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arXiv · 2603.06989

MipSLAM: Alias-Free Gaussian Splatting SLAM

Yingzhao Li·Yan Li·Shixiong Tian·Yanjie Liu·Lijun Zhao·Gim Hee Lee
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Abstract

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.

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Yingzhao Li, Yan Li, Shixiong Tian, Yanjie Liu, Lijun Zhao, Gim Hee Lee. 2026-03-07. MipSLAM: Alias-Free Gaussian Splatting SLAM. https://arxiv.org/abs/2603.06989

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