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Expanding Sparse Radar Depth Based on Joint Bilateral Filter for Radar-Guided Monocular Depth Estimation.

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Summary

This study introduces a novel radar expansion technique for monocular depth estimation, significantly increasing radar point density and reducing errors. The method achieves state-of-the-art performance in radar-guided depth estimation.

Keywords:
depth estimationjoint bilateral filtermultimodalitynuScenesradarsignal expansion

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Fusion

Background:

  • Monocular depth estimation benefits from radar data for improved accuracy and robustness.
  • Existing radar expansion methods struggle with sparse data and require lidar supervision.

Purpose of the Study:

  • To develop a novel radar expansion technique for radar-guided monocular depth estimation.
  • To enhance the density and quality of radar data for depth estimation tasks.

Main Methods:

  • A novel radar expansion technique inspired by the joint bilateral filter.
  • Utilizing spatial and range kernels to calculate confidence scores for radar point expansion.
  • Implementing a range-aware window size for radar expansion.

Main Results:

  • Successfully increased radar points from an average of 39 to 100,000 per frame.
  • Achieved reduced intrinsic errors in expanded radar data compared to raw radar and prior methods.
  • Demonstrated state-of-the-art performance on the nuScenes dataset.

Conclusions:

  • The proposed radar expansion method significantly improves radar data utility for monocular depth estimation.
  • This technique offers a cost-effective and robust solution for depth estimation in various conditions.
  • The approach advances the field of sensor fusion for autonomous systems.