mlsanityData to 14 Oct 2026

2D Representation for Unguided Single-View 3D Super-Resolution in Real-Time

arXiv 2511.08224 ↗First posted 11 Nov 2025cs.CV cs.AIcited by 0

On the map: State-space sequence models (S4, Mamba) → Vision Mamba backbones

Abstract (arXiv)

We introduce 2Dto3D-SR, a versatile framework for real-time single-view 3D super-resolution that eliminates the need for high-resolution RGB guidance. Our framework encodes 3D data from a single viewpoint into a structured 2D representation, enabling the direct application of existing 2D image super-resolution architectures. We utilize the Projected Normalized Coordinate Code (PNCC) to represent 3D geometry from a visible surface as a regular image, thereby circumventing the complexities of 3D point-based or RGB-guided methods. This design supports lightweight and fast models adaptable to various deployment environments. We evaluate 2Dto3D-SR with two implementations: one using Swin Transformers for high accuracy, and another using Vision Mamba for high efficiency. Experiments show the Swin Transformer model achieves state-of-the-art accuracy on standard benchmarks, while the Vision Mamba model delivers competitive results at real-time speeds. This establishes our geometry-guided pipeline as a surprisingly simple yet viable and practical solution for real-world scenarios, especially where high-resolution RGB data is inaccessible.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Vision Mamba backbones, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 28 of its 44 references that are in the map. First posted on arXiv 11 Nov 2025.

Builds on

28 of 44 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024DVMSR: Distillated Vision Mamba for Efficient Super-Resolution5 May

Other references · 27

  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  2. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
  3. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
  4. 2022Activating More Pixels in Image Super-Resolution Transformercited by 438
  5. 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Modelscited by 415
  6. 2023ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shiftingcited by 365
  7. 2018PU-Net: Point Cloud Upsampling Networkcited by 209
  8. 2019PU-GAN: a Point Cloud Upsampling Adversarial Networkcited by 164
  9. 2019Pixel-Adaptive Convolutional Neural Networkscited by 126
  10. 2019PU-GCN: Point Cloud Upsampling using Graph Convolutional Networkscited by 101
  11. 2024Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesiscited by 97
  12. 2021Discrete Cosine Transform Network for Guided Depth Map Super-Resolutioncited by 71
15 more
  1. 2024AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillationcited by 66
  2. 2021On Efficient Transformer-Based Image Pre-training for Low-Level Visioncited by 64
  3. 2019Deformable Kernel Networks for Joint Image Filteringcited by 59
  4. 2017Joint Image Filtering with Deep Convolutional Networkscited by 52
  5. 2020Channel Attention based Iterative Residual Learning for Depth Map Super-Resolutioncited by 40
  6. 2024DRCT: Saving Image Super-resolution away from Information Bottleneckcited by 40
  7. 2021Joint Implicit Image Function for Guided Depth Super-Resolutioncited by 31
  8. 2022Learning Graph Regularisation for Guided Super-Resolutioncited by 27
  9. 2023Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolutioncited by 19
  10. 2023Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsamplingcited by 19
  11. 2023Recurrent Structure Attention Guidance for Depth Super-Resolutioncited by 16
  12. 2019Point Cloud Super Resolution with Adversarial Residual Graph Networkscited by 13
  13. 2024Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolutioncited by 5
  14. 2024HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolutioncited by 3
  15. 2023PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit Surfacecited by 1

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  1. 2025SuperCarver: Texture-Consistent 3D Geometry Super-Resolution for High-Fidelity Surface Detail Generation
  2. 2025Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework
  3. 2026Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective
  4. 2026SRGAN-CKAN: Expressive Super-Resolution with Nonlinear Functional Operators under Minimal Resources
  5. 2026Surflo: Consistent 3D Surface Flow Model with Global State
  6. 2022Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images
  7. 2025ZeroScene: A Zero-Shot Framework for 3D Scene Generation from a Single Image and Controllable Texture Editing
  8. 2026Any Resolution Any Geometry: From Multi-View To Multi-Patch