Abstract. There is a high demand of 3D data for 360?
panoramic images and
videos, pushed by the growing availability on the market of specialized hardware
for both capturing (e.g., omni-directional cameras) as well as visualizing in 3D
(e.g., head mounted displays) panoramic images and videos. At the same time, 3D
sensors able to capture 3D panoramic data are expensive and/or hardly available.
To fill this gap, we propose a learning approach for panoramic depth map estimation from a single image. Thanks to a specifically developed distortion-aware
deformable convolution filter, our method can be trained by means of conventional perspective images, then used to regress depth for panoramic images, thus
bypassing the effort needed to create annotated panoramic training dataset. We
also demonstrate our approach for emerging tasks such as panoramic monocular
SLAM, panoramic semantic segmentation and panoramic style transfer