1 research outputs found
Spherical Image Generation from a Single Normal Field of View Image by Considering Scene Symmetry
Spherical images taken in all directions (360 degrees) allow representing the
surroundings of the subject and the space itself, providing an immersive
experience to the viewers. Generating a spherical image from a single
normal-field-of-view (NFOV) image is convenient and considerably expands the
usage scenarios because there is no need to use a specific panoramic camera or
take images from multiple directions; however, it is still a challenging and
unsolved problem. The primary challenge is controlling the high degree of
freedom involved in generating a wide area that includes the all directions of
the desired plausible spherical image. On the other hand, scene symmetry is a
basic property of the global structure of the spherical images, such as
rotation symmetry, plane symmetry and asymmetry. We propose a method to
generate spherical image from a single NFOV image, and control the degree of
freedom of the generated regions using scene symmetry. We incorporate
scene-symmetry parameters as latent variables into conditional variational
autoencoders, following which we learn the conditional probability of spherical
images for NFOV images and scene symmetry. Furthermore, the probability density
functions are represented using neural networks, and scene symmetry is
implemented using both circular shift and flip of the hidden variables. Our
experiments show that the proposed method can generate various plausible
spherical images, controlled from symmetric to asymmetric.Comment: 15 page