Synchronous Update and Optimization Method for Large-Scale Image 3D Reconstruction Technology Under Cloud-Edge Fusion Architecture

Aiming at the problems of limited bandwidth and network delay in the traditional centralized cloud computing mode during large-scale image processing of transmission and distribution digital corridors, a synchronous updating and optimization method for large-scale image 3D reconstruction technology under cloud-edge fusion architecture is proposed. Based on the cloud-side fusion architecture, the image data of the transmission and distribution corridor is preprocessed, feature extraction is performed by deep learning, synchronous updating is performed by using the cloud-side cooperative network, and matching and 3D reconstruction are performed according to the order of the point cloud data; given the dynamically changing characteristics of the image data in the cloud-side fusion environment, the incremental learning is combined with the continuous learning and synchronous updating of the model parameters, to realize the adaptive updating mechanism. The research method utilizes the advantage of cloud-edge fusion architecture to distribute the computational tasks to the cloud and edge, realizing parallel processing and load balancing, and improving the accuracy and efficiency of 3D reconstruction. The experimental results show that the research method in this paper has an image feature point matching rate as high as 96.72%, a lower network latency rate, and a higher real-time performance, which provides strong technical support for the optimization of the transmission and distribution digital corridor 3D reconstruction technology.

@article{Zhang2024,
  title     = {Synchronous Update and Optimization Method for Large-Scale Image 3D Reconstruction Technology Under Cloud-Edge Fusion Architecture},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Jian Zhang and Jingbin Luo and Yilong Chen},
  doi       = {10.14569/IJACSA.2024.01508112},
  url       = {https://doi.org/10.14569/IJACSA.2024.01508112}
}