Calib-X: Training-Free Camera-to-Robot Pose Estimation via Dense Feature Matching

Camera-to-robot pose estimation is a fundamental yet challenging problem for vision-based robotic manipulation, requiring accurate recovery of the rigid transformation between a camera and a robot. Conventional calibration approaches often rely on fiducial markers or manual procedures, limiting scalability in real-world deployments. Recent learning-based methods alleviate this burden by directly predicting poses from images, but they typically require large-scale pose annotations and costly retraining when transferring to new robots or environments. In this paper, we introduce $\text{Calib-X}$, a $\text{training-free}$ framework that reformulates camera-to-robot calibration as an $\text{end-to-end dense feature matching}$ problem. Leveraging robot kinematics, Calib-X first generates multi-view robot renderings from predefined viewpoints and establishes dense correspondences between rendered templates and an uncalibrated camera image using semantic features from the foundation model DINOv3. These correspondences enable direct estimation of the initial camera pose without any task-specific optimization or data collection. Furthermore, we propose an iterative pose refinement strategy that continuously re-renders the robot under the updated pose and performs dense feature alignment with the query image, progressively improving calibration accuracy. Extensive experiments on four public benchmarks demonstrate that Calib-X achieves accurate and robust camera-to-robot pose estimation across diverse robotic platforms and environments. Without requiring markers, annotations, or retraining, Calib-X provides a scalable and generalizable solution for real-world robot calibration. The code will be released publicly upon publication.