无需高分辨率图像，用2D表示实现实时单视角3D超分辨率。
2D Representation for Unguided Single-View 3D Super-Resolution in Real-Time
- 将3D几何信息编码为规则2D图像，直接复用2D超分模型
- Swin Transformer版达顶尖精度，Vision Mamba版实现实时推理
- 适合无高分辨率图像可用的真实场景应用
我们提出2Dto3D-SR，一种实时单视角3D超分辨率框架，无需高分辨率RGB引导。该框架将单视角3D数据编码为结构化2D表示，使现有2D图像超分辨率架构可直接应用。通过投影归一化坐标码（PNCC）将可见表面的3D几何信息表示为规则图像，规避了3D点云或RGB引导方法的复杂性。该设计支持轻量高效模型，适用于多种部署环境。我们采用两种实现：基于Swin Transformer的高精度版本和基于Vision Mamba的高效率版本。实验表明，Swin Transformer模型在标准基准上达到当前最优精度，Vision Mamba模型在实时速度下表现良好。这证明了我们的几何引导流程是一种简单、可行且实用的解决方案，尤其适用于无法获取高分辨率RGB数据的实际场景。
原文摘要 · Abstract (English)
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.
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