Calib3R: Hand-Eye Calibration and 3D Metric-Scaled Scene Reconstruction with 3D Foundation Models
arXiv:2509.08813v2 Announce Type: replace Abstract: Robots often rely on RGB images for tasks like manipulation. However, reliable interaction typically requires a 3D scene representation that is metric-scaled and aligned with the robot reference frame. This depends on accurate hand-eye calibration and dense 3D reconstruction, tasks usually treated separately, despite both relying on geometric correspondences from RGB data. Traditional calibration techniques needs patterns, while RGB-based reco
Overview
arXiv:2509.08813v2 Announce Type: replace Abstract: Robots often rely on RGB images for tasks like manipulation. However, reliable interaction typically requires a 3D scene representation that is metric-scaled and aligned with the robot reference frame. This depends on accurate hand-eye calibration and dense 3D reconstruction, tasks usually treated separately, despite both relying on geometric correspondences from RGB data. Traditional calibration techniques needs patterns, while RGB-based reconstruction yields 3D 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 hand-eye calibration and metric-scaled 3D reconstruction via unified optimization. Calib3R handles single- and multi-camera setups on robot arms. It builds on a 3D foundation model to extract pointmaps from RGB images, which are combined with robot poses to reconstruct a scaled 3D scene aligned with the robot base reference frame. Experiments on diverse datasets show that Calib3R achieves accurate calibration with less than 10 images, outperforming patternless and pattern-based methods.
Source
Originally published at arxiv.org.
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Source: https://arxiv.org/abs/2509.08813
