mlsanityData to 7 Oct 2026

Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth Estimation

arXiv 2204.02386 ↗First posted 5 Apr 2022cs.CVcited by 6

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Abstract (arXiv)

Deep-learning-based approaches to depth estimation are rapidly advancing, offering superior performance over existing methods. To estimate the depth in real-world scenarios, depth estimation models require the robustness of various noise environments. In this work, a Pyramid Frequency Network(PFN) with Spatial Attention Residual Refinement Module(SARRM) is proposed to deal with the weak robustness of existing deep-learning methods. To reconstruct depth maps with accurate details, the SARRM constructs a residual fusion method with an attention mechanism to refine the blur depth. The frequency division strategy is designed, and the frequency pyramid network is developed to extract features from multiple frequency bands. With the frequency strategy, PFN achieves better visual accuracy than state-of-the-art methods in both indoor and outdoor scenes on Make3D, KITTI depth, and NYUv2 datasets. Additional experiments on the noisy NYUv2 dataset demonstrate that PFN is more reliable than existing deep-learning methods in high-noise scenes.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 32 of its 60 references that are in the map. First posted on arXiv 5 Apr 2022.

Builds on

32 of 60 references.

References · 32

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20 more
  1. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 341
  2. 2019A Fourier Perspective on Model Robustness in Computer Visioncited by 288
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  4. 2015DeepStereo: Learning to Predict New Views from the World's Imagerycited by 185
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  6. 2017Learning Depth from Monocular Videos using Direct Methodscited by 154
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  8. 2017Semi-Supervised Deep Learning for Monocular Depth Map Predictioncited by 109
  9. 2018Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Networkcited by 109
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  11. 2018Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometrycited by 85
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  17. 2019SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimationcited by 59
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Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 2.

6 papers.

  1. 2023Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objectscited by 8
  2. 2024Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Priorcited by 7
  3. 2025Journey into Automation: Image-Derived Pavement Texture Extraction and Evaluationcited by 7
  4. 2025Depth-Aware Super-Resolution via Distance-Adaptive Variational Formulationcited by 4
  5. 2024City Scene Super-Resolution via Geometric Error Minimizationcited by 1
  6. 2024Semi-supervised Chinese Poem-to-Painting Generation via Cycle-consistent Adversarial Networkscited by 1