GaussianSR: 3D Gaussian Super-Resolution with 2D Diffusion Priors
Authors: name: Xiqian Yu, name: Hanxin Zhu, name: Tianyu He, name: Zhibo Chen

Achieving high-resolution novel view synthesis (HRNVS) from low-resolution
input views is a challenging task due to the lack of high-resolution data.
Previous methods optimize high-resolution Neural Radiance Field (NeRF) from
low-resolution input views but suffer from slow rendering speed. In this work,
we base our method on 3D Gaussian Splatting (3DGS) due to its capability of
producing high-quality images at a faster rendering speed. To alleviate the
shortage of data for higher-resolution synthesis, we propose to leverage
off-the-shelf 2D diffusion priors by distilling the 2D knowledge into 3D with
Score Distillation Sampling (SDS). Nevertheless, applying SDS directly to
Gaussian-based 3D super-resolution leads to undesirable and redundant 3D
Gaussian primitives, due to the randomness brought by generative priors. To
mitigate this issue, we introduce two simple yet effective techniques to reduce
stochastic disturbances introduced by SDS. Specifically, we 1) shrink the range
of diffusion timestep in SDS with an annealing strategy; 2) randomly discard
redundant Gaussian primitives during densification. Extensive experiments have
demonstrated that our proposed GaussainSR can attain high-quality results for
HRNVS with only low-resolution inputs on both synthetic and real-world
datasets. Project page: https://chchnii.github.io/GaussianSR/