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Cet article décrit une approche pour la modélisation géométrique et l’auto‐calibration de ce système. Il est couramment déployé en cartographie mobile en vue de l’acquisition d’images pour la reconstruction 3D. Sub‐pixel interior orientation stability and millimetre‐level relative orientation stability were also demonstrated over a 10‐month period.įr Le Ladybug5 est un système intégrant plusieurs caméras et caractérisé par un champ de vision quasi‐sphérique. Centimetre‐level 3D reconstruction accuracy can be achieved, with image‐space precision and object‐space accuracy improved by 92% and 93%, respectively, relative to a two‐term lens distortion model. Weighted relative orientation stability constraints are added to the self‐calibrating bundle adjustment solution to enforce the angular and positional stability between the Ladybug5’s six cameras. The collinearity equations of the pinhole camera model are augmented with five radial lens distortion terms to correct the severe barrel distortion. This paper describes an approach for the geometric modelling and self‐calibration of this system. It is commonly deployed on mobile mapping systems to collect imagery for 3D reality capture. Experiments with this data against a state-of-the-art in-air network as well as different artificial inputs show that the style transfer as well as the depth estimation exhibit promising performance.Įn The Ladybug5 is an integrated, multi‐camera system that features a near‐spherical field of view. We test our approach on style-transferred in-air images as well as on our own real underwater dataset, for which we computed sparse ground truth depths data via stereopsis. This way, our learning model is designed to obtain the depth up to scale, without the need of corresponding ground truth underwater depth data, which is typically not available. Given those synthetic underwater images and their ground truth depth, we then train a network to estimate the depth. For this, the in-air images are style-transferred to the underwater style as the first step. To this end, we leverage publicly available in-air RGB-D image pairs for underwater depth estimation in the spherical domain with an unsupervised approach. This paper proposes a method for monocular underwater depth estimation, which is an open problem in robotics and computer vision. An explicit calibration model is hence to be chosen carefully and most likely relies on the offset’s margins and each individual application. Our method is capable of improving accuracy in one data set at a maximum of 30% but even so cannot provide improvements for the second data sets.
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The algorithm is employed for analysis on a simulation and two real data sets and performance of additionally estimating the dome center is investigated. It is about two orders of magnitude faster than standard ray tracing implementations that account for refraction while providing similar or equal results. Hence, we propose a novel efficient, yet strict optimization algorithm to account for offsets between dome port centers and entrance pupil. However, simulations and other authors show that systematic residual errors occur with these approaches up to considerable margins if offsets of some millimeters are present. This is strictly only possible if entrance pupil of the lens and dome center coincide which is not trivial to achieve.
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For hemispherical interfaces, the usual approach to refraction is to rely on standard pinhole representations, e.g. Refraction effects, their description and modeling are important aspects of underwater and multimedia photogrammetry.