| 引用本文: | 魏迎梅,康来.多视图三角化中特征点噪声尺度的自适应估算.[J].国防科技大学学报,2015,37(6):116-120 ,134.[点击复制] |
| WEI Yingmei,KANG Lai.Adaptive estimation of noise scale in feature localization for multi view triangulation[J].Journal of National University of Defense Technology,2015,37(6):116-120 ,134[点击复制] |
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| 多视图三角化中特征点噪声尺度的自适应估算 |
| 魏迎梅1, 康来2 |
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(1.国防科技大学 信息系统与管理学院， 湖南 长沙 410073;2.国防科技大学 信息系统工程重点实验室， 湖南 长沙 410073)
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| 摘要: |
| 鲁棒性多视图三角化方法通常借助重投影误差经验阈值来剔除图像对应中的错误匹配，该经验阈值的选取直接影响三维重构场景点的数量和精度。在分析图像特征点定位噪声及对极传递几何原理的基础上，建立对极传递过程不确定性的传递模型，提出一种基于核密度估计的最优噪声尺度估算方法，并将该噪声尺度作为多视图三角化中错误匹配筛选的依据。实验结果表明，该方法可以获得准确的噪声尺度估计，从而有效提升多视图三角化方法的三维重构质量。 |
| 关键词: 多视图三角化 特征点定位 高斯噪声 核密度估计 |
| DOI：10.11887/j.cn.201506022 |
| 投稿日期：2015-01-28 |
| 基金项目:国家自然科学基金资助项目（61402487） |
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| Adaptive estimation of noise scale in feature localization for multi view triangulation |
| WEI Yingmei1, KANG Lai2 |
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(1. College of Information System and Management, National University of Defense Technology, Changsha 410073, China;2. Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| Robust multi-view triangulation algorithms usually rely on an empirical reprojection error threshold to identify and remove the outliers. The selection of such threshold is critical to both the quantity of successfully reconstructed scene point and its accuracy. Based on the analysis of the noise in feature point localization and the geometry of epipolar transfer, the uncertainty propagation model in epipolar transfer was derived. A novel noise scale estimation approach based on kernel density estimation was proposed and the estimated noise scale was further incorporated into robust state-of-the-art multi-view triangulation algorithm. Experimental results demonstrate that the proposed method is able to obtain accurate estimation of noise scale and to improve the 3D reconstruction quality of multi-view triangulation algorithm significantly. |
| Keywords: image-based 3D reconstruction feature point localization Gaussian noise kernel density estimation |
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