The quality of 3D scene reconstruction and monitoring through structure‐from‐motion multiview stereo (SfM‐MVS) depends on critical key factors, including camera calibration and image network geometry. The goal of this paper is to examine the monitoring ability of an SfM‐MVS workflow based on four or more ground‐based digital single‐lens reflex (DSLR) cameras and to estimate differences when adopting both fixed and variable camera positions and orientations. This was achieved by conducting work on a scaled laboratory testfield and a sea cliff. Tests demonstrate that a monitoring system using just four fixed cameras can achieve valuable monitoring capabilities and tolerate imperfections in the camera calibration. Furthermore, such a configuration can achieve accuracies comparable to terrestrial laser scanning (TLS) and drone‐based photogrammetry. The study demonstrates that minimising registration errors between point clouds is critical. The “registration SIFT” approach could resolve such problems.

中文翻译：

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使用固定摄像机优化SfM‐MVS边坡监控系统的质量

通过结构从运动的多视图立体声（SfM-MVS）进行3D场景重建和监视的质量取决于关键的关键因素，包括摄像机校准和图像网络几何形状。本文的目的是研究基于四个或更多基于地面的数字单镜反光（DSLR）摄像机的SfM-MVS工作流程的监视能力，并估计在采用固定和可变摄像机位置和方向时的差异。这是通过在规模较大的实验室测试场和海崖上进行工作来实现的。测试表明，仅使用四个固定摄像机的监视系统可以实现有价值的监视功能，并可以承受摄像机校准中的缺陷。此外，这种配置可以实现与地面激光扫描（TLS）和基于无人机的摄影测量法相当的精度。该研究表明，最小化点云之间的配准错误至关重要。“注册SIFT”方法可以解决此类问题。