MipSLAM: Alias-Free Gaussian Splatting SLAM
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866916071496220672 |
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| author | Li, Yingzhao Li, Yan Tian, Shixiong Liu, Yanjie Zhao, Lijun Lee, Gim Hee |
| author_facet | Li, Yingzhao Li, Yan Tian, Shixiong Liu, Yanjie Zhao, Lijun Lee, Gim Hee |
| contents | This paper introduces MipSLAM, a frequency-aware 3D Gaussian Splatting (3DGS) SLAM framework capable of high-fidelity anti-aliased novel view synthesis and robust pose estimation under varying camera configurations. Existing 3DGS-based SLAM systems often suffer from aliasing artifacts and trajectory drift due to inadequate filtering and purely spatial optimization. To overcome these limitations, we propose an Elliptical Adaptive Anti-aliasing (EAA) algorithm that approximates Gaussian contributions via geometry-aware numerical integration, avoiding costly analytic computation. Furthermore, we present a Spectral-Aware Pose Graph Optimization (SA-PGO) module that reformulates trajectory estimation in the frequency domain, effectively suppressing high-frequency noise and drift through graph Laplacian analysis. Extensive evaluations on Replica and TUM datasets demonstrate that MipSLAM achieves state-of-the-art rendering quality and localization accuracy across multiple resolutions. Code is available at https://github.com/yzli1998/MipSLAM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06989 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MipSLAM: Alias-Free Gaussian Splatting SLAM Li, Yingzhao Li, Yan Tian, Shixiong Liu, Yanjie Zhao, Lijun Lee, Gim Hee Computer Vision and Pattern Recognition This paper introduces MipSLAM, a frequency-aware 3D Gaussian Splatting (3DGS) SLAM framework capable of high-fidelity anti-aliased novel view synthesis and robust pose estimation under varying camera configurations. Existing 3DGS-based SLAM systems often suffer from aliasing artifacts and trajectory drift due to inadequate filtering and purely spatial optimization. To overcome these limitations, we propose an Elliptical Adaptive Anti-aliasing (EAA) algorithm that approximates Gaussian contributions via geometry-aware numerical integration, avoiding costly analytic computation. Furthermore, we present a Spectral-Aware Pose Graph Optimization (SA-PGO) module that reformulates trajectory estimation in the frequency domain, effectively suppressing high-frequency noise and drift through graph Laplacian analysis. Extensive evaluations on Replica and TUM datasets demonstrate that MipSLAM achieves state-of-the-art rendering quality and localization accuracy across multiple resolutions. Code is available at https://github.com/yzli1998/MipSLAM. |
| title | MipSLAM: Alias-Free Gaussian Splatting SLAM |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.06989 |