A collaborative shape segmentation method based on graph convolution neural network
A convolutional network and collaborative segmentation technology, applied in the field of geometric modeling and analysis of graphics, can solve the problem of high time complexity in the training process and achieve high precision
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[0020] In conjunction with the appendix, the technical solution of the present invention is clearly and completely described through specific implementation examples.
[0021] 1. Network structure diagram
[0022] Such as figure 1 As shown, the system is mainly divided into three steps. First, the shape is over-segmented into 30 sub-slices. A graphical model is then constructed for these sub-slices through feature extraction. Finally, through the graph convolutional network model, the segmentation results of other models on the graph are learned.
[0023] 2. The model is over-segmented
[0024] figure 2 It shows that we have over-segmented the model. The system uses normalized segmentation to segment the model. We divide each model into 30 sub-slices. Their boundaries basically coincide with the feature lines. Therefore, the segmentation problem is transformed into the clustering problem of over-segmented sub-slices.
[0025] 3. Network output description
[0026] i...
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