Friday, May 2025
11:20 AM - 11:40 AM
Room: 220C
Session: Artificial Intelligence for AR/VR/MR
Neural Network-Empowered Hologram Compression for Computational Near-Eye Displays
Student
Description:
Holography enhances VR and AR displays by providing realistic 3D imagery, but current computer-generated hologram (CGH) algorithms face high computational demands. This work presents an efficient hologram generation and compression method using a pre-trained wave propagation model and a filter-free design. The approach reduces data redundancy, simplifies hardware, and achieves near real-time decoding, enabling practical use in compact AR/VR systems.