Visual hull-based prediction framework for 3D object coding

In this paper, we propose a visual hull-based predictive framework for compressing depth data of a 3D object. Given a 3D object represented by layered depth image, we obtain a visual hull as a model. Then we compute and encode prediction residuals between the visual hull and the original 3D object. We first explain the merits of a visual hull in terms of prediction accuracy, computational costs, and efficient encoding of a model, and then we show the overall process of the proposed method.