Fast Geometry Estimation for Phase-coding Structured Light Field
Li Liu, Sen Xiang, Huiping Deng, Jin Wu · 2020
Estimation scene geometry is an important and fundamental task in light field processing. In conventional light field, there exist homogeneous texture surfaces, which brings ambiguity and heavy computation load in estimating the depth. In this paper, we propose phase-coding structured light field (PSLF), which projects sinusoidal waveform patterns and the phase is assigned to every pixel as the code. With the EPI of PSLF, we propose a depth estimation method. To be specific, the cost is convex with respect to the inclination angle of the candidate line in the EPI, and we propose to iterate rotating the candidate line until it converges to the optimal one. In addition, to cope with problem that the candidate samples cover multiple depth layers, we propose a method to reject the outlier samples. Experimental results demonstrate that, compared with conventional LF, the proposed PSLF improves the depth quality with mean absolute error being 0.007 pixels. In addition, the proposed optimization-based depth estimation method improves efficiency obviously with the processing speed being about 2.71 times of the tradition method.