{"title":"基于深度学习的大场景激光点云多维特征最优组合分类研究","authors":"Lei Wang, Zhiyong Zhang, Xiaonan Li","doi":"10.1109/ICCEAI52939.2021.00033","DOIUrl":null,"url":null,"abstract":"As the self-occlusion or occluded 3D point clouds objects in complex scenes, which could affect the accuracy of objects classification, we propose Optimal Combination of Multidimensional Features based on deep learning for large-scene laser point clouds in classification. We construct the optimal combination matrix of multidimensional features by extracting the three-dimensional features of the three-dimensional point cloud and the two-dimensional features in multiple directions. The multidimensional optimal combination features are introduced into the convolutional network. The experimental results show that effectiveness of classification for large-scale point clouds, the effectiveness of 3D feature of point cloud is higher than that of 2D feature. The classification accuracy of our method can reach 98.8% on the Large-Scene Point Cloud Oakland data set, which obtains the better classification accuracy than other classification algorithms the paper mentioned.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Research on Deep Learning Based Optimal Combination of Multidimensional Features in Large-Scene Laser Point Clouds Classification\",\"authors\":\"Lei Wang, Zhiyong Zhang, Xiaonan Li\",\"doi\":\"10.1109/ICCEAI52939.2021.00033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the self-occlusion or occluded 3D point clouds objects in complex scenes, which could affect the accuracy of objects classification, we propose Optimal Combination of Multidimensional Features based on deep learning for large-scene laser point clouds in classification. We construct the optimal combination matrix of multidimensional features by extracting the three-dimensional features of the three-dimensional point cloud and the two-dimensional features in multiple directions. The multidimensional optimal combination features are introduced into the convolutional network. The experimental results show that effectiveness of classification for large-scale point clouds, the effectiveness of 3D feature of point cloud is higher than that of 2D feature. The classification accuracy of our method can reach 98.8% on the Large-Scene Point Cloud Oakland data set, which obtains the better classification accuracy than other classification algorithms the paper mentioned.\",\"PeriodicalId\":331409,\"journal\":{\"name\":\"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCEAI52939.2021.00033\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCEAI52939.2021.00033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
针对复杂场景中自遮挡或遮挡的三维点云对象,影响目标分类精度的问题,提出了基于深度学习的大场景激光点云多维特征最优组合分类方法。通过在多个方向上提取三维点云的三维特征和二维特征,构建最优的多维特征组合矩阵。在卷积网络中引入了多维最优组合特征。实验结果表明,对大规模点云进行分类的有效性,点云的三维特征分类的有效性高于二维特征分类的有效性。本文方法在Large-Scene Point Cloud Oakland数据集上的分类准确率可达98.8%,获得了比本文提到的其他分类算法更好的分类准确率。
Research on Deep Learning Based Optimal Combination of Multidimensional Features in Large-Scene Laser Point Clouds Classification
As the self-occlusion or occluded 3D point clouds objects in complex scenes, which could affect the accuracy of objects classification, we propose Optimal Combination of Multidimensional Features based on deep learning for large-scene laser point clouds in classification. We construct the optimal combination matrix of multidimensional features by extracting the three-dimensional features of the three-dimensional point cloud and the two-dimensional features in multiple directions. The multidimensional optimal combination features are introduced into the convolutional network. The experimental results show that effectiveness of classification for large-scale point clouds, the effectiveness of 3D feature of point cloud is higher than that of 2D feature. The classification accuracy of our method can reach 98.8% on the Large-Scene Point Cloud Oakland data set, which obtains the better classification accuracy than other classification algorithms the paper mentioned.