Wimi Develops A Deep CNN-Based 3D Image Reconstruction Algorithm System

The system contains several modules, including dataset preparation, feature extraction, parameter generation, 3D reconstruction, model evaluation, and application interface, each with unique functions and roles, forming a complete system.

Dataset preparation: The 3D image reconstruction algorithm needs a large amount of 3D model data as a training set so that the deep learning algorithm can learn the morphological and structural features of the 3D model. This module is responsible for collecting and producing the training dataset and performing data pre-processing and cleaning to ensure the quality and availability of the dataset. The dataset's quality directly affects the algorithm's accuracy and robustness. The dataset contains a variety of 3D models of different classes and morphologies to ensure the universality and generalization ability of the algorithm.

Feature extraction: This module performs feature extraction and representation of the input image using a convolutional neural network, which typically includes multiple convolutional and pooling layers, to extract high-level features from the input image.

Parameter generation: This module uses fully connected layers or other regression algorithms to map the feature vectors from the encoder output into the 3D space. These parameters control the morphology, size, pose, and other attributes of the 3D model.

3D reconstruction: This module inputs the parameters into the 3D model to generate the final 3D reconstruction model. This module typically uses deconvolution and upsampling layers to map the feature vectors from the encoder output into 3D space.

Model evaluation: This module evaluates the differences and errors between the generated 3D and original models. These errors can be used to optimize the algorithm parameters and improve the training dataset to increase the accuracy and robustness of the 3D reconstructed model.

Application Interface: This module presents the 3D reconstructed model and provides a user interaction interface that allows the user to adjust the attributes and parameters of the model to achieve customized design and personalization requirements.

Compared with traditional 3D reconstruction algorithms, WiMi's deep CNN-based
3D image reconstruction algorithm system has the advantages of high accuracy and adaptability. It uses deep learning to extract images' features and structural information by training a large amount of data to obtain a more accurate 3D model.

The system will have a broader application prospect with the rapid development of deep learning, computer vision algorithms, and virtual reality technology. For example, by relying on this technology, the medical field can better classify and diagnose cases, robots can perform more accurate obstacle avoidance, and manufacturing industries can achieve faster and more precise item modeling, etc. As the technology develops, it can also be combined with other technologies, such as AR and VR, to achieve a broader range of applications.

About WIMI Hologram Cloud

WIMI Hologram Cloud, Inc. (NASDAQ:wimi ) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.

Safe Harbor Statements

This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20−F and 6−K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward−looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services.

Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.

SOURCE WiMi Hologram Cloud Inc.

MENAFN26052023003732001241ID1106324693