Mip-Splatting: Alias-free 3D Gaussian Splatting

November 27, 2023 ยท Declared Dead ยท ๐Ÿ› Computer Vision and Pattern Recognition

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Authors Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, Andreas Geiger arXiv ID 2311.16493 Category cs.CV: Computer Vision Citations 633 Venue Computer Vision and Pattern Recognition Last Checked 1 month ago
Abstract
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, \eg, 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.
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