{"title":"基于快速定向和旋转图像的相机姿态估计鲁棒图像匹配","authors":"Junqi Bao, Xiaochen Yuan, C. Lam","doi":"10.1145/3579654.3579720","DOIUrl":null,"url":null,"abstract":"This paper presents a novel image matching method for camera pose estimation based on point cloud segmentation. The Oriented Fast and Rotated Brief (ORB) is employed to extract the key points, which are then extracted based on matched point cloud planes. The point cloud planes are segmented based on the depth image, and then matched by the distance of the centroid between planes. The putative corresponding key points on the planes are generated based on the distance of their 3-D coordinates and the descriptors of the key points are further matched based on the putative corresponding key points. As an additional constraint, the spatial relative position in 3-D spaces solves the problem that the descriptors of each key point in some scenarios are too similar which may lead to a mismatch. According to the experimental results, the superiority of the proposed approach is illustrated by comparing with the existing matching methods.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust Image Matching for Camera Pose Estimation Using Oriented Fast and Rotated Brief\",\"authors\":\"Junqi Bao, Xiaochen Yuan, C. Lam\",\"doi\":\"10.1145/3579654.3579720\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel image matching method for camera pose estimation based on point cloud segmentation. The Oriented Fast and Rotated Brief (ORB) is employed to extract the key points, which are then extracted based on matched point cloud planes. The point cloud planes are segmented based on the depth image, and then matched by the distance of the centroid between planes. The putative corresponding key points on the planes are generated based on the distance of their 3-D coordinates and the descriptors of the key points are further matched based on the putative corresponding key points. As an additional constraint, the spatial relative position in 3-D spaces solves the problem that the descriptors of each key point in some scenarios are too similar which may lead to a mismatch. According to the experimental results, the superiority of the proposed approach is illustrated by comparing with the existing matching methods.\",\"PeriodicalId\":146783,\"journal\":{\"name\":\"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3579654.3579720\",\"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 of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3579654.3579720","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robust Image Matching for Camera Pose Estimation Using Oriented Fast and Rotated Brief
This paper presents a novel image matching method for camera pose estimation based on point cloud segmentation. The Oriented Fast and Rotated Brief (ORB) is employed to extract the key points, which are then extracted based on matched point cloud planes. The point cloud planes are segmented based on the depth image, and then matched by the distance of the centroid between planes. The putative corresponding key points on the planes are generated based on the distance of their 3-D coordinates and the descriptors of the key points are further matched based on the putative corresponding key points. As an additional constraint, the spatial relative position in 3-D spaces solves the problem that the descriptors of each key point in some scenarios are too similar which may lead to a mismatch. According to the experimental results, the superiority of the proposed approach is illustrated by comparing with the existing matching methods.