CVAug 22, 2025

Arbitrary-Scale 3D Gaussian Super-Resolution

arXiv:2508.16467v22 citationsh-index: 13
Originality Incremental advance
AI Analysis

This work addresses the need for flexible and efficient high-resolution rendering in 3D Gaussian Splatting, offering a solution for applications requiring arbitrary-scale super-resolution, though it is incremental as it builds on existing 3DGS methods.

The paper tackles the problem of 3D Gaussian Splatting super-resolution being limited to fixed scales, which is impractical for resource-limited scenarios, by proposing an integrated framework that enables arbitrary-scale super-resolution with a single model, achieving a 6.59 dB PSNR gain over baseline 3DGS and real-time rendering at 85 FPS.

Existing 3D Gaussian Splatting (3DGS) super-resolution methods typically perform high-resolution (HR) rendering of fixed scale factors, making them impractical for resource-limited scenarios. Directly rendering arbitrary-scale HR views with vanilla 3DGS introduces aliasing artifacts due to the lack of scale-aware rendering ability, while adding a post-processing upsampler for 3DGS complicates the framework and reduces rendering efficiency. To tackle these issues, we build an integrated framework that incorporates scale-aware rendering, generative prior-guided optimization, and progressive super-resolving to enable 3D Gaussian super-resolution of arbitrary scale factors with a single 3D model. Notably, our approach supports both integer and non-integer scale rendering to provide more flexibility. Extensive experiments demonstrate the effectiveness of our model in rendering high-quality arbitrary-scale HR views (6.59 dB PSNR gain over 3DGS) with a single model. It preserves structural consistency with LR views and across different scales, while maintaining real-time rendering speed (85 FPS at 1080p).

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