mlsanityData to 7 Oct 2026

DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain

arXiv 2410.14980 ↗First posted 19 Oct 2024cs.CVcited by 11

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Abstract (arXiv)

In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial domain, our approach estimates the frequency coefficients of depth patches after transforming them into the discrete cosine domain. This unique formulation allows for the modeling of local depth correlations within each patch. Crucially, the frequency transformation segregates the depth information into various frequency components, with low-frequency components encapsulating the core scene structure and high-frequency components detailing the finer aspects. This decomposition forms the basis of our progressive strategy, which begins with the prediction of low-frequency components to establish a global scene context, followed by successive refinement of local details through the prediction of higher-frequency components. We conduct comprehensive experiments on NYU-Depth-V2, TOFDC, and KITTI datasets, and demonstrate the state-of-the-art performance of DCDepth. Code is available at https://github.com/w2kun/DCDepth.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 45 of its 62 references that are in the map. First posted on arXiv 19 Oct 2024.

Builds on

45 of 62 references.

References · 45

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
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  11. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  12. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
33 more
  1. 2018MegaDepth: Learning Single-View Depth Prediction from Internet Photoscited by 731
  2. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
  3. 2020AdaBins: Depth Estimation using Adaptive Binscited by 596
  4. 2018Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Drivingcited by 455
  5. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 379
  6. 2019Enforcing geometric constraints of virtual normal for depth predictioncited by 233
  7. 2020Learning to Recover 3D Scene Shape from a Single Imagecited by 214
  8. 2023Semantic-SAM: Segment and Recognize Anything at Any Granularitycited by 185
  9. 2022BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimationcited by 166
  10. 2022NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimationcited by 151
  11. 2019Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentationcited by 141
  12. 2021Transformer-Based Attention Networks for Continuous Pixel-Wise Predictioncited by 125
  13. 2022Revealing the Dark Secrets of Masked Image Modelingcited by 117
  14. 2022P3Depth: Monocular Depth Estimation with a Piecewise Planarity Priorcited by 106
  15. 2022LocalBins: Improving Depth Estimation by Learning Local Distributionscited by 92
  16. 2022Attention Attention Everywhere: Monocular Depth Prediction with Skip Attentioncited by 92
  17. 2016Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networkscited by 91
  18. 2021RigNet: Repetitive Image Guided Network for Depth Completioncited by 80
  19. 2018Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimationcited by 78
  20. 2023iDisc: Internal Discretization for Monocular Depth Estimationcited by 71
  21. 2021Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Darkcited by 57
  22. 2021Towards Real-Time Monocular Depth Estimation for Robotics: A Surveycited by 55
  23. 2023VA-DepthNet: A Variational Approach to Single Image Depth Predictioncited by 54
  24. 2023IEBins: Iterative Elastic Bins for Monocular Depth Estimationcited by 53
  25. 2023NDDepth: Normal-Distance Assisted Monocular Depth Estimationcited by 45
  26. 2021Adaptive Surface Normal Constraint for Depth Estimationcited by 43
  27. 2024Tri-Perspective View Decomposition for Geometry-Aware Depth Completioncited by 42
  28. 2018Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networkscited by 31
  29. 2022DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completioncited by 27
  30. 2022Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completioncited by 23
  31. 2023Single Image Depth Prediction Made Better: A Multivariate Gaussian Takecited by 14
  32. 2023Learnable Differencing Center for Nighttime Depth Perceptioncited by 14
  33. 2023AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimizationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 5.

11 papers.

  1. 2024Scalable Autoregressive Monocular Depth Estimationcited by 9
  2. 2025Region-aware Depth Scale Adaptation with Sparse Measurementscited by 3
  3. 2025Learning Inverse Laplacian Pyramid for Progressive Depth Completioncited by 2
  4. 2025ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Drivingcited by 1
  5. 2025BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical Worldcited by 0
  6. 2025Object Concepts Emerge from Motioncited by 0
  7. 2026Robust Depth Super-Resolution via Adaptive Diffusion Samplingcited by 0
  8. 2026Fractal Autoregressive Depth Estimation with Continuous Token Diffusioncited by 0
  9. 2026DegBins: Degradation-Driven Binning for Depth Super-Resolutioncited by 0
  10. 2026FeDepth: Federated Learning for Depth Estimation under Robot Heterogeneitycited by 0
  11. 2026Object Concepts Emerge from Motioncited by 0