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

Claim 0:
1. A method for point cloud up-sampling based on deep learning, comprising:
step 1: obtaining training data, wherein the training data comprises a first number of sparse input points and a second number of dense input points, the first number of sparse input points are uniformly sampled from a Computer Aided Design (CAD) model, and the second number of dense input points are sampled from the CAD model based on curvature;
step 2: constructing a deep network model, wherein the deep network model is used for extracting initial eigenvectors from the first number of sparse input points, respectively performing a replication operation and a sampling operation based on curvature features on the initial eigenvectors to obtain a second number of intermediate eigenvectors, performing a splicing operation on each intermediate eigenvector of the second number of intermediate eigenvectors, inputting the spliced intermediate eigenvectors into a multilayer perceptron to obtain a second number of sampling eigenvectors, and determining a second number of sampling prediction points according to the second number of sampling eigenvectors;
step 3: training the deep network model, determining an objective function according to the second number of sampling prediction points and the second number of dense input points, and adjusting parameters of the deep network model based on the objective function until the objective function converges; and
step 4: testing the deep network model, sampling several seed points from a point cloud representation of an object under test, respectively constructing an input point cloud to be tested from each seed point of the several seed points and neighborhood points thereof, inputting the input point clouds to be tested into the trained deep network model to obtain a test point cloud corresponding to each of the input point clouds to be tested respectively, and aggregating all of the test point clouds to obtain point cloud data of the object under test after up-sampling.