Title:
DEEP LEARNING-BASED POINT CLOUD UPSAMPLING METHOD
Document Type and Number:
WIPO Patent Application WO/2021/232687
Kind Code:
A1
Abstract:
Disclosed in the present invention is a deep learning-based point cloud upsampling method, comprising: obtaining training data made up of a first quantity of sparse input points and a second quantity of dense input points; constructing a deep network model, used for respectively performing copying and curvature-based sampling operations on an initial feature vector extracted from the first quantity of sparse input points, obtaining a second quantity of intermediate feature vectors, performing a joining operation on every intermediate feature vector, inputting intermediate feature vectors having undergone said joining operations into a multi-layer perceptron, and determining sampling prediction points on the basis of sampling feature vectors output from the multilayer perceptron; training the deep network model until target functions determined by the sampling prediction points and the dense input points converge; testing the deep network model, and obtaining point cloud data of a test object after upsampling. The present method is able to convert a sparse point cloud into a curvature adaptive distribution-based dense point cloud, accurately representing contours of an object, and better facilitating 3D data expression, rendering, and visualization.
Inventors:
JIA KUI (CN)
LIN JIEHONG (CN)
CHEN KE (CN)
LIN JIEHONG (CN)
CHEN KE (CN)
Application Number:
PCT/CN2020/125380
Publication Date:
November 25, 2021
Filing Date:
October 30, 2020
Export Citation:
Assignee:
UNIV SOUTH CHINA TECH (CN)
International Classes:
G06T17/20
Foreign References:
| CN111724478A | 2020-09-29 | |||
| CN107862293A | 2018-03-30 | |||
| CN110163799A | 2019-08-23 | |||
| US20130151210A1 | 2013-06-13 | |||
| US20180089536A1 | 2018-03-29 |
Other References:
YANG, BIN ET AL.: "Adaptive Up-Sampling Algorithm of Point Cloud Model", APPLICATION RESEARCH OF COMPUTERS, vol. 29, no. 6, 30 June 2012 (2012-06-30), pages 2354 - 2356, XP055870261, ISSN: 1001-3695
Attorney, Agent or Firm:
JIAQUAN IP LAW (CN)
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