mlsanityData to 14 Oct 2026

Joint Implicit Image Function for Guided Depth Super-Resolution

arXiv 2107.08717 ↗First posted 19 Jul 2021cs.CVcited by 31

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Abstract (arXiv)

Guided depth super-resolution is a practical task where a low-resolution and noisy input depth map is restored to a high-resolution version, with the help of a high-resolution RGB guide image. Existing methods usually view this task as a generalized guided filtering problem that relies on designing explicit filters and objective functions, or a dense regression problem that directly predicts the target image via deep neural networks. These methods suffer from either model capability or interpretability. Inspired by the recent progress in implicit neural representation, we propose to formulate the guided super-resolution as a neural implicit image interpolation problem, where we take the form of a general image interpolation but use a novel Joint Implicit Image Function (JIIF) representation to learn both the interpolation weights and values. JIIF represents the target image domain with spatially distributed local latent codes extracted from the input image and the guide image, and uses a graph attention mechanism to learn the interpolation weights at the same time in one unified deep implicit function. We demonstrate the effectiveness of our JIIF representation on guided depth super-resolution task, significantly outperforming state-of-the-art methods on three public benchmarks. Code can be found at urlhttps://git.io/JC2sU.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 28 of its 62 references that are in the map. First posted on arXiv 19 Jul 2021.

Builds on

28 of 62 references.

References · 28

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  3. 2017Graph Attention Networkscited by 8,923
  4. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  5. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  6. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  7. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  8. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  9. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
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  11. 2018Residual Dense Network for Image Super-Resolutioncited by 799
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16 more
  1. 2016Semantic Scene Completion from a Single Depth Imagecited by 472
  2. 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 453
  3. 2020Local Implicit Grid Representations for 3D Scenescited by 373
  4. 2019DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstructioncited by 333
  5. 2020Deep Unfolding Network for Image Super-Resolutioncited by 254
  6. 2014Learning Rich Features from RGB-D Images for Object Detection and Segmentationcited by 127
  7. 2019Pixel-Adaptive Convolutional Neural Networkscited by 126
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  9. 2020Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentationcited by 122
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  11. 2020Semantic Scene Completion using Local Deep Implicit Functions on LiDAR Datacited by 74
  12. 2019Deformable Kernel Networks for Joint Image Filteringcited by 59
  13. 2017Joint Image Filtering with Deep Convolutional Networkscited by 52
  14. 2020RoutedFusion: Learning Real-time Depth Map Fusioncited by 52
  15. 2016A Deep Primal-Dual Network for Guided Depth Super-Resolutioncited by 12
  16. 2013A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolutioncited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 13; 2024: 5; 2025: 5; 2026: 2.

31 papers.

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  1. 2024Bidirectional Multi-Scale Implicit Neural Representations for Image Derainingcited by 54
  2. 2021An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representationcited by 48
  3. 2023Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolutioncited by 33
  4. 2022Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomographycited by 31
  5. 2023Spherical Space Feature Decomposition for Guided Depth Map Super-Resolutioncited by 28
  6. 2023SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolutioncited by 27
  7. 2023Uncovering and Quantifying Social Biases in Code Generationcited by 25
  8. 2023Guided Depth Map Super-resolution: A Surveycited by 23
  9. 2023Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolutioncited by 19
  10. 2021Implicit Neural Representation Learning for Hyperspectral Image Super-Resolutioncited by 17
  11. 2023G2-MonoDepth: A General Framework of Generalized Depth Inference from Monocular RGB+X Datacited by 13
  12. 2022Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolutioncited by 11
19 more
  1. 2024Fourier-enhanced Implicit Neural Fusion Network for Multispectral and Hyperspectral Image Fusioncited by 10
  2. 2024RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methodscited by 8
  3. 2025C2PD: Continuity-Constrained Pixelwise Deformation for Guided Depth Super-Resolutioncited by 8
  4. 2022Learning Continuous Depth Representation via Geometric Spatial Aggregatorcited by 7
  5. 2023In Search of a Data Transformation That Accelerates Neural Field Trainingcited by 7
  6. 2023Depth Super-Resolution from Explicit and Implicit High-Frequency Featurescited by 5
  7. 2025Rotation Equivariant Arbitrary-scale Image Super-Resolutioncited by 5
  8. 2023Real-time 3D Semantic Scene Completion Via Feature Aggregation and Conditioned Predictioncited by 3
  9. 2023DPF: Learning Dense Prediction Fields with Weak Supervisioncited by 3
  10. 2024PanAdapter: Two-Stage Fine-Tuning with Spatial-Spectral Priors Injecting for Pansharpeningcited by 3
  11. 2022Extreme-scale Talking-Face Video Upsampling with Audio-Visual Priorscited by 1
  12. 2023DSR-Diff: Depth Map Super-Resolution with Diffusion Modelcited by 1
  13. 2024Learning Hierarchical Color Guidance for Depth Map Super-Resolutioncited by 1
  14. 2025Multi-Order Matching Network for Alignment-Free Depth Super-Resolutioncited by 1
  15. 2026NAIMA: Semantics Aware RGB Guided Depth Super-Resolutioncited by 1
  16. 2023360^circ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfercited by 0
  17. 2025IGAF: Incremental Guided Attention Fusion for Depth Super-Resolutioncited by 0
  18. 20252D Representation for Unguided Single-View 3D Super-Resolution in Real-Timecited by 0
  19. 2026WAVE: Reversing the Guidance Hierarchy for Coarse-to-Fine Guided Depth Super-Resolutioncited by 0