/learnable dynamic grouping convolution/positional encoding分类
信息技术与安全科学引用本文复制引用
康玥,杨军..可学习动态分组卷积神经网络的大规模点云分割[J].计算机工程与应用,2024,60(10):217-226,10.基金项目
国家自然科学基金(42261067) （42261067）
兰州市人才创新创业项目(2020-RC-22) （2020-RC-22）
兰州交通大学天佑创新团队(TY202002) （TY202002）
甘肃省教育厅优秀研究生"创新之星"项目(2022CXZX-613). （2022CXZX-613）
