In the field of aircraft design and maintenance, with the innovation of cabin cable three-dimensional (3D) scanning and sensor technology, high-precision cabin point cloud data has become the key to improving the accuracy of cabin navigation and building a realistic virtual reality environment. In the face of largescale 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 upsampling network (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 perceptron (MLP) 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.