This research paper introduces SuperGS, a novel method for high-resolution novel view synthesis (HRNVS) that leverages the efficiency of 3D Gaussian Splatting (3DGS) while overcoming its limitations in handling high-resolution details.
The study aims to address the challenge of synthesizing high-resolution novel views from low-resolution input images, a task where traditional 3DGS methods struggle due to the coarse nature of their primitives.
SuperGS employs a two-stage coarse-to-fine framework. In the first stage, a low-resolution scene representation is optimized using 3DGS. This representation serves as initialization for the second stage, where super-resolution is achieved through two key innovations:
Multi-resolution Feature Gaussian Splatting (MFGS): This approach replaces the traditional 3DGS pipeline by constructing a latent feature field using hash-based grids. This allows for flexible feature sampling at arbitrary positions and view directions, enabling the derivation of new Gaussian features from the low-resolution scene representation. An image decoder then synthesizes high-resolution novel views from the rendered feature map.
Gradient-guided Selective Splitting (GSS): This strategy selectively subdivides coarse Gaussian primitives into finer ones, guided by a 2D pretrained super-resolution model. This ensures detailed representation in complex regions while preserving larger primitives in smoother areas, optimizing memory efficiency.
SuperGS presents a novel and effective solution for HRNVS, leveraging the strengths of 3DGS while mitigating its limitations. The proposed MFGS and GSS strategies significantly improve detail rendering and memory efficiency, making SuperGS a promising approach for various applications requiring high-quality novel view synthesis.
This research significantly advances the field of novel view synthesis by enabling high-resolution rendering from low-resolution inputs using 3DGS. The proposed method's efficiency and effectiveness open up new possibilities for applications in virtual reality, augmented reality, and 3D content creation.
While SuperGS demonstrates impressive results, future research could explore incorporating arbitrary-scale 2D super-resolution models to achieve arbitrary-scale super-resolution within the framework. Additionally, investigating the generalization capabilities of SuperGS across diverse and complex scenes could further enhance its applicability.
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