WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality (“AR”) Technology provider, today announced that it had developed an interactive virtual reality holographic imaging system based on artificial intelligence technology to simulate realities in the digital world better. The system empowers virtual reality, improves the quality of content, optimizes and advance the personalized user experience, facilitates more effective interaction between users and technology, and provides users with more realistic virtual reality holograms.

WiMi’s system applies AI in the holographic imaging process. Artificial intelligence greatly improves the operational efficiency of holographic imaging by enabling it to track objects, create 3D world models, learn model features, and make judgments. Deep learning models using AI can help holographic imaging systems interpret complex environments, use more realistic models in holographic imaging systems, and empower users with more significant situational interaction to optimize the immersive experience of holographic imaging. Artificial intelligence will help drive the adoption of immersive technology in the consumer and commercial sectors.

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Applying deep learning methods to holographic imaging circumvents many of the problems associated with coherent imaging systems and takes full advantage of its holographic imaging paradigm. The system achieves holographic image phase recovery, phase unfolding, super-resolution, and sensing by supervised optimization of a deep CNN using accurately aligned image data. Deep CNNs typically contain tens to hundreds of layers of convolutional kernels (filters), bias terms, and non-linear activation functions. Developers first train the convolution kernels, bias terms, and non-linear activation functions in a convolutional neural network and then use the neural network to perform a predefined image reconstruction task, performing a single forward pass through the network to reconstruct an artificially intelligent computed hologram. This reconstruction process typically takes a fraction of a second to complete when using the GPU, without any iterations, manual tuning of any hyperparameters, or refinement of the physical assumptions made about the image reconstruction model. This non-iterative, single-forward propagation reconstruction capability constitutes one of the main advantages of deep learning-based imaging solutions.

In an AI-driven holographic imaging system, the greater the data collected, the more realistic the reconstructed virtual environment is likely to be. Higher-quality data can produce equally higher-quality environments and even create more personalized settings for the user. The virtual worlds depicted in the “metaverse” are complex, and building them is not simple. CNNs can process complex images and learn and predict by setting up scalable data pipelines to help deep learning models of artificial intelligence continuously train, improve and optimize the user experience.

In the future, artificial intelligence is expected to be the engine that drives the holographic industry forward. WiMi’s R&D team will further research artificial intelligence technology and apply it to interactive virtual real-world holographic imaging systems while incorporating holographic interaction technology to render virtual objects in natural scenes or put real things in virtual scenes to make holographic imaging more realistic.

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