解决3D高斯点云的伪影与定位漂移问题，实现高质量新视角合成。
MipSLAM: Alias-Free Gaussian Splatting SLAM
- 用几何感知积分法避免复杂计算，有效抑制图像伪影。
- 在Replica和TUM数据集上达到最佳渲染质量和定位精度。
- 适合需要高保真重建与稳定轨迹的应用场景。
本文提出MipSLAM，一种频率感知的3D高斯点云同步定位与地图构建框架，可在不同相机配置下实现高保真无伪影的新视角合成与鲁棒位姿估计。现有基于3DGS的SLAM系统常因过滤不足和纯空间优化导致伪影与轨迹漂移。为此，我们设计了椭圆自适应抗混叠（EAA）算法，通过几何感知数值积分近似高斯贡献，避免昂贵的解析计算。此外，提出频谱感知位姿图优化（SA-PGO）模块，在频域重构位姿估计，利用图拉普拉斯分析有效抑制高频噪声与漂移。在Replica和TUM数据集上的大量实验表明，MipSLAM在多种分辨率下均达到领先渲染质量与定位精度。代码已公开于https://github.com/yzli1998/MipSLAM。
原文摘要 · Abstract (English)
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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