Recently, 3D Gaussian Splatting has demonstrated impressive novel view synthesis results, reaching high fidelity and efficiency. However, strong artifacts can be observed when changing the sampling rate, e.g., by changing focal length or camera distance. We find that the source for this phenomenon can be attributed to the lack of 3D frequency constraints and the usage of a 2D dilation filter. To address this problem, we introduce a 3D smoothing filter which constrains the size of the 3D Gaussian primitives based on the maximal sampling frequency induced by the input views, eliminating high-frequency artifacts when zooming in. Moreover, replacing 2D dilation with a 2D Mip filter, which simulates a 2D box filter, effectively mitigates aliasing and dilation issues. Our evaluation, including scenarios such a training on single-scale images and testing on multiple scales, validates the effectiveness of our approach.
最近，三维高斯溅点在新视角合成方面展现了令人印象深刻的成果，达到了高保真度和高效率。然而，当改变采样率时，例如通过改变焦距或相机距离，可以观察到强烈的失真现象。我们发现，这一现象的源头可以归因于缺乏三维频率约束和使用二维扩张滤波器。为了解决这个问题，我们引入了一个三维平滑滤波器，该滤波器基于输入视图引起的最大采样频率来约束三维高斯原语的大小，消除了放大时的高频失真。此外，用二维 Mip 滤波器替换二维扩张滤波器，该滤波器模拟二维盒式滤波器，有效缓解了混叠和扩张问题。我们的评估，包括在单尺度图像上训练和在多尺度上测试的场景，验证了我们方法的有效性。