High fidelity point completion for substation equipment point cloud

Da Li

doi:10.1117/12.3037812

12 September 2024 High fidelity point completion for substation equipment point cloud

Da Li

Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 132560M (2024) https://doi.org/10.1117/12.3037812
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Da Li "High fidelity point completion for substation equipment point cloud", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 132560M (12 September 2024); https://doi.org/10.1117/12.3037812

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