mlsanityData to 14 Oct 2026

C2PD: Continuity-Constrained Pixelwise Deformation for Guided Depth Super-Resolution

arXiv 2501.07688 ↗First posted 13 Jan 2025cs.CVcited by 8

Not on the map. Why?

Abstract (arXiv)

Guided depth super-resolution (GDSR) has demonstrated impressive performance across a wide range of domains, with numerous methods being proposed. However, existing methods often treat depth maps as images, where shading values are computed discretely, making them struggle to effectively restore the continuity inherent in the depth map. In this paper, we propose a novel approach that maximizes the utilization of spatial characteristics in depth, coupled with human abstract perception of real-world substance, by transforming the GDSR issue into deformation of a roughcast with ideal plasticity, which can be deformed by force like a continuous object. Specifically, we firstly designed a cross-modal operation, Continuity-constrained Asymmetrical Pixelwise Operation (CAPO), which can mimic the process of deforming an isovolumetrically flexible object through external forces. Utilizing CAPO as the fundamental component, we develop the Pixelwise Cross Gradient Deformation (PCGD), which is capable of emulating operations on ideal plastic objects (without volume constraint). Notably, our approach demonstrates state-of-the-art performance across four widely adopted benchmarks for GDSR, with significant advantages in large-scale tasks and generalizability.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 28 of its 49 references that are in the map. First posted on arXiv 13 Jan 2025.

Builds on

28 of 49 references.

References · 28

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16 more
  1. 2019Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusioncited by 79
  2. 2021Discrete Cosine Transform Network for Guided Depth Map Super-Resolutioncited by 71
  3. 2021Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baselinecited by 61
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  6. 2020RoutedFusion: Learning Real-time Depth Map Fusioncited by 52
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  8. 2021Dual-Camera Super-Resolution with Aligned Attention Modulescited by 34
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  10. 2021BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimationcited by 31
  11. 2023Spherical Space Feature Decomposition for Guided Depth Map Super-Resolutioncited by 28
  12. 2023SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolutioncited by 27
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  16. 2023Learned Image Reasoning Prior Penetrates Deep Unfolding Network for Panchromatic and Multi-Spectral Image Fusioncited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.