MipSLAM：无混叠高斯泼溅SLAM
MipSLAM: Alias-Free Gaussian Splatting SLAM
AI总结：
提出MipSLAM框架，通过椭圆自适应抗混叠算法和频谱感知位姿图优化，实现高保真抗混叠新视角合成与鲁棒位姿估计。
摘要：
本文介绍了MipSLAM，一种频率感知的3D高斯泼溅（3DGS）SLAM框架，能够在不同相机配置下实现高保真抗混叠新视角合成和鲁棒位姿估计。现有的基于3DGS的SLAM系统常因滤波不足和纯空间优化而遭受混叠伪影和轨迹漂移。为克服这些限制，我们提出椭圆自适应抗混叠（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，我们提出频谱感知位姿图优化（SA-PGO）模块，在频域中重新表述轨迹估计，通过图拉普拉斯分析有效抑制高频噪声和漂移。在Replica和TUM数据集上的广泛评估表明，MipSLAM在多种分辨率下实现了最先进的渲染质量和定位精度。代码可在https://github.com/yzli1998/MipSLAM获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
