PlanaReLoc：基于区域结构匹配的三维平面基元相机重定位
PlanaReLoc: Camera Relocalization in 3D Planar Primitives via Region-Based Structure Matching
把平面基元当作区域级结构表示来做跨模态匹配，绕开了对纹理/颜色地图、位姿先验和逐场景训练的依赖；做室内重定位或地图表示的人可以直接借鉴这种「平面图 + 深度匹配器 + 鲁棒位姿求解」的轻量范式。
原文依据 a deep matcher associates planar primitives across the query image and the map within a learned unified embedding space
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论尽管基于结构的重定位器长期以来在建立或回归查询图像与地图关联时一直追求点对应，本文则率先使用平面基元和三维平面地图，在结构化环境中实现轻量化的 6-DoF 相机重定位。平面基元不仅是投影几何中的基本实体，还可作为区域级表示，同时蕴含结构信息与语义信息。这促使我们提出 PlanaReLoc，一种简洁的以平面为中心的范式：深度匹配器在学得的统一嵌入空间中关联查询图像与地图中的平面基元，随后在鲁棒框架下求解并优化 6-DoF 位姿。在 ScanNet 和 12Scenes 数据集上跨数百个场景的全面实验表明，平面基元在促成可靠的跨模态结构对应方面具有优越性，并能在无需真实纹理/彩色地图、位姿先验或逐场景训练的情况下实现有效的相机重定位。代码与数据见 https://github.com/3dv-casia/PlanaReLoc。
英文原文摘要 1174 字符
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.