SeeRayLoc: Generalizable Floorplan Localization with What You See

Ray-based floorplan localization estimates camera pose by matching image-derived depth rays against floorplan geometry. Existing methods directly predict floorplan-aligned wall distances, including geometry that is often not visible in the image. Such limitation on implicit scene completion can overfit to dataset-specific domain priors and harm cross-domain generalization. We introduce SeeRayLoc, a framework that decouples visible geometry estimation from localization-oriented ray construction. SeeRayLoc estimates visible scene depth and adaptively selects localization-relevant rays for floorplan matching. Floorplan supervision guides ray selection instead of hidden-geometry prediction, yielding generalizable floorplan localization. We evaluate SeeRayLoc through source-to-target transfer across six indoor localization datasets, including FurnFloc, our newly curated dataset for furnished scenes. The evaluation spans diverse scene distributions, camera configurations, fields of view, and both perspective and panoramic imagery. Extensive experiments show that SeeRayLoc has stronger transfer robustness than direct floorplan-depth prediction, achieving the highest R@1m30° recall in all seven Structured3D source transfer settings across perspective and panoramic imagery. It proves the effectiveness of visible geometry providing a more generalizable basis for ray-based floorplan localization. Project page and code at: https://anonymous.4open.science/r/SeeRayLoc-9573.