Arbitrary-Scale 3D Gaussian Super-Resolution

Authors

  • Huimin Zeng Northeastern University
  • Yue Bai Northeastern University
  • Yun Fu Northeastern University

DOI:

https://doi.org/10.1609/aaai.v40i15.38222

Abstract

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 producing 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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Published

2026-03-14

How to Cite

Zeng, H., Bai, Y., & Fu, Y. (2026). Arbitrary-Scale 3D Gaussian Super-Resolution. Proceedings of the AAAI Conference on Artificial Intelligence, 40(15), 12304–12312. https://doi.org/10.1609/aaai.v40i15.38222

Issue

Section

AAAI Technical Track on Computer Vision XII