Industrial photogrammetry systems commonly require multiple coded targets to establish a global coordinate frame for relatively large objects. However, there are many industrial products that do not allow coded targets to be placed on their surfaces. We propose an accurate and dense point cloud generation approach for measuring large-sized, untextured objects. Unlike most existing industrial measurement methods, our photogrammetry approach is free of coded targets. We have three core contributions. First, a Rotation-Free Digital Image Correlation (RFDIC) method is proposed to improve the multi-view stereopsis matching precision. Second, based on the technique of structure from motion (SfM), a coarse-to-fine strategy is utilized to construct the multi-view geometry and accurately optimize camera poses. Third, the devices used are consumer-friendly and commonly available, i.e., only a digital projector and a camera are needed to obtain the dense points on the measured object surface. The experimental results show that the error of reconstructed length for a scale bar is less than 0.01 mm/m. Compared to the state-of-the-art commercial measurement system, the average error of point cloud reconstruction is about 0.055 mm, which meets the accuracy demand for industrial applications.

工业摄影测量系统通常需要多个编码目标才能为相对较大的对象建立全局坐标系。但是，许多工业产品不允许将编码目标放置在其表面上。我们提出了一种精确而密集的点云生成方法，用于测量大型，无纹理的对象。与大多数现有的工业测量方法不同，我们的摄影测量方法没有编码目标。我们有三个核心贡献。首先，提出了一种无旋转数字图像相关（RFDIC）方法，以提高多视点立体视觉的匹配精度。其次，基于从运动构造（SfM）的技术，采用了从粗到精的策略来构造多视图几何结构并准确地优化相机的姿态。第三，所使用的设备易于使用且易于使用，即只需要一台数字投影仪和一台照相机即可获得被测物体表面上的密集点。实验结果表明，比例尺重构长度误差小于0.01 mm / m。与最新的商业测量系统相比，点云重建的平均误差约为0.055 mm，可以满足工业应用的精度要求。