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

• URL: http://arxiv.org/abs/2410.02571v3
• Date: Wed, 11 Dec 2024 14:18:12 GMT
• Title: SuperGS: Super-Resolution 3D Gaussian Splatting Enhanced by Variational Residual Features and Uncertainty-Augmented Learning
• Authors: Shiyun Xie, Zhiru Wang, Xu Wang, Yinghao Zhu, Chengwei Pan, Xiwang Dong,
• Abstract summary: Super-Resolution 3DGS (SuperGS) is an expansion of 3DGS designed with a two-stage coarse-to-fine training framework.<n>SuperGS surpasses state-of-the-art HRNVS methods on both real-world and synthetic datasets using only low-resolution inputs.
• Score: 6.309174895120047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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