| 文物对象近景序列影像位姿高精度估计方法 |
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| 引用本文： | 胡春梅,夏国芳,张旭,刘喜,王伟,尉丽霞.文物对象近景序列影像位姿高精度估计方法[J].测绘通报,2022,0(1):50-55. |
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| 作者姓名： | 胡春梅 夏国芳 张旭 刘喜 王伟 尉丽霞 |
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| 作者单位： | 1. 北京建筑大学, 北京 100044;2. 中国文物信息咨询中心, 北京 100029;3. 山东正元数字城市建设有限公司, 山东 烟台 264670;4. 黑龙江测绘计量仪器检定站, 黑龙江 哈尔滨 150081 |
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| 基金项目： | 国家自然科学基金（41401536；42171416）；北京建筑大学市属高校基本科研业务费专项资金（X18230） |
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| 摘 要： | 针对文物对象影像三维纹理重建中影像位姿估计的问题，本文提出了三维数据驱动及平差的位姿高精度估计方法。首先通过影像特征提取、匹配、误匹配点剔除得到准确的同名特征；然后建立首对影像高精度位姿相对关系，并以其像对三维点数据为驱动，结合二三维索引确定后续影像的相对位姿；最后以光束法平差与LM算法确定高精度的影像位姿参数。试验表明，本文方法在确定位姿速度和位姿精度上都体现了一定的优越性，为影像后续的密集点云生成奠定了基础。
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| 关 键 词： | 序列影像 影像匹配 数据驱动 影像位姿 高精度 |
| 收稿时间： | 2021-01-18 |
| 修稿时间： | 2021-11-25 |
High-precision pose estimation method for close range sequence images of cultural relic objects |
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| HU Chunmei,XIA Guofang,ZHANG Xu,LIU Xi,WANG Wei,WEI Lixia.High-precision pose estimation method for close range sequence images of cultural relic objects[J].Bulletin of Surveying and Mapping,2022,0(1):50-55. |
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| Authors: | HU Chunmei XIA Guofang ZHANG Xu LIU Xi WANG Wei WEI Lixia |
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| Institution: | 1. Beijing University of Civil Engineering and Architecture, Beijing 100044, China;2. China Cultural Relics Information Consulting Center, Beijing 100029, China;3. Shandong Zhengyuan Digital City Construction Co., Ltd., Yantai 264670, China;4. Heilongjiang Surveying and Mapping Measuring Instrument Verification Station, Harbin 150081, China |
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| Abstract: | Aiming at the problem of pose estimation in 3D texture reconstruction of cultural relic image, this paper proposes a high-precision pose estimation method based on 3D data-driven and adjustment. Firstly, the accurate homonymous features are obtained through image feature extraction, matching and false matching point elimination. Then the high-precision pose relative relationship of the first pair of images is established, and the relative pose of the subsequent images is determined by combining the two-dimensional index driven by the 3D point data of the first pair of images. Finally, the high-precision image pose parameters are determined by beam adjustment and LM algorithm. The experimental results show that this method has certain advantages in determining the pose speed and pose accuracy, which lays the foundation for the subsequent generation of dense point cloud. |
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| Keywords: | sequence image image matching data driven image pose high-precision |
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