Accurate and dense point cloud generation for industrial Measurement via target-free photogrammetry

摘要

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.