Not yet reviewed by Pith; the record is open.

Robots often rely on RGB images for tasks like manipulation and navigation. However, reliable interaction typically requires a 3D scene representation that is metric-scaled and aligned with the robot reference frame. This depends on accurate camera-to-robot calibration and dense 3D reconstruction, tasks usually treated separately, despite both relying on geometric correspondences from RGB data. Traditional calibration needs patterns, while RGB-based reconstruction yields geometry with an unknown scale in an arbitrary frame. Multi-camera setups add further complexity, as data must be expressed in a shared reference frame. We present Calib3R, a patternless method that jointly performs camera-to-robot calibration and metric-scaled 3D reconstruction via unified optimization. Calib3R handles single- and multi-camera setups on robot arms or mobile robots. It builds on the 3D foundation model MASt3R to extract pointmaps from RGB images, which are combined with robot poses to reconstruct a scaled 3D scene aligned with the robot. Experiments on diverse datasets show that Calib3R achieves accurate calibration with less than 10 images, outperforming target-less and marker-based methods.