Noise is an important problem that affects image segmentation. A novel scheme was proposed that could accurately extract multiple objects in a noisy real-world scene. The phase map and disparity map were obtained by using the binocular structured light system based on sinusoidal fringe projection. Firstly, a disparity map was transformed into the corresponding U-disparity map. Then, according to the different projection characteristics of object and noise regions in the disparity map, the preliminary segmentation regions were obtained by using the closed region detection algorithm. In addition, the fringe modulation analysis method was used to remove the noise in the shadow region, and the accurate segmentation results were finally obtained. Experimental results and objective evaluation data indicate that the proposed segmentation algorithm in this paper is not only robust to noise but also can effectively separate the object from the horizontal support surface. It has the advantages of low computational complexity and strong anti-interference ability in different scenarios. The average segmentation accuracy is above 90%, and the best accuracy can achieve 99.2%. The average running time is almost 27 ms.