# SuperGS: Super-Resolution 3D Gaussian Splatting Enhanced by Variational Residual Features and Uncertainty-Augmented Learning

arXiv: https://arxiv.org/abs/2410.02571 · HTML: https://mlsanity.com/paper/2410.02571/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2024.

## Built on by (most cited first, 6 of 6)

- [Arbitrary-Scale 3D Gaussian Super-Resolution](https://mlsanity.com/paper/2508.16467/) (2025)
- [Sequence Matters: Harnessing Video Models in 3D Super-Resolution](https://mlsanity.com/paper/2412.11525/) (2024)
- [SPAGS: Sparse-View Articulated Object Reconstruction from Single State via Planar Gaussian Splatting](https://mlsanity.com/paper/2511.17092/) (2025)
- [MVGSR: Multi-View Consistent 3D Gaussian Super-Resolution via Epipolar Guidance](https://mlsanity.com/paper/2512.15048/) (2025)
- [MACRO: Training-free Multi-plane Attention for Closeup Render Optimization](https://mlsanity.com/paper/2607.03875/) (2026)
- [SubSplat: High-Resolution Pixel-aligned 3DGS via Sub-pixel Gaussian Reparameterization](https://mlsanity.com/paper/2607.20813/) (2026)

## Abstract (arXiv)

Recently, 3D Gaussian Splatting (3DGS) has exceled in novel view synthesis (NVS) with its real-time rendering capabilities and superior quality. However, it faces challenges for high-resolution novel view synthesis (HRNVS) due to the coarse nature of primitives derived from low-resolution input views. To address this issue, we propose Super-Resolution 3DGS (SuperGS), which is an expansion of 3DGS designed with a two-stage coarse-to-fine training framework. In this framework, we use a latent feature field to represent the low-resolution scene, serving as both the initialization and foundational information for super-resolution optimization. Additionally, we introduce variational residual features to enhance high-resolution details, using their variance as uncertainty estimates to guide the densification process and loss computation. Furthermore, the introduction of a multi-view joint learning approach helps mitigate ambiguities caused by multi-view inconsistencies in the pseudo labels. Extensive experiments demonstrate that SuperGS surpasses state-of-the-art HRNVS methods on both real-world and synthetic datasets using only low-resolution inputs. Code is available at https://github.com/SYXieee/SuperGS.