Abstract: The present invention discloses a method for point cloud up-sampling based on deep learning, including: obtaining training data including a first number of sparse input points and a second number of dense input points; constructing a deep network model to be used for respectively performing replication and sampling operation based on curvature on initial eigenvectors extracted from the first number of sparse input points to obtain a second number of intermediate eigenvectors, performing splicing operation on each intermediate eigenvector, inputting the spliced intermediate eigenvectors into a multilayer perceptron, and determining sampling prediction points based on the sampling eigenvectors output by the multilayer perceptron; training the deep network model until an objective function determined by the sampling prediction points and the dense input points converges; and testing the deep network model to obtain point cloud data of an object under test after up-sampling.

Abstract: A damping bracket and protective case for an electronic device with the same is disclosed, the damping bracket includes a base, a supporting part and a damping shaft; the damping shaft is a tubular member capable of elastic deformation in a radial direction, and an opening penetrating through both ends of the damping shaft is provided; one end of the base is provided with a first connecting part, and the first connecting part is provided with a first shaft hole; the supporting part is provided with a hinge end and a supporting end, the supporting end is provided with a second connecting part, and the connecting second part is provided with a second shaft hole; the damping shaft is arranged in the first shaft hole and the second shaft hole in an interference fit manner; the damping bracket is installed on the back side of the mobile phone case, which can provide multi-angle support for the horizontal screen and vertical screen of the mobile phone, free both hands, and ensure the clear picture of the mobile pho

Abstract: The present invention discloses a method for point cloud up-sampling based on deep learning, including: obtaining training data including a first number of sparse input points and a second number of dense input points; constructing a deep network model to be used for respectively performing replication and sampling operation based on curvature on initial eigenvectors extracted from the first number of sparse input points to obtain a second number of intermediate eigenvectors, performing splicing operation on each intermediate eigenvector, inputting the spliced intermediate eigenvectors into a multilayer perceptron, and determining sampling prediction points based on the sampling eigenvectors output by the multilayer perceptron; training the deep network model until an objective function determined by the sampling prediction points and the dense input points converges; and testing the deep network model to obtain point cloud data of an object under test after up-sampling.

Abstract: In one embodiment, a method of processing a natural language input using a conversational knowledge graph in a virtual assistant is disclosed. The method includes receiving a natural language query from a user; translating the natural language query received from the user into corresponding intents; retrieving conversational knowledge context information based on the intents; using the retrieved conversational knowledge context information to customize back-end service calls to downstream applications; receiving a result of the customized back-end service calls; sending the result of the customized back-end service calls in a response to the natural language understanding system; translating the response from the fulfillment service system into a natural language response; and providing the natural language translated response to the user.

Abstract: In one embodiment, a method of processing a natural language input using a conversational knowledge graph in a virtual assistant is disclosed. The method includes receiving a natural language query from a user; translating the natural language query received from the user into corresponding intents; retrieving conversational knowledge context information based on the intents; using the retrieved conversational knowledge context information to customize back-end service calls to downstream applications; receiving a result of the customized back-end service calls; sending the result of the customized back-end service calls in a response to the natural language understanding system; translating the response from the fulfillment service system into a natural language response; and providing the natural language translated response to the user.