Generative Adversarial Networks for Solving Hand-Eye Calibration without Data Correspondence
I Hong, J Ha - arXiv preprint arXiv:2408.05613, 2024 - arxiv.org
I Hong, J Ha
arXiv preprint arXiv:2408.05613, 2024•arxiv.orgIn this study, we rediscovered the framework of generative adversarial networks (GANs) as a
solver for calibration problems without data correspondence. When data correspondence is
not present or loosely established, the calibration problem becomes a parameter estimation
problem that aligns the two data distributions. This procedure is conceptually identical to the
underlying principle of GAN training in which networks are trained to match the generative
distribution to the real data distribution. As a primary application, this idea is applied to the …
solver for calibration problems without data correspondence. When data correspondence is
not present or loosely established, the calibration problem becomes a parameter estimation
problem that aligns the two data distributions. This procedure is conceptually identical to the
underlying principle of GAN training in which networks are trained to match the generative
distribution to the real data distribution. As a primary application, this idea is applied to the …
In this study, we rediscovered the framework of generative adversarial networks (GANs) as a solver for calibration problems without data correspondence. When data correspondence is not present or loosely established, the calibration problem becomes a parameter estimation problem that aligns the two data distributions. This procedure is conceptually identical to the underlying principle of GAN training in which networks are trained to match the generative distribution to the real data distribution. As a primary application, this idea is applied to the hand-eye calibration problem, demonstrating the proposed method's applicability and benefits in complicated calibration problems.
arxiv.org