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RGB-conditioned frequency domain refinement for sparse-to-dense depth completion.

Hua Wang1, Zhipeng Tang1, Pornntiwa Pawara2

  • 1College of Computer and Data Science, Putian University, Putian, 351100, Fujian, China.

Scientific Reports
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a frequency-guided method for depth completion, improving RGB-guided dense depth prediction. The approach reduces texture artifacts and edge blurring for high-fidelity depth maps.

Keywords:
Depth completionDynamic convolutionFrequency guideModulation strategy

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Depth completion predicts dense depth maps from sparse sensor data and RGB images.
  • Current methods often exhibit texture copying and edge blurring due to direct RGB-depth feature fusion.

Purpose of the Study:

  • To develop a novel frequency-guided refinement approach for enhanced depth completion.
  • To decouple structure and texture transfer in the frequency domain to mitigate artifacts.

Main Methods:

  • Decomposition of RGB features into wavelet sub-bands.
  • Learning content-adaptive kernels for frequency-specific processing.
  • Employing reliability-aware cross-stage modulation for robust multi-scale reconstruction.

Main Results:

  • Generation of high-fidelity depth maps with sharp edges.
  • Significant suppression of texture artifacts compared to existing methods.
  • State-of-the-art performance on benchmark datasets.

Conclusions:

  • The proposed frequency-guided refinement effectively addresses limitations in RGB-guided depth completion.
  • Decoupling structure and texture via frequency domain processing yields superior results.
  • The method offers a robust and accurate solution for generating high-quality depth maps.