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The paper presents an algorithm for reconstruction of 3D circle from its apparition in <i>n</i> images. It supposes that camera poses areknown up to an uncertainty. They will be considered as observations and will be refined during the reconstruction process. First,circle apparitions will be estimated in every individual image from a set of 2D points using a constrained optimization. Uncertainty of2D points are propagated in 2D ellipse estimation and leads to covariance matrix of ellipse parameters. In 3D reconstruction processellipse and camera pose parameters are considered as observations with known covariances. A minimal parametrization of 3D circleenables to model the projection of circle in image without any constraint. The reconstruction is performed by minimizing the length ofobservation residuals vector in a non linear Gauss-Helmert model. The output consists in parameters of the corresponding circle in 3Dand their covariances. The results are presented on simulated data

Multi-view 3D circular target reconstruction with uncertainty analysis