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Non-central panorama indoor dataset.

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

  • 1Instituto de Investigacion en Ingenieria de Aragon (I3A), University of Zaragoza, Zaragoza, Spain.

Data in Brief
|June 30, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces the first dataset of non-central panoramas for indoor scene understanding. This new resource aids AI development by providing crucial 3D geometrical information from image distortions.

Keywords:
Computer VisionIndoor Scene UnderstandingLayout EstimationMonocular Depth EstimationNon-central PanoramasOmnidirectional Vision

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Omnidirectional images are vital for AI-driven scene understanding.
  • Existing annotated datasets lag behind the rapid development of AI algorithms.
  • Non-central panoramas offer unique geometrical information for 3D reconstruction, unlike central panoramas.

Purpose of the Study:

  • To present the first comprehensive dataset of non-central panoramas for indoor scene understanding.
  • To address the scarcity of annotated data for non-central panoramic imagery.
  • To facilitate advancements in AI algorithms that leverage 3D geometrical cues from image distortions.

Main Methods:

  • Compilation of 2574 RGB non-central panoramas from approximately 650 distinct virtual indoor environments.
  • Generation of associated depth maps and pixel-wise annotations for each panorama.
  • Inclusion of annotations such as structural edge maps, image corners, 3D room corners, and camera poses.

Main Results:

  • The dataset comprises 2574 non-central panoramas with detailed annotations.
  • It enables the retrieval of 3D information and room layout from image distortions.
  • The data is sourced from photorealistic virtual environments with automatic pixel-wise annotation.

Conclusions:

  • This dataset represents a significant contribution to the field of indoor scene understanding.
  • It provides essential data for training and evaluating AI models that utilize non-central panoramic imagery.
  • The availability of this dataset is expected to accelerate research in 3D reconstruction and scene analysis.