{"title":"基于内容的3d对象搜索","authors":"D. Vranic","doi":"10.1109/ICCIMA.2001.970477","DOIUrl":null,"url":null,"abstract":"The topic of this paper is content-based 3D-object retrieval. The approach is based on feature vectors, which capture 3D-shape of a model represented as a triangle mesh. The feature vectors are invariant with respect to translation, rotation, scaling, and reflection and robust with respect to level-of-detail. Before the feature extraction, each 3D-object is transformed (normalized) into a canonical position and orientation. The search is performed in the feature vector space in which the feature vector of a query model is used as a key. Original normalization steps and feature vectors are presented in this communication.","PeriodicalId":232504,"journal":{"name":"Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Content-based search for 3D-objects\",\"authors\":\"D. Vranic\",\"doi\":\"10.1109/ICCIMA.2001.970477\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The topic of this paper is content-based 3D-object retrieval. The approach is based on feature vectors, which capture 3D-shape of a model represented as a triangle mesh. The feature vectors are invariant with respect to translation, rotation, scaling, and reflection and robust with respect to level-of-detail. Before the feature extraction, each 3D-object is transformed (normalized) into a canonical position and orientation. The search is performed in the feature vector space in which the feature vector of a query model is used as a key. Original normalization steps and feature vectors are presented in this communication.\",\"PeriodicalId\":232504,\"journal\":{\"name\":\"Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCIMA.2001.970477\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIMA.2001.970477","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The topic of this paper is content-based 3D-object retrieval. The approach is based on feature vectors, which capture 3D-shape of a model represented as a triangle mesh. The feature vectors are invariant with respect to translation, rotation, scaling, and reflection and robust with respect to level-of-detail. Before the feature extraction, each 3D-object is transformed (normalized) into a canonical position and orientation. The search is performed in the feature vector space in which the feature vector of a query model is used as a key. Original normalization steps and feature vectors are presented in this communication.