WiMi Developed DCNN AI Technology Based on N-dimensional Manifold Holographic Technology

 WiMi Hologram Cloud, a leading global Hologram Augmented Reality (“AR”) Technology provider,  announced that its R&D team had developed a method for creating realistic 3D holographic digital content using deep convolutional neural network AI models based on N-Dimensional Manifold algorithmic holographic technology.

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The main difficulty and challenge in producing holographic digital content is the amount of data in holographic digital content. Each holographic digital content contains a large amount of data. These data include a full range of 360-degree digital information such as depth of field and dimension of the holographic digital content. The production of holographic digital content requires a large amount of computing power. Previously, the industry usually used distributed algorithms for computational processing, which required many advanced computers. Therefore, the first holographic digital content was done in-house or in professional holographic digital content research labs, and its natural production is rather costly. The high cost of holographic content and display made it unavailable for ordinary people. However, with the development of AI technology, researchers at the WiMi R&D Center were able to develop DCNN AI technology for solving problems based on N-dimensional manifold holographic technology. This AI-driven model is the best way to generate holographic content from a series of input images.

Traditional holographic content production methods create many holographic blocks and then use specific algorithmic models to synthesize these holographic blocks into a complete hologram, similar to a photo that is not large enough to be stitched together with multiple images. Therefore, each time the production of holographic digital content requires a lot of time and effort, and the requirements for computer graphics processing capabilities are incredibly high. WiMi’s technology is different from traditional holographic digital content processing. It uses DCNN AI technology to intelligently discriminate images and identify N-dimensional information, such as objects, depth of field, phase, etc., in digital photos, and then process 3D holographic images pixel-wise and with high precision through complementary difference algorithm control. DCNN AI can replace computer peripherals such as manual or multi-camera LIDAR sensors,

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significantly reducing image blocks that need to be processed. It can analyze images and categorize them into groups by CNN, considerably reducing the reliance on computer image processing power. In this case, holographic digital content can be processed and generated using ordinary computers or mobile devices like cell phones and tablets. Combined with WiMi’s holographic digital content compression processing system, the intelligent algorithm removes coding redundancy, temporal redundancy, spatial redundancy, and irrelevant information desensitization to compress the data volume of holographic content and achieve a lightweight layout of holographic digital content, which is more conducive to the dissemination of holographic content.

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