Abstract
To improve the three-dimensional (3D) reconstruction effect of intelligent manufacturing image and reduce the reconstruction time, a new CAD-aided 3D reconstruction of intelligent manufacturing image based on time series was proposed. Kinect sensor is used to collect depth image data and convert it into 3D point cloud coordinates. The collected point cloud data are divided into regions, and different point cloud denoising algorithms are used to filter and denoise the divided regions. With the help of CAD, FLANN matching algorithm is used to extract feature points of time-series images and complete image matching. Three-dimensional reconstruction of sparse point cloud and dense point cloud is carried out to complete 3D reconstruction of intelligent manufacturing images. The experimental results show that the image PSNR of this method is always above 52 dB, and the maximum reconstruction time is 4.9 s. The 3D reconstruction effect of intelligent manufacturing image is better, and it has higher practical application value.
Highlights
In recent years, in the field of information technology and industry, significant changes have taken place, such as large data, cloud computing, mobile Internet, 3D printing, and industrial robots, and these changes have brought a new round of revolution in global manufacturing, including intelligent manufacturing as a product of informatization and industrialization depth fusion, and got great progress
Multiple intelligent manufacturing images were collected, and the collected images were integrated to construct the experimental sample set. e experimental sample set was input into the computer, and the data in the sample set were processed using the reconstruction method based on P2M framework improvement, the reconstruction method based on vanishing point optimization and the reconstruction method based on time series
Compared with the reconstruction methods based on P2M frame improvement and vanishing point optimization, the reconstruction time of image 3D reconstruction based on time series always remains at a low level, indicating that this method has a very high efficiency of 3D reconstruction of intelligent manufacturing images
Summary
In the field of information technology and industry, significant changes have taken place, such as large data, cloud computing, mobile Internet, 3D printing, and industrial robots, and these changes have brought a new round of revolution in global manufacturing, including intelligent manufacturing as a product of informatization and industrialization depth fusion, and got great progress. According to the obtained 3D coordinates, the 3D point cloud of the object surface is obtained, and the 3D reconstruction of fuzzy image edge contour is realized based on machine learning. Based on FLANN matching algorithm, feature points of time-series images were extracted, and 3D reconstruction of sparse point cloud and dense point cloud was carried out by CMVS algorithm and PMVS algorithm, respectively, so as to complete 3D reconstruction of intelligent manufacturing images
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