用梯度信息提升3D高斯点云图像分辨率，速度更快且更清晰。
Lightweight Gradient-Aware Upscaling of 3D Gaussian Splatting Images
- 利用高斯分布的解析梯度做梯度感知插值，实现轻量级超分
- 在轻量GPU上实现3-4倍于基线的视图合成速度提升
- 适用于各类3DGS模型，适合实时渲染与资源受限场景
本文提出一种专为轻量级GPU设计的3D高斯点云图像超分技术。相比原始3DGS，该方法显著提升渲染速度并减少重建伪影。通过直接利用高斯的解析图像梯度，实现基于梯度的双三次样条插值，在计算成本几乎不变的前提下，对低分辨率3DGS渲染结果进行上采样。该方法与具体3DGS实现无关，可使新视角合成速度达到基线的3至4倍。在多个数据集上的大量实验验证了其性能提升和高重建保真度。进一步将该超分方法集成至3DGS模型的梯度优化流程中，并分析其对重建质量与效率的影响。
原文摘要 · Abstract (English)
We introduce an image upscaling technique tailored for 3D Gaussian Splatting (3DGS) on lightweight GPUs. Compared to 3DGS, it achieves significantly higher rendering speeds and reduces artifacts commonly observed in 3DGS reconstructions. Our technique upscales low-resolution 3DGS renderings with a marginal increase in cost by directly leveraging the analytical image gradients of Gaussians for gradient-based bicubic spline interpolation. The technique is agnostic to the specific 3DGS implementation, achieving novel view synthesis at rates 3x-4x higher than the baseline implementation. Through extensive experiments on multiple datasets, we showcase the performance improvements and high reconstruction fidelity attainable with gradient-aware upscaling of 3DGS images. We further demonstrate the integration of gradient-aware upscaling into the gradient-based optimization of a 3DGS model and analyze its effects on reconstruction quality and performance.
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