  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.
