SuperGS: Super-Resolution 3D Gaussian Splatting via Latent Feature Field and Gradient-guided Splitting
Recently, 3D Gaussian Splatting (3DGS) has exceled in novel view synthesis 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, utilizing pretrained low-resolution scene representation as an initialization for super-resolution optimization. Moreover, we introduce Multi-resolution Feature Gaussian Splatting (MFGS) to incorporates a latent feature field for flexible feature sampling and Gradient-guided Selective Splitting (GSS) for effective Gaussian upsampling. By integrating these strategies within the coarse-to-fine framework ensure both high fidelity and memory efficiency. Extensive experiments demonstrate that SuperGS surpasses state-of-the-art HRNVS methods on challenging real-world datasets using only low-resolution inputs.
近期，3D高斯散射（3DGS）凭借其实时渲染能力和卓越的质量在新视图合成领域表现出色。然而，由于从低分辨率输入视图推导出的基元较为粗糙，3DGS在高分辨率新视图合成（HRNVS）中面临挑战。为解决这一问题，我们提出了超分辨率3DGS（SuperGS），这是3DGS的扩展，采用了一个两阶段的粗到细训练框架，利用预训练的低分辨率场景表示作为超分辨率优化的初始化。此外，我们引入了多分辨率特征高斯散射（MFGS），该方法结合了一个潜在特征场，实现了灵活的特征采样，并通过梯度引导的选择性分裂（GSS）实现高效的高斯上采样。通过将这些策略整合到粗到细的框架中，确保了高保真度和内存效率。大量实验表明，SuperGS在仅使用低分辨率输入的情况下，超越了在复杂现实世界数据集上的最先进HRNVS方法。