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REHEARSE-3D: A Multi-Modal Emulated Rain Dataset for 3D Point Cloud De-Raining.

Abu Mohammed Raisuddin1, Jesper Holmblad1, Hamed Haghighi2

  • 1School of Information Technology, Halmstad University, 30118 Halmstad, Sweden.

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Summary

Researchers introduce REHEARSE-3D, a large dataset for 3D point cloud de-raining. This dataset aids autonomous driving systems in handling adverse weather, improving safety by accurately detecting and removing raindrops.

Keywords:
4D RADARLiDARemulated rainmulti-modal datasetpoint cloud de-raining

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Area of Science:

  • Autonomous Driving Systems
  • Sensor Technology
  • Computer Vision

Background:

  • Sensor degradation, particularly from raindrops, significantly impacts LiDAR point cloud quality in autonomous driving.
  • Inaccurate measurements due to weather interference pose safety risks if systems lack weather awareness.

Purpose of the Study:

  • Introduce REHEARSE-3D, a novel, large-scale, multi-modal emulated rain dataset for 3D point cloud de-raining research.
  • Provide a valuable resource for advancing weather-aware autonomous driving technologies.

Main Methods:

  • Developed REHEARSE-3D, featuring 9.2 billion point-wise annotations, high-resolution LiDAR (LiDAR-256), and 4D RADAR point clouds.
  • Collected data under controlled weather, day/night conditions, including rain-characteristic information.
  • Benchmarked raindrop detection and removal using fused LiDAR and 4D RADAR data.

Main Results:

  • Evaluated various statistical and deep learning models for raindrop detection and removal.
  • SalsaNext and 3D-OutDet models achieved over 94% Intersection over Union (IoU) for raindrop detection.

Conclusions:

  • REHEARSE-3D is the largest point-wise annotated dataset for 3D point cloud de-raining.
  • The dataset facilitates sensor noise modeling and point-level weather impact analysis.
  • The findings demonstrate effective raindrop detection in fused sensor data, enhancing autonomous driving safety.