High fidelity point completion for substation equipment point cloud

The raw substation equipment point cloud obtained by LIDAR is often partial due to occlusion and limitation of angles and can not provide an adequate data base for 3D reconstruction, shape classification, etc. Point cloud completion aims at estimating the full shape of an object based on partial observation. This paper propose a high fidelity point cloud completion model based the architecture of encoder-decoder. Proposed model gradually generates coarse point cloud and detailed point cloud. At the stage of encoder, a residual module ResMLP is designed using only MLP. There is a pyramid-like structure between modules to extract depth features, which has the ability of deeper network expansion. At the stage of decoder, by combining partial input with coarse point cloud, the enhanced skeleton center points of objects are obtained by extracting key points, which alleviate structural blur of input. Finally, the visualization of experimental results shows that the proposed model can effectively supplement the missing part of the equipment point cloud. Even some objects perform well with 50% missing. The final evaluation metrics CDL1, CDL2 and F-Score reach 10.490, 0.362 and 0.601, respectively.