OmniSCV: An Omnidirectional Synthetic Image Generator for Computer
  Vision
Bruno Berenguel-Baeta Jesus Bermudez-Cameo Jose J. Guerrero
Omnidirectional and 360{\deg} images are becoming widespread in industry and
in consumer society, causing omnidirectional computer vision to gain attention.
Their wide field of view allows the gathering of a great amount of information
about the environment from only an image. However, the distortion of these
images requires the development of specific algorithms for their treatment and
interpretation. Moreover, a high number of images is essential for the correct
training of computer vision algorithms based on learning. In this paper, we
present a tool for generating datasets of omnidirectional images with semantic
and depth information. These images are synthesized from a set of captures that
are acquired in a realistic virtual environment for Unreal Engine 4 through an
interface plugin. We gather a variety of well-known projection models such as
equirectangular and cylindrical panoramas, different fish-eye lenses,
catadioptric systems, and empiric models. Furthermore, we include in our tool
photorealistic non-central-projection systems as non-central panoramas and
non-central catadioptric systems. As far as we know, this is the first reported
tool for generating photorealistic non-central images in the literature.
Moreover, since the omnidirectional images are made virtually, we provide
pixel-wise information about semantics and depth as well as perfect knowledge
of the calibration parameters of the cameras. This allows the creation of
ground-truth information with pixel precision for training learning algorithms
and testing 3D vision approaches. To validate the proposed tool, different
computer vision algorithms are tested as line extractions from dioptric and
catadioptric central images, 3D Layout recovery and SLAM using equirectangular
panoramas, and 3D reconstruction from non-central panoramas.