# EasyHeC++: Fully Automatic Hand-Eye Calibration with Pretrained Image Models

arXiv: https://arxiv.org/abs/2410.09293 · HTML: https://mlsanity.com/paper/2410.09293/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2024.

## Built on by (most cited first, 6 of 6)

- [AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons](https://mlsanity.com/paper/2503.03081/) (2025)
- [Learning Adaptive Dexterous Grasping from Single Demonstrations](https://mlsanity.com/paper/2503.20208/) (2025)
- [Hydra: Marker-Free RGB-D Hand-Eye Calibration](https://mlsanity.com/paper/2504.20584/) (2025)
- [R3D: Revisiting 3D Policy Learning](https://mlsanity.com/paper/2604.15281/) (2026)
- [Continual Hand-Eye Calibration for Open-world Robotic Manipulation](https://mlsanity.com/paper/2604.15814/) (2026)
- [DRHeC: Differentiable Rendering for Hand-Eye Calibration with RGB-Based Gradients](https://mlsanity.com/paper/2609.36779/) (2026)

## Abstract (arXiv)

Hand-eye calibration plays a fundamental role in robotics by directly influencing the efficiency of critical operations such as manipulation and grasping. In this work, we present a novel framework, EasyHeC++, designed for fully automatic hand-eye calibration. In contrast to previous methods that necessitate manual calibration, specialized markers, or the training of arm-specific neural networks, our approach is the first system that enables accurate calibration of any robot arm in a marker-free, training-free, and fully automatic manner. Our approach employs a two-step process. First, we initialize the camera pose using a sampling or feature-matching-based method with the aid of pretrained image models. Subsequently, we perform pose optimization through differentiable rendering. Extensive experiments demonstrate the system's superior accuracy in both synthetic and real-world datasets across various robot arms and camera settings. Project page: https://ootts.github.io/easyhec_plus.