Reliability-weighted fusion of multiview photogrammetric point clouds for 3D terrain reconstruction of the lunar surface

Siyan Xu, Chen Chen, Yusheng Xu, Z. Ye, Huan Xie, X. Tong
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Abstract

Benefitting from advances in photogrammetry and computer vision, 3D point clouds generated from dense image matching have been proved to be an accurate, reliable, and cost-effective data source for lunar topographic mapping. To achieve a full coverage mapping of the lunar surface, a merging of point clouds generated from multiple observations is mandatory. Due to the limit of dense matching accuracy and accumulative registration errors, integrated point clouds normally suffer from disturbed stratification, outliers, and redundant points, resulting in sharp edges between the seams of the fused point clouds. To address the seaming problems of merged point clouds and achieve a seamless fusion result, a weighted fusion strategy evaluating the reliability of points from triangulation errors in the photogrammetry process is proposed. The global registration and post-processing of point clouds are also addressed to optimize the result. With comparisons to other software outputs, a DEM with a resolution of 6 m/pixel is produced, with a lower bias to ground truth and better visualization. As a result, the proposed method can improve the completeness and precision of digital surface model to a certain extent and satisfy the application requirements.
多视点云可靠性加权融合在月球表面三维地形重建中的应用
得益于摄影测量学和计算机视觉技术的进步,密集图像匹配生成的三维点云已被证明是月球地形测绘的准确、可靠和经济有效的数据源。为了实现月球表面的全覆盖映射,必须合并由多个观测产生的点云。由于密集匹配精度和累积配准误差的限制,融合点云通常会出现扰动分层、异常点和冗余点,导致融合点云的接缝之间出现尖锐的边缘。为了解决合并点云的拼接问题,实现无缝融合,提出了一种基于摄影测量过程中三角测量误差的点可靠性加权融合策略。并对点云的全局配准和后处理进行了优化。与其他软件输出相比,生成的DEM分辨率为6米/像素,对地面真实度的偏差更低,可视化效果更好。结果表明,该方法能在一定程度上提高数字曲面模型的完整性和精度,满足应用要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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