用几何先验提升单目3D高斯点云重建，实时且精度更高
GeoGS-SLAM: Online Monocular Reconstruction Using Gaussian Splatting with Geometric Priors
- 结合3D高斯点云与预训练几何模型，直接从图像和先验采样点
- 在多个场景中实现比现有方法更优的渲染质量与跟踪精度
- 适合需要实时高保真重建的机器人与AR应用
基于3D高斯点云的SLAM方法虽表现优异，但通常依赖外部深度传感器。而利用预训练前馈模型引入几何先验的系统虽实现实时稠密重建，却常在优化中丢弃原始RGB信息，降低重建质量。本文提出GeoGS-SLAM，一种在线单目稠密重建系统，融合3DGS地图表示与学习到的几何先验。给定未标定的RGB输入，首先通过前馈视觉几何模型预测相机与场景先验；随后直接从RGB输入和几何先验中采样高斯基元扩展场景地图。采用粗到细策略联合优化相机位姿与场景地图，最小化光度与几何损失。为保证全局一致性，进一步引入在线回环检测与位姿图优化。在室内外多个基准测试中，GeoGS-SLAM在保持实时性能的同时，实现了优于当前最先进方法的渲染质量与跟踪精度。
原文摘要 · Abstract (English)
SLAM methods based on 3D Gaussian Splatting (3DGS) have demonstrated impressive tracking and mapping performance, but typically require additional geometric information from external depth sensors. Meanwhile, recent SLAM systems that leverage geometric priors from pre-trained feed-forward models enable real-time dense reconstruction, yet often discard original RGB information during optimization, thus degrading overall reconstruction quality. We present GeoGS-SLAM, an online monocular dense reconstruction system that combines the 3DGS-based map representation with learned geometric priors. Given uncalibrated RGB input, we first employ a feed-forward visual geometry model to predict camera and scene priors. The Gaussian scene map is then expanded by directly sampling Gaussian primitives from both RGB input and geometric priors. Camera poses and the scene map are jointly optimized through a coarse-to-fine strategy that minimizes both photometric and geometric losses. To ensure global consistency, we further incorporate online loop closure detection and pose graph optimization. Extensive experiments across indoor and outdoor benchmarks demonstrate that GeoGS-SLAM achieves superior rendering quality and tracking accuracy compared to state-of-the-art methods while maintaining online real-time performance. Project page: https://rlgao.github.io/geogs_slam.
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