Patent ID: 11880959
Assignee: SOUTH CHINA UNIVERSITY OF TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 1:
2. The method of claim 1, wherein the deep network model comprises a feature extraction module, a feature sampling module and a coordinate regression module, wherein
the feature extraction module is configured to extract eigenvectors from all of the first number of sparse input points received by the deep network model to obtain the initial eigenvectors of the first number of sparse input points;
the feature sampling module is configured to replicate the-initial eigenvectors to obtain a third number of first eigenvectors, determine a sampling probability according to a curvature of each sparse input point of the first number of sparse input points, perform sampling according to the sampling probability to obtain a fourth number of second eigenvectors, determine the second number of intermediate eigenvectors according to all of the third number of first eigenvectors and all of the fourth number of second eigenvectors, splice a 2D vector generated by a 2D mesh mechanism onto each intermediate eigenvector of the second number of intermediate eigenvectors, and input the spliced intermediate eigenvectors into the multilayer perceptron to obtain the second number of sampling eigenvectors, wherein the 2D vectors spliced onto each intermediate eigenvector of the second number of intermediate eigenvectors are different; and
the coordinate regression module is configured to determine the second number of sampling prediction points according to the second number of sampling eigenvectors.