{"title":"一种点云配准的学习检测器方法","authors":"Liyin Zhang, Yi Yang, Z. Xiong, Liu Chao","doi":"10.1117/12.2589346","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a Detector-Net method for point cloud registration which learns a 3D feature detector of a specific descriptor. Different from the traditional detectors, deep neural network is used to generate this detector and manual annotation of feature points is not required. Instead, we leverage the aligned point cloud to deduce distinguishing points to generate training data. The indoor point cloud dataset is used as the training set, and experimental results show that the Detector-Net has better accuracy among traditional detectors.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"11719 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A learned detector method for point cloud registration\",\"authors\":\"Liyin Zhang, Yi Yang, Z. Xiong, Liu Chao\",\"doi\":\"10.1117/12.2589346\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a Detector-Net method for point cloud registration which learns a 3D feature detector of a specific descriptor. Different from the traditional detectors, deep neural network is used to generate this detector and manual annotation of feature points is not required. Instead, we leverage the aligned point cloud to deduce distinguishing points to generate training data. The indoor point cloud dataset is used as the training set, and experimental results show that the Detector-Net has better accuracy among traditional detectors.\",\"PeriodicalId\":415097,\"journal\":{\"name\":\"International Conference on Signal Processing Systems\",\"volume\":\"11719 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Signal Processing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2589346\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Signal Processing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2589346","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A learned detector method for point cloud registration
In this paper, we propose a Detector-Net method for point cloud registration which learns a 3D feature detector of a specific descriptor. Different from the traditional detectors, deep neural network is used to generate this detector and manual annotation of feature points is not required. Instead, we leverage the aligned point cloud to deduce distinguishing points to generate training data. The indoor point cloud dataset is used as the training set, and experimental results show that the Detector-Net has better accuracy among traditional detectors.