SuperGS: Consistent and Detailed 3D Super-Resolution Scene Reconstruction via Gaussian Splatting
This work addresses the problem of detailed 3D scene reconstruction for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the challenge of high-resolution novel view synthesis in 3D Gaussian Splatting by proposing SuperGS, a two-stage coarse-to-fine framework that uses a latent feature field and multi-view consistent densification, achieving state-of-the-art performance on forward-facing and 360-degree datasets.
Recently, 3D Gaussian Splatting (3DGS) has excelled in novel view synthesis (NVS) with its real-time rendering capabilities and superior quality. However, it encounters 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 SuperGS, an expansion of Scaffold-GS designed with a two-stage coarse-to-fine training framework. In the low-resolution stage, we introduce a latent feature field to represent the low-resolution scene, which serves as both the initialization and foundational information for super-resolution optimization. In the high-resolution stage, we propose a multi-view consistent densification strategy that backprojects high-resolution depth maps based on error maps and employs a multi-view voting mechanism, mitigating ambiguities caused by multi-view inconsistencies in the pseudo labels provided by 2D prior models while avoiding Gaussian redundancy. Furthermore, we model uncertainty through variational feature learning and use it to guide further scene representation refinement and adjust the supervisory effect of pseudo-labels, ensuring consistent and detailed scene reconstruction. Extensive experiments demonstrate that SuperGS outperforms state-of-the-art HRNVS methods on both forward-facing and 360-degree datasets.