PS-NET: an end-to-end phase space depth estimation approach for computer-generated holograms

In the present work, an end-to-end approach is proposed for recovering an RGB-D scene representation directly from a hologram using its phase space representation. The proposed method involves four steps. First, a set of silhouette images is extracted from the hologram phase space representation. Second, a minimal 3D volume that describes these silhouettes is extracted. Third, the extracted 3D volume is decomposed into horizontal slices, and each slice is processed using a neural network to generate a coarse estimation of the scene geometry. Finally, a third neural network is employed to refine the estimation for higher precision applications. Experimental results demonstrate that the proposed approach yields faster and more accurate results compared to numerical reconstruction-based methods. Moreover, the obtained RGB-D representation can be directly utilized for alternative applications such as motion estimation.