PGCR: Pose–Geometry Coupled Reasoning for Image-to-Point Cloud Registration

Image-to-point cloud registration aims to estimate the 6-DoFcamera pose of a query image with respect to a 3D point cloud, whichremains challenging due to the heterogeneous nature of dense visual ap-pearance and sparse geometric structure. Most existing learning-basedmethods follow an overlap–then–registration paradigm, where pose esti-mation is restricted to a predicted overlapping region. Such a sequentialdecomposition is inherently fragile, as inaccurate overlap prediction maydiscard geometrically consistent regions and irreversibly constrain poseoptimization. In this work, we reformulate image-to-point cloud registra-tion as a pose–geometry coupled reasoning problem, where camera poseand scene geometry are treated as interdependent variables. Based onthis formulation, we propose PGCR, a unified framework that estimatescamera pose by explicitly reasoning over dense cross-modal geometry.PGCR jointly predicts camera pose together with dense geometric rep-resentations and grounds pose inference in global geometric consistency.To enable effective pose–geometry coupling, we introduce a Geometry-aware Pose Refinement mechanism to enforce prediction-level consistencybetween pose and geometry, and a Pose-guided Progressive Refinementstrategy to adapt cross-modal interaction according to intermediate poseestimates. Extensive experiments on two widely used outdoor and twoindoor benchmarks demonstrate that PGCR consistently outperformsprior state-of-the-art methods across all evaluation metrics while remain-ing computationally efficient.