Phase Unwrapping for Time-of-Flight Sensor Based on Image Segmentation

Phase unwrapping is a fundamental problem in Time-of-Flight (ToF) imaging, especially when high modulation frequency is used to achieve precise measurement accuracy. This paper proposes a novel double-frequency based phase unwrapping method for ToF sensors. The new method incorporates the idea of image segmentation to solve phase unwrapping region-by-region instead of pixel-by-pixel. The depth image is segmented based on the constraint between depth measurements and wrap numbers at two modulation frequencies. To avoid misclassification of the pixels around phase jump edges in the presence of inevitable noise, we employ a modified distance function to classify the edge pixels into the corresponding connected area. Furthermore, a graph model is used to accurately model the phase jumping relation between different connected areas. On this basis, we propose an MRF framework to fuse the amplitude and depth information to unwrap the phase. Experimental results on both synthetic and real-world data demonstrate that, the proposed method is robust to noise and outperforms state-of-the-art methods, while being highly efficient enabling real-time running.