Research on Image Depth Estimation Based on PatchMatchNet in the Field of Holographic 3D Display

The display effect of real objects in holographic 3D displays primarily depends on the accuracy of their depth and detail information. However, existing methods face challenges such as insufficient precision, high computational cost, and limitations on mobile devices. To address these issues, this paper proposes a multi-view depth estimation method based on PatchMatchNet(PMN). The method extracts image features using a feature extraction network and optimizes depth estimation through an adaptive propagation and evaluation mechanism, thereby enhancing the accuracy of object depth estimation. The obtained depth map is used to generate a hologram via tomography, which is then displayed as a holographic 3D image using a Spatial Light Modulator (SLM). Comparative analysis with openMVS and MVSNet demonstrates that PMN outperforms in terms of both depth estimation accuracy and computational efficiency, validating its practical application in holographic displays.