Abstract
Point cloud can assist unmanned equipment to locate and detect in electric power inspection. It needs equipment and surrounding environment to obtain point cloud directly by radar. The efficiency of obtaining 3D point cloud in patrol inspection can be improved by using deep learning network through single image generation. In order to generate high-precision reconstruction results, a two-stage training network for 3D point cloud reconstruction is proposed in this paper. Firstly, the network of image to point cloud is trained and used to generate rough point cloud. Secondly, the trained point cloud auto-encoder generates more accurate point cloud data. Finally, the two models are combined to obtain accurate point cloud reconstruction results from an image. This method can generate accurate and uniform point cloud 3D model. The validity and practicability of the model are proved by the test of synthetic data set and the quantitative and qualitative analysis. Compared with the other three famous networks, the proposed network reconstruction accuracy is improved.
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