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PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation.

Haojie Liu1, Kang Liao1, Chunyu Lin1

  • 1Beijing Key Laboratory of Advanced Information Science and Network, Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China.

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|March 18, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Pseudo-LiDAR interpolation network (PLIN) to boost LiDAR sensor data frequency. PLIN generates high-quality point clouds, enabling better synchronization with high-frequency cameras in multi-sensor systems.

Keywords:
3D point cloudconvolutional neural networksdepth completionpseudo-LiDAR interpolationvideo interpolation

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Fusion

Background:

  • LiDAR sensors provide reliable 3D spatial data at low frequencies (approx. 10 Hz), crucial for autonomous driving and UAVs.
  • Integrating LiDAR with higher-frequency sensors like cameras (approx. 20 Hz) requires matching sensor data rates, often by reducing camera frequency.
  • This mismatch limits the potential of multi-sensor systems in applications demanding real-time, high-resolution data.

Purpose of the Study:

  • To develop a novel deep learning framework, the Pseudo-LiDAR Interpolation Network (PLIN), for increasing LiDAR sensor data frequency.
  • To generate temporally and spatially coherent, high-quality LiDAR point cloud sequences that match camera frequencies.
  • To enhance multi-sensor system performance by enabling higher effective LiDAR data rates without compromising quality.

Main Methods:

  • Proposed a novel Pseudo-LiDAR Interpolation Network (PLIN) for generating intermediate LiDAR point clouds.
  • Implemented a coarse interpolation stage using consecutive sparse depth maps and motion information.
  • Designed a refined interpolation stage incorporating realistic scene priors for enhanced accuracy.
  • Employed a coarse-to-fine cascade structure to progressively integrate multi-modal information.

Main Results:

  • PLIN successfully generates temporally and spatially high-quality point cloud sequences.
  • The method achieves promising performance on the KITTI dataset, outperforming traditional interpolation techniques.
  • PLIN significantly surpasses state-of-the-art video interpolation methods in generating pseudo-LiDAR data.
  • Demonstrated the effectiveness of the coarse-to-fine cascade structure in perceiving multi-modal information.

Conclusions:

  • PLIN is the first deep framework specifically designed for Pseudo-LiDAR point cloud interpolation.
  • The proposed network effectively increases LiDAR sensor frequency, enabling better synchronization with cameras.
  • PLIN offers significant potential for improving navigation systems that rely on integrated LiDAR and camera data.