WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (“WiMi” or the “Company”), a leading global Hologram Augmented Reality (“AR”) Technology provider, today announced the development of a 3D-CNN hologram classification algorithm based on deep learning. This technique uses a convolutional neural network and computer vision to build classifiers for classifying targets in holograms.
Using a 3D stereo hologram as input can capture the shape and spatial features of the target more accurately. The hologram image passes through convolutional, pooling, and fully connected layers, from which the algorithm extracts feature information and filters and optimizes it layer by layer. This enables fast and accurate automatic recognition and classification of 3D objects. 3D-CNN can efficiently extract 3D features of multiple resolutions and combine them to improve classification performance. When training the model, supervised learning is performed using labeled holograms, and a back-propagation algorithm optimizes the model parameters.
The 3D-CNN-based hologram classification technology provides essential technical support for object recognition by training neural network models to achieve fast and accurate classification of holograms with the advantage of deep learning. The implementation steps of this algorithm technology include: first, the hologram is feature extracted and pre-processed to transform it into 3D tensor data. Then, the 3D-CNN is used to train and learn the features of the hologram and extract its high-level semantic features. Finally, a classifier is used to classify the obtained features to achieve the automatic classification of the hologram.
WiMi’s 3D-CNN-based hologram classification technology can adapt to the particular characteristics of holograms and better handle the 3D and wavefront information of holograms. Its use of deep neural networks can extract more feature information to achieve higher accuracy classification. 3D-CNN can utilize GPU for efficient parallel computing and high training efficiency. Moreover, it will scale with the increased data size, which can handle more data and obtain better classification results.
The 3D-CNN-based hologram classification algorithm has many applications and development prospects in several fields. Meanwhile, its technical principle can also be applied to classifying or processing other 3D images, which have good promotion value. 3D-CNN-based hologram classification technology has been widely used in autonomous driving, medical image diagnosis, intelligent security, virtual reality, etc.
In autonomous driving, hologram classification can identify vehicles, pedestrians, traffic lights, and other objects on the road, thus helping automatic driving decisions and realizing functions such as automated vehicle driving, safety detection, and path planning. In medical image diagnosis, hologram classification can analyze and diagnose medical images to help doctors make fast and accurate diagnoses and improve their work efficiency. In intelligent security, 3D-CNN-based hologram classification technology can be used for character recognition, behavior analysis, etc., to enhance monitoring effects and early warning capabilities. In virtual reality, hologram classification can realize object recognition in the virtual world, thus enhancing the realism and interactivity of virtual reality.
With the continuous development and deepening of artificial intelligence technology, the application field of 3D-CNN-based hologram classification technology will be expanded. Its application in intelligent transportation, medical care, security, virtual reality, and other areas will bring more convenience and innovation.
However, the 3D-CNN-based hologram classification algorithm technology also faces some challenges. It faces problems such as complex data acquisition, high computational complexity, and model parameter tuning, which require continuous exploration and research solutions.WiMi will also further study how to improve the performance and efficiency of 3D-CNN-based hologram classification algorithm technology in the future and continuously expand its application scenarios.
