PlanaReLoc: Camera Relocalization in 3D Planar Primitives via Region-Based Structure Matching

TL;DR

PlanaReLoc introduces a novel camera relocalization method using 3D planar primitives and region-based structure matching, enabling reliable relocalization in structured environments without textured maps or pose priors.

Contribution

The paper pioneers the use of planar primitives and a deep matching framework for lightweight 6-DoF camera relocalization in structured environments.

Findings

Outperforms point-based methods in relocalization accuracy

Effective without textured or colored maps

No need for pose priors or per-scene training

Abstract

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 .