Less-parametic Point Cloud Upsampling Network
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Abstract:
In the field of aircraft design and maintenance, with the innovation of cabin cable 3D scanning and sensor technology, high-precision cabin point cloud data has become the key to improve the accuracy of cabin navigation and build a realistic virtual reality environment. In the face of large scale point cloud data, how to efficiently and uniformly construct a realistic virtual reality environment has become a challenge. In this paper, we propose a new low-parametric point cloud up sampling method, LPNet, which is based on the no-learn model to learn the complementary geometric knowledge between point clouds based on some simple data transformations, to efficiently retain the geometric properties of point clouds, and then input the results into the up-sampling module, and simply insert a few layers of multilayer perceptive machines (MLPs) to efficiently generate high-resolution point clouds. It is able to efficiently generate high-resolution point clouds, showing great flexibility and realizing the efficient use of computational resources.
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Project Supported:
The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)
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