Deformed Phase Prediction Using SVM for Structured Light Depth Generation

Abstract

In phase-based structured light, absolute phase unwrapping, which is a cumbersome step, is often considered necessary before calculating depth. In this paper, we notice that depth is only related to the deformed phase but not the absolute unwrapped phase. Furthermore, the deformed phase is highly related to the changes of the wrapped reference and captured phases. Based on these findings, we propose a classification-based scheme that can directly report deformed phase. To be specific, we cast the problem of inferring fringe order difference as a multi-class classification task, where phase samples within half a period are fed to the classifier and the fringe oder difference is the class. Besides, we use a radial basis function support vector machine as the classifier. In such a manner, for every pixel, the deformed phase can be obtained directly without knowing the absolute unwrapped phase. Moreover, the proposed method only needs phase from a single frequency and is pixel-independent, so it is free from troubles such as poor real-time performance in temporal unwrapping or error accumulation in spatial unwrapping. Experiments on 3dsmax data and real-captured data prove that the proposed method can produce high quality depth maps.