  The 3D localisation of an object and the estimation of its properties, such
as shape and dimensions, are challenging under varying degrees of transparency
and lighting conditions. In this paper, we propose a method for jointly
localising container-like objects and estimating their dimensions using two
wide-baseline, calibrated RGB cameras. Under the assumption of circular
symmetry along the vertical axis, we estimate the dimensions of an object with
a generative 3D sampling model of sparse circumferences, iterative shape
fitting and image re-projection to verify the sampling hypotheses in each
camera using semantic segmentation masks. We evaluate the proposed method on a
novel dataset of objects with different degrees of transparency and captured
under different backgrounds and illumination conditions. Our method, which is
based on RGB images only, outperforms in terms of localisation success and
dimension estimation accuracy a deep-learning based approach that uses depth
maps.
