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UASOL, a large-scale high-resolution outdoor stereo dataset.

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  • 1Institute for Computer Research, University of Alicante, P.O. Box 99, 03080, Alicante, Spain. zbauer@dccia.ua.es.

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We introduce a new dataset for outdoor depth estimation from a pedestrian's perspective, crucial for training advanced deep learning models. This dataset provides high-definition RGB images and depth maps, addressing a gap in current research.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Deep learning methods excel in depth estimation but require extensive, accurate data.
  • Existing datasets often lack outdoor, egocentric perspectives needed for real-world applications.
  • Pedestrian-centric data is vital for autonomous systems and human-computer interaction.

Purpose of the Study:

  • To introduce a novel dataset for single and stereo RGB outdoor depth estimation.
  • To provide an egocentric, pedestrian-viewpoint dataset with high-definition imagery.
  • To facilitate the development and benchmarking of advanced depth estimation algorithms.

Main Methods:

  • Acquisition of synchronized RGB frames and depth maps from a pedestrian's viewpoint.
  • Inclusion of diverse outdoor scenes with varying lighting and weather conditions.
  • Dataset features human interaction and significant data variability.

Main Results:

  • A comprehensive dataset comprising numerous high-definition color frames and corresponding depth maps.
  • The dataset captures real-world complexities, including human interactions.
  • Demonstrates the utility of the dataset for training and evaluating depth estimation models.

Conclusions:

  • The proposed dataset fills a critical need for egocentric, outdoor depth estimation data.
  • It enables more robust and accurate deep learning models for real-world applications.
  • Facilitates advancements in autonomous navigation and augmented reality systems.