SA-GS: Scale-Adaptive Gaussian Splatting for Training-Free Anti-Aliasing
Xiaowei Song Jv Zheng Shiran Yuan Huan-ang Gao Jingwei Zhao Xiang He Weihao Gu Hao Zhao
In this paper, we present a Scale-adaptive method for Anti-aliasing Gaussian
Splatting (SA-GS). While the state-of-the-art method Mip-Splatting needs
modifying the training procedure of Gaussian splatting, our method functions at
test-time and is training-free. Specifically, SA-GS can be applied to any
pretrained Gaussian splatting field as a plugin to significantly improve the
field's anti-alising performance. The core technique is to apply 2D
scale-adaptive filters to each Gaussian during test time. As pointed out by
Mip-Splatting, observing Gaussians at different frequencies leads to mismatches
between the Gaussian scales during training and testing. Mip-Splatting resolves
this issue using 3D smoothing and 2D Mip filters, which are unfortunately not
aware of testing frequency. In this work, we show that a 2D scale-adaptive
filter that is informed of testing frequency can effectively match the Gaussian
scale, thus making the Gaussian primitive distribution remain consistent across
different testing frequencies. When scale inconsistency is eliminated, sampling
rates smaller than the scene frequency result in conventional jaggedness, and
we propose to integrate the projected 2D Gaussian within each pixel during
testing. This integration is actually a limiting case of super-sampling, which
significantly improves anti-aliasing performance over vanilla Gaussian
Splatting. Through extensive experiments using various settings and both
bounded and unbounded scenes, we show SA-GS performs comparably with or better
than Mip-Splatting. Note that super-sampling and integration are only effective
when our scale-adaptive filtering is activated. Our codes, data and models are
available at https://github.com/zsy1987/SA-GS.