SHS Web Conf.
Volume 181, 20242023 International Conference on Digital Economy and Business Administration (ICDEBA 2023)
|Number of page(s)
|Supply Chain Management and Logistics
|17 January 2024
Application of Structured Light 3D Reconstruction Technology in Industrial Automation Scenarios in the Context of Digital Transformation
ZheJiang Gongshang University Hangzhou College of Commerce, China
* Corresponding author: email@example.com
In this work, a dynamic structured light 3D reconstruction method adapted to random phase shift step size is proposed for the problem of dynamic workpiece 3D reconstruction in industrial manufacturing scenes. The method firstly utilizes a structured light sinusoidal grating pattern to match the relative phase shift of the workpieces moving along the perpendicular direction of the stripes on the conveyor belt. Secondly, in order to solve the problem of non-uniform phase shift step length due to external vibration, electromagnetic radiation and other disturbing factors of the conveyor belt and other mechanical devices, the method proposes a dynamic structured light 3D reconstruction model based on RPSNet, which is based on the CycleGAN network model, and uses the AIR2U-net model proposed in this paper as the generator, and the multilayer convolutional neural network CNN as the discriminator to realize the grating map. discriminator to realize the conversion of raster map to depth map. Finally, for the problem that the network model needs a large amount of data for training and it is difficult to collect data in the actual industrial scene, this paper uses the Thing10k dataset, and the dataset made by Blender simulation software for model training. Finally, a higher quality 3D reconstruction of the workpiece is realized.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.