  Panoramic images have advantages in information capacity and scene stability
due to their large field of view (FoV). In this paper, we propose a method to
synthesize a new dataset of panoramic image. We managed to stitch the images
taken from different directions into panoramic images, together with their
labeled images, to yield the panoramic semantic segmentation dataset
denominated as SYNTHIA-PANO. For the purpose of finding out the effect of using
panoramic images as training dataset, we designed and performed a comprehensive
set of experiments. Experimental results show that using panoramic images as
training data is beneficial to the segmentation result. In addition, it has
been shown that by using panoramic images with a 180 degree FoV as training
data the model has better performance. Furthermore, the model trained with
panoramic images also has a better capacity to resist the image distortion.
