Tao Zhang1, Shaokui Gu1, Jinxing Niu1,*, Yi Cao2
CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4603-4614, 2022, DOI:10.32604/cmc.2022.027079
Abstract Traditional three-dimensional (3D) image reconstruction method, which highly dependent on the environment and has poor reconstruction effect, is easy to lead to mismatch and poor real-time performance. The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology. To solve the problem, a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper. The algorithm first extracts the feature information of multiple two-dimensional (2D) images based on scale and rotation invariance parameters of Scale-invariant feature transform (SIFT) operator. Secondly, self-encoding learning neural network is introduced into the… More >
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