LAUSR

Three-dimensional (3-D) measurement of metal surfaces is one of the fundamental tasks for product life-cycle management of complex equipment, which is meaningful but challenging due to its optical characteristics of high reflectivity. To reliably reconstruct 3-D metal surfaces, the commonly used techniques heavily rely on multiple exposures for optimal fusion but do not fit to high-efficiency monitoring. To alleviate this reliance, we propose a novel single-exposure method called deep sinusoidal prior (DSP) for damaged phase recovery of highly reflective surfaces. Specifically, the sinusoidal hypothesis is instilled into an untrained deep neural network (DNN) as two-stream information, in order to bypass the problem of brightness enhancement. Utilizing elaborately designed loss functions, this approach enables us to restore the accurate phase encoding by fitting the DNN to two-stream sinusoidal priors. Experimental results demonstrate that the proposed DSP method has superior performances on damaged phase recovery requiring no training samples. For instance, measuring a standard workpiece, absolute errors of the DSP method have been decreased substantially (81.69% and 59.49%) compared with the direct measurement and achieved similar accuracy (0.0754 versus 0.0744 mm) compared with the reference. Most strikingly, the proposed method, for the first time, demonstrates a new perspective of recovering the reliable phase from a degraded one itself, contributing to the superior generalization capability insensitive to fringe frequencies, imaging settings, and variant scenes.