A three-dimensional (3D) panorama provides a 360° perspective for users, giving them a strong 3D sense of reality. Although researchers have developed a large number of algorithms to detect salient areas in two-dimensional and 3D images in recent years, there are few studies on the saliency detection of stereoscopic panoramic images. All available context information in an equirectangular projection (ERP) image which is used as the global information is used, the cubic projection (CMP) image is used as local information, and global and local visual saliency maps are integrated, taking into account the projection characteristics of the panoramic image and the CMP image which helps to eliminate the distortion and frame effect caused by the top and bottom. In this work, the proposed stereo panoramic saliency detection model is composed of two parts, i.e., color similarity and regional contrast methods. First, multi-scale linear iterative clustering superpixel segmentation is carried out on the images, and the color contrast feature map is obtained according to the color difference of pixel blocks. Then, the regional contrast is calculated according to the compactness of spatial distribution. The saliency map is obtained based on feature maps of color contrast and regional contrast. The final stereoscopic panoramic saliency map is obtained by combining equatorial migration and fusing depth information. Finally, the results obtained are compared and verified in the public stereo panoramic image database ODI. Experimental results show that the saliency maps obtained by the proposed method have high precision, recall rate, and F-measure value, and the comprehensive performance of the proposed method is better than that of the six classical saliency prediction algorithms. The proposed model not only makes full use of the image information, but also effectively suppresses the complex background area, so as to obtain the saliency map that is more consistent with the visual perception.