Recently, 3D Gaussian Splatting (3DGS) has gained popularity as a novel explicit 3D representation. This approach relies on the representation power of Gaussian primitives to provide a high-quality rendering. However, primitives optimized at low resolution inevitably exhibit sparsity and texture deficiency, posing a challenge for achieving high-resolution novel view synthesis (HRNVS). To address this problem, we propose Super-Resolution 3D Gaussian Splatting (SRGS) to perform the optimization in a high-resolution (HR) space. The sub-pixel constraint is introduced for the increased viewpoints in HR space, exploiting the sub-pixel cross-view information of the multiple low-resolution (LR) views. The gradient accumulated from more viewpoints will facilitate the densification of primitives. Furthermore, a pre-trained 2D super-resolution model is integrated with the sub-pixel constraint, enabling these dense primitives to learn faithful texture features. In general, our method focuses on densification and texture learning to effectively enhance the representation ability of primitives. Experimentally, our method achieves high rendering quality on HRNVS only with LR inputs, outperforming state-of-the-art methods on challenging datasets such as Mip-NeRF 360 and Tanks & Temples. Related codes will be released upon acceptance.

翻译：近年来，三维高斯泼溅（3DGS）作为一种新颖的显式三维表征方法受到广泛关注。该方法依赖高斯图元的表征能力以提供高质量渲染。然而，在低分辨率下优化的图元不可避免地表现出稀疏性和纹理缺失，这对实现高分辨率新视角合成（HRNVS）提出了挑战。为解决此问题，我们提出超分辨率三维高斯泼溅（SRGS），在高分辨率（HR）空间中进行优化。针对HR空间中增加的视点，我们引入了亚像素约束，以利用多个低分辨率（LR）视图之间的亚像素跨视角信息。来自更多视点的梯度累积将促进图元的致密化。此外，我们将预训练的二维超分辨率模型与亚像素约束相结合，使这些致密化的图元能够学习到准确的纹理特征。总体而言，我们的方法侧重于致密化和纹理学习，以有效增强图元的表征能力。实验表明，我们的方法仅使用LR输入即可在HRNVS上实现高渲染质量，在Mip-NeRF 360和Tanks & Temples等具有挑战性的数据集上超越了现有先进方法。相关代码将在论文录用后开源。