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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Point Cloud Compression: Impact on Object Detection in Outdoor Contexts.

Luís Garrote1,2, João Perdiz1,2, Luís A da Silva Cruz1,3

  • 1Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, Portugal.

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|August 12, 2022
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Summary
This summary is machine-generated.

Point cloud compression minimally impacts autonomous driving object detection, especially for larger objects. Using depth maps derived from compressed point clouds offers competitive performance compared to raw data.

Keywords:
depth filteringdepth mapslossy compressionmachine learningobject detectionpoint cloud

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Area of Science:

  • Computer Vision
  • Robotics
  • Autonomous Systems

Background:

  • Autonomous driving systems require advanced perception, including object detection, generating massive sensor data.
  • Onboard processing limitations necessitate offloading computation and transmitting sensor data via vehicle-to-infrastructure (V2I) or vehicle-to-vehicle (V2V) links.
  • Light Detection and Ranging (LiDAR) point clouds are crucial but voluminous, posing transmission challenges without compression.

Purpose of the Study:

  • To evaluate the impact of point cloud compression on object detection performance in autonomous driving.
  • To assess the influence of compression levels on object detection accuracy using different architectures.
  • To analyze the effect of compression on depth maps generated from point clouds.

Main Methods:

  • Object detection was performed on raw and compressed LiDAR point clouds from the KITTI dataset.
  • Two distinct object detection architectures were employed.
  • The study analyzed compression's impact on depth maps generated via two projection methods.

Main Results:

  • Low-to-medium levels of point cloud compression showed minimal degradation in object detection performance.
  • Larger objects were less affected by compression artifacts.
  • Object detection using depth maps derived from compressed point clouds demonstrated competitive results against raw point cloud data.

Conclusions:

  • Point cloud compression is a viable strategy for enabling efficient data transmission in autonomous driving systems.
  • Depth map generation from compressed point clouds presents a robust alternative for perception tasks.
  • Further research can optimize compression techniques for enhanced object detection reliability.