Whole Shape Estimation of Transparent Object from Its Contour using Statistical Shape Model

This study presents a method for estimating the three-dimensional (3D) shapes of transparent objects from an RGB-D image using a statistical shape model. Statistical shape models compress the dimensions of multiple shapes to represent shape variations using fewer parameters. It is difficult to measure the depth of a transparent object using sensors. Therefore, the statistical shape model is deformed to fit the contour extracted from an RGB image and estimate the shape of the object. The depth image is used only to detect the plane on which the transparent objects are placed. Unlike other estimation methods, the proposed method estimates the whole shape of a transparent object. To validate the proposed method, the obtained estimation accuracy is compared with that of a machine-learning-based method. In addition, the estimated whole shape is compared with the measured data from a 3D scanner.