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OmniSCV: An Omnidirectional Synthetic Image Generator for Computer Vision.

Bruno Berenguel-Baeta1, Jesus Bermudez-Cameo1, Jose J Guerrero1

  • 1Instituto de Investigación en Ingeniería de Aragón, Universidad de Zaragoza, 50018 Zaragoza, Spain.

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|April 11, 2020
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Summary
This summary is machine-generated.

This study introduces a novel tool for generating omnidirectional image datasets with precise semantic and depth information. The tool synthesizes photorealistic images, including non-central projections, crucial for training advanced computer vision algorithms.

Keywords:
computer visiondeep learningimage generatornon-central systemsomnidirectional camerassemantic labelvirtual environment

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Area of Science:

  • Computer Vision
  • Image Processing
  • Robotics

Background:

  • Omnidirectional and 360º images are increasingly prevalent, necessitating specialized computer vision algorithms due to inherent distortions.
  • Training robust computer vision models requires large, annotated datasets, which are challenging to acquire for omnidirectional imagery.

Purpose of the Study:

  • To present a tool for synthesizing omnidirectional image datasets with pixel-wise semantic and depth information.
  • To address the need for high-quality, ground-truth data for training and testing omnidirectional computer vision algorithms.

Main Methods:

  • Utilizing Unreal Engine 4 to generate realistic virtual environments and capture omnidirectional images.
  • Implementing a diverse range of projection models, including central (equirectangular, cylindrical, fish-eye) and non-central systems.
  • Providing pixel-accurate semantic, depth, and camera calibration data for synthesized images.

Main Results:

  • The developed tool successfully generates photorealistic omnidirectional images with comprehensive ground-truth data.
  • Demonstrated the tool's capability by validating various computer vision tasks, including line extraction, 3D layout recovery, SLAM, and 3D reconstruction.
  • Achieved the first reported generation of photorealistic non-central omnidirectional images.

Conclusions:

  • The proposed tool significantly facilitates the creation of datasets for omnidirectional computer vision research.
  • Enables precise training and testing of algorithms for 3D vision and scene understanding using synthetically generated data.
  • Offers a versatile solution for generating diverse omnidirectional image datasets, including novel non-central projection types.