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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.08224 |
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| _version_ | 1866911259471904768 |
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| author | Mas, Ignasi Huerta, Ivan Morros, Ramon Ruiz-Hidalgo, Javier |
| author_facet | Mas, Ignasi Huerta, Ivan Morros, Ramon Ruiz-Hidalgo, Javier |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08224 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 2D Representation for Unguided Single-View 3D Super-Resolution in Real-Time Mas, Ignasi Huerta, Ivan Morros, Ramon Ruiz-Hidalgo, Javier Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | 2D Representation for Unguided Single-View 3D Super-Resolution in Real-Time |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2511.08224 |