<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to have a better product display and thus attract consumers’ purchases and increase the economic benefits of the enterprise, in this paper, we propose a deep learning model for brand 3D image design. A feedforward neural network that estimates the error of previous layers based on the error of the output layer assigns the convolutional kernel weight parameters of the network in the interval and stops when the error reaches a preset accuracy or reaches a preset maximum learning count. The locally-aware convolutional neural network acquires local features that are finer than the global features and outputs the feature maps of the convolutional layers after passing the activation function to calculate the sensitivity of the sampled layer units. Given the sensitivity information of the feature map, the gradient of the kernel function weights is obtained, and the updated parameters are trained to achieve feature map recursion and solve the image boundary problem. A 3D recurrent neural network is constructed using data-driven multiple or single images, transformed into a low-dimensional feature matrix, processed with 3D pixel data, extracted perceptual features, and generated high-resolution images. The analysis of the results shows that the CD value of the used model is 0.477 and the EMD value is 0.579, which makes the constructed 3D images with more obvious detail levels and more accurate structural design, while the model of Pixel2Mesh focuses more on surface information, so the generated model is more realistic and closer to the real image.</p>
</abstract>

In order to have a better product display and thus attract consumers’ purchases and increase the economic benefits of the enterprise, in this paper, we propose a deep learning model for brand 3D image design. A feedforward neural network that estimates the error of previous layers based on the error of the output layer assigns the convolutional kernel weight parameters of the network in the interval and stops when the error reaches a preset accuracy or reaches a preset maximum learning count. The locally-aware convolutional neural network acquires local features that are finer than the global features and outputs the feature maps of the convolutional layers after passing the activation function to calculate the sensitivity of the sampled layer units. Given the sensitivity information of the feature map, the gradient of the kernel function weights is obtained, and the updated parameters are trained to achieve feature map recursion and solve the image boundary problem. A 3D recurrent neural network is constructed using data-driven multiple or single images, transformed into a low-dimensional feature matrix, processed with 3D pixel data, extracted perceptual features, and generated high-resolution images. The analysis of the results shows that the CD value of the used model is 0.477 and the EMD value is 0.579, which makes the constructed 3D images with more obvious detail levels and more accurate structural design, while the model of Pixel2Mesh focuses more on surface information, so the generated model is more realistic and closer to the real image.

Deep learning model-based brand design 3D image construction

Xinyang Agriculture and Forestry University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Deep learning model-based brand design 3D image construction"}

In order to have a better product display and thus attract consumers’ purchases and increase the economic benefits of the enterprise, in this paper, we propose...

Deep learning model-based brand design 3D image construction

Publié en ligne: 28 avr. 2023

Pages: -

Reçu: 06 mai 2022

Accepté: 28 sept. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00117

Mots clés
Brand design, Deep learning model, Convolutional neural network, Image construction, Image boundary

© 2023 Zeping Huang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Deep learning model-based brand design 3D image construction

Publié en ligne: 28 avr. 2023

Pages: -

Reçu: 06 mai 2022

Accepté: 28 sept. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00117

Mots clésBrand design, Deep learning model, Convolutional neural network, Image construction, Image boundary

© 2023 Zeping Huang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Brand design, Deep learning model, Convolutional neural network, Image construction, Image boundary