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 this https URL

在本文中，我们提出了一种适用于自适应尺度的抗锯齿高斯点光源方法（SA-GS）。SA-GS 方法在测试时无需进行训练，可以作为插件应用于任何预训练的高斯点光源场，显著提高场的抗锯齿性能。核心技术是在测试时对每个高斯应用二维自适应尺度滤波器，有效匹配高斯尺度，从而使高斯原始分布在不同测试频率下保持一致。与 Mip-Splatting 相比，SA-GS 在各种设置和有界 / 无界场景的广泛实验中表现相当或更好。