用3D高斯点云+语义信息直接估相机位姿，无需初始猜测。
SGLoc: Semantic Localization System for Camera Pose Estimation from 3D Gaussian Splatting Representation
- 基于语义匹配建立2D图像与3DGS地图对应关系
- 多层级渐进式回归，实现无先验全局定位
- 在12scenes和7scenes上超越基线，适合无初始位姿场景
我们提出SGLoc，一种新型定位系统，通过利用语义信息，直接从3D高斯点云（3DGS）表示中回归相机位姿。该方法借助2D图像与3D场景表示之间的语义关联，无需初始位姿先验即可估计6自由度（6DoF）位姿。系统采用多级位姿回归策略，从全局3DGS地图逐步估计并优化查询图像的位姿。同时引入基于语义的全局检索算法，通过匹配2D查询图像与3DGS语义表示提取的场景语义描述子，将图像对齐至3DGS地图的局部区域，获得粗略位姿估计。随后通过迭代优化查询图像与3DGS渲染图像间的差异来精炼位姿。SGLoc在12scenes和7scenes数据集上表现优于基线方法，展现出无需初始位姿先验的优秀全局定位能力。代码将公开于https://github.com/IRMVLab/SGLoc。
原文摘要 · Abstract (English)
We propose SGLoc, a novel localization system that directly regresses camera poses from 3D Gaussian Splatting (3DGS) representation by leveraging semantic information. Our method utilizes the semantic relationship between 2D image and 3D scene representation to estimate the 6DoF pose without prior pose information. In this system, we introduce a multi-level pose regression strategy that progressively estimates and refines the pose of query image from the global 3DGS map, without requiring initial pose priors. Moreover, we introduce a semantic-based global retrieval algorithm that establishes correspondences between 2D (image) and 3D (3DGS map). By matching the extracted scene semantic descriptors of 2D query image and 3DGS semantic representation, we align the image with the local region of the global 3DGS map, thereby obtaining a coarse pose estimation. Subsequently, we refine the coarse pose by iteratively optimizing the difference between the query image and the rendered image from 3DGS. Our SGLoc demonstrates superior performance over baselines on 12scenes and 7scenes datasets, showing excellent capabilities in global localization without initial pose prior. Code will be available at https://github.com/IRMVLab/SGLoc.
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