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A multi-camera dataset for depth estimation in an indoor scenario.

Giulio Marin1, Gianluca Agresti1, Ludovico Minto1

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Fusion

Background:

  • Time-of-Flight (ToF) and stereo vision are key depth acquisition technologies.
  • Each sensor type has unique strengths and weaknesses.
  • Combining data from multiple sensors can improve depth estimation accuracy.

Purpose of the Study:

  • Introduce a novel dataset for depth estimation research.
  • Facilitate the development of fusion and denoising algorithms for depth data.
  • Evaluate sensor fusion performance under varying lighting conditions.

Main Methods:

  • Acquired data using a multi-camera system: Microsoft Kinect v2 (ToF), Intel RealSense R200 (active stereo), and Stereolabs ZED (passive stereo).
  • Captured indoor scenes under diverse external lighting conditions.
  • Established depth ground truth using a line laser for all acquired scenes.

Main Results:

  • The dataset provides synchronized depth data from multiple sensors.
  • Enables testing of fusion algorithms in challenging lighting.
  • A subset was used to validate prior fusion research.

Conclusions:

  • The presented dataset is valuable for advancing depth estimation techniques.
  • It supports research in sensor fusion and denoising for ToF and stereo vision.
  • The data is suitable for evaluating algorithms under realistic indoor conditions.