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A neuromorphic dataset for tabletop object segmentation in indoor cluttered environment.

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Researchers introduce the Event-based Segmentation Dataset (ESD), a novel 3D dataset for benchmarking event-based segmentation algorithms. ESD provides crucial depth and instance labels for cluttered indoor scenes, advancing computer vision research.

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Fusion

Background:

  • Conventional cameras struggle with motion blur, low dynamic range, and temporal sampling limitations.
  • Existing event-based datasets lack comprehensive depth information crucial for occluded scene segmentation.

Purpose of the Study:

  • Introduce a novel, high-quality event-based dataset for benchmarking segmentation algorithms.
  • Provide a 3D spatial-temporal dataset with critical depth and instance labels for indoor object segmentation.

Main Methods:

  • Developed the Event-based Segmentation Dataset (ESD) using stereo-configured event-based cameras.
  • Collected 145 sequences with 14,166 annotated RGB frames and millions of events.
  • Annotated event-wise depth and instance labels for tabletop objects.

Main Results:

  • ESD offers a unique 3D spatial-temporal benchmark for event-based segmentation.
  • The dataset includes dense annotations for RGB, depth, and event data.
  • Presents a challenging benchmark for segmenting objects in cluttered indoor environments.

Conclusions:

  • The release of ESD aims to foster advancements in event-based segmentation research.
  • Provides a valuable resource for developing and evaluating algorithms requiring depth information.
  • Encourages the development of more robust segmentation techniques for real-world applications.