A PTV-based feature-point matching algorithm for binocular stereo photogrammetry

The procedure of feature matching is one of the most important components in binocular or multi-ocular stereoscopic photogrammetry. In this paper, a feature-point matching algorithm based on the technique of particle tracking velocimetry is proposed for the scenario of measuring complex surface morphology by dense-point three-dimensional reconstruction. The core idea is to mix the epipolar-line constraint of line-of-sight (LOS) with the measure of a global similarity pairing and estimate the depth of each feature point in an iterative way. Experimental test is conducted to verify the algorithm performance by measuring the surface topology of a wave-like model. The result demonstrates that the feature-point matching algorithm is superior to traditional LOS method in terms of accuracy and robustness. Moreover, replacing the first module of coarse matching in the proposed algorithm by LOS will save the computational cost significantly without sacrificing the measurement accuracy.