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Experiences and achievements in automated image sequence orientation for close-range photogrammetric projects

机译:用于近距离摄影项目的自动图像序列定向的经验和成就

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Automatic image orientation of close-range image blocks is becoming a task of increasing importance in the practice of photogrammetry. Although image orientation procedures based on interactive tie point measurements do not require any preferential block structure, the use of structured sequences can help to accomplish this task in an automated way. Automatic orientation of image sequences has been widely investigated in the Computer Vision community. Here the method is generally named "Structure from Motion" (SfM), or "Structure and Motion". These refer to the simultaneous estimation of the image orientation parameters and 3D object points of a scene from a set of image correspondences. Such approaches, that generally disregard camera calibration data, do not ensure an accurate 3D reconstruction, which is a requirement for photogrammetric projects. The major contribution of SfM is therefore viewed in the photogrammetric community as a powerful tool to automatically provide a dense set of tie points as well as initial parameters for a final rigorous bundle adjustment. The paper, after a brief overview of automatic procedures for close-range image sequence orientation, will show some characteristic examples. Although powerful and reliable image orientation solutions are nowadays available at research level, there are certain questions that are still open. Thus the paper will also report some open issues, like the geometric characteristics of the sequences, scene's texture and shape, ground constraints (control points and/or free-network adjustment), feature matching techniques, outlier rejection and bundle adjustment models.
机译:在摄影测量学的实践中,近距离图像块的自动图像定向正变得越来越重要。尽管基于交互式联系点测量的图像定向过程不需要任何优先块结构,但是结构化序列的使用可以帮助自动完成此任务。图像序列的自动定向已在计算机视觉社区中得到广泛研究。在这里,该方法通常称为“运动结构”(SfM),或“运动结构”。这些是指根据一组图像对应关系同时估计场景的图像方向参数和3D对象点。通常不考虑相机校准数据的此类方法不能确保准确的3D重建,这是摄影测量项目的要求。因此,SfM的主要贡献在摄影测量界被视为一种强大的工具,可以自动提供密集的连接点集以及用于最终严格束调整的初始参数。在简要概述了近距离图像序列定向的自动过程之后,本文将显示一些典型示例。尽管如今在研究级别可以使用功能强大且可靠的图像定向解决方案,但仍有一些问题尚待解决。因此,本文还将报告一些未解决的问题,例如序列的几何特征,场景的纹理和形状,地面约束(控制点和/或自由网络调整),特征匹配技术,离群值剔除和束调整模型。

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