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| Large Scale Point Cloud Lightweight Method for Power Transformer Area Based on Spectral Graph Theory |
| Yang Fan1, Wu Tao1, Hao Hanxue1, Diao Guanxun2, Li Yong3 |
1. State Key Laboratory of Power Transmission Equipment Technology Chongqing University Chongqing 400044 China;
2. State Grid Shanghai Electric Power Company Shanghai 200010 China;
3. The YDROBOT Company Beijing 100080 China |
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Abstract The three-dimensional visualization based on point clouds has important application value in the digital and intelligent transformation of the power industry. For transformers, the actual production often obtains the point cloud of their entire operating area, and the huge data scale brings difficulties to use. Existing lightweight methods for processing large-scale point clouds always lead to significant visual distortion. Therefore, this paper proposes anew point cloud lightweight method that can take visual effects into account.
Firstly, the K-nearest neighbor (KNN) algorithm establishes an edge set for the original point cloud and converts it into a graph signal. Then, the graph signal is transformed into the frequency domain through the graph Fourier transform. Further, the feature and uniformity loss expressions during point cloud lightweight are derived. After quantifying the visual information loss, an objective function was established to minimize the loss. Then, the grid search was performed on parameters k and ρ, which affect the proportion of features and uniformity in the lightweight point cloud. A set of lightweight point clouds that meet the minimum visual information loss but have different proportions of features and uniformity can be obtained. A visual distortion quantification criterion was established to select the lightweight point cloud with the lowest visual distortion. The criteria project the 3D point cloud onto a feature domain composed of geometric and color features closely related to visual effects, and convert the visual effects of the point cloud into multiple histograms. Furthermore, statistical parameters quantify each feature's histograms, and the point cloud's visual effects are transformed into vectors. The visual distortion was calculated using the difference between vectors, and the lightweight point cloud with the lowest visual distortion was selected. Finally, the effectiveness of the proposed method was validated using a benchmark dataset and a large-scale point cloud of transformer areas containing over 80 million points. Based on the visual scores calibrated on the benchmark dataset, the correctness of the established visual distortion quantification criteria was first verified, further validating the effectiveness of the proposed method. For a large-scale colored point cloud in a specific transformer area, the proposed method has improved the visual effect of the lightweight point cloud by 57.4%, 69.2%, 62.2%, and 75.6% compared to mainstream random down-sampling, voxel averaging, non-uniform grid, and curvature sampling methods, respectively.
The following conclusion can be drawn: the visual effect of lightweight point clouds is not only affected by visual information loss but also by the proportion of feature information and uniformity in the lightweight point cloud. It is necessary to further search for the optimal proportion while minimizing visual information loss to obtain the lightweight point cloud with the lowest visual distortion. Projection of point clouds onto geometric and color feature domains related to visual effects can quantitatively describe the visual distortion of lightweight point clouds and thus select the lightest point cloud with the best visual effect. Compared with mainstream methods, the lightweight point cloud obtained by the proposed method has better visual effects while retaining the same number of points.
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Received: 27 November 2023
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2024, Vol. 39 
