Abstract: Unlike conventional 2D images, 360 panorama contains all the visual information of the current space, so it has a wide range of applications in video surveillance and virtual reality. However, a certain angle is available at a certain time. Therefore, the significant region detection of the 360 panorama is very important to visual angle prediction. To solve this problem, we  propose a multi-angle segmentation based 360 panoramic image saliency detection. Firstly, the panoramic images are cut at multiple angles, and the segmentation results are projected to the cube to remove certain distortion. Then, the salient calculation is conducted for each cube surface through dense and sparse reconstruction. Finally, the saliency images of each surface are projected to the rectangular of the warp and weft mapping, and multi-angle fusion is made to obtain the final salient figure. The results of the 360 panorama test by manual annotation show that the algorithm can accurately detect the saliency and is better than the other methods for the saliency detection of the 360 panorama.

Key words: 360 panorama, saliency detection, multi-angle segmentation, dense reconstruction error, sparse reconstruction error