用平面几何体实现轻量级3D相机重定位，无需纹理图或场景训练。
PlanaReLoc: Camera Relocalization in 3D Planar Primitives via Region-Based Structure Matching
- 基于平面几何体匹配，构建统一嵌入空间进行跨模态对应
- 在ScanNet和12Scenes上实现高精度6-DoF定位，无需纹理图或姿态先验
- 适合结构化环境下的实时重定位，代码开源可复现
尽管基于结构的重定位方法长期依赖点对应关系建立查询-地图关联，本文首次提出利用平面几何体与3D平面地图实现轻量级6自由度相机重定位。平面几何体不仅是射影几何的基本单元，更作为区域级表征，融合结构与语义信息。我们提出PlanaReLoc，一种以平面为中心的简化范式：通过深度匹配器在学习的统一嵌入空间中关联查询图像与地图中的平面几何体，随后在鲁棒框架下求解并优化6-DoF位姿。在包含数百个场景的ScanNet和12Scenes数据集上的全面实验表明，平面几何体能有效促进跨模态结构对应，实现可靠相机重定位，且无需真实纹理/颜色地图、姿态先验或每场景训练。代码与数据已公开于https://github.com/3dv-casia/PlanaReLoc。
原文摘要 · Abstract (English)
While structure-based relocalizers have long strived for point correspondences when establishing or regressing query-map associations, in this paper, we pioneer the use of planar primitives and 3D planar maps for lightweight 6-DoF camera relocalization in structured environments. Planar primitives, beyond being fundamental entities in projective geometry, also serve as region-based representations that encapsulate both structural and semantic richness. This motivates us to introduce PlanaReLoc, a streamlined plane-centric paradigm where a deep matcher associates planar primitives across the query image and the map within a learned unified embedding space, after which the 6-DoF pose is solved and refined under a robust framework. Through comprehensive experiments on the ScanNet and 12Scenes datasets across hundreds of scenes, our method demonstrates the superiority of planar primitives in facilitating reliable cross-modal structural correspondences and achieving effective camera relocalization without requiring realistically textured/colored maps, pose priors, or per-scene training. The code and data are available at https://github.com/3dv-casia/PlanaReLoc .
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