基于服裝點云的高精度袖窿分割線自動識別方法

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中圖分類號：TS941.26 文獻標志碼：A 文章編號：1673-3851（2025）07-0488-10

引用格式：，等?；诜b點云的高精度袖窿分割線自動識別方法[J].學報（自然科學），2025，53（4）：488-497.

Abstract：To solve the problems that it is dificult to identify the armhole segmentation lines and that the sleeve template does not meet the requirements of plate making，an automatic high-precision recognition method for armhole segmentation lines based on garment point clouds was proposed by taking tops with the shoulder line at the shoulder position as an example. First，a dynamic graph convolutional neural network （DGCNN） was used to coarsely segment the garment point cloud to obtain the armhole point cloud area，and the armhole key point cloud was obtained by denoising. Then，the weighted least squares method was used to fit the key point cloud of the armhole，and the front and rear armhole dividing lines were obtained，and the split lines were optimized by combining the arc interpolation method and cubic spline interpolation to solve the problems of missing and dislocated connections. Finally，the surface deployment algorithm based on angle protection generated the sleeve template，and the sleeve cap arc correction scheme was added to compare the generated template with the real one and that generated by the NeuralTailor model to verify the accuracy of the templates. The experimental results show that the armhole segmentation line generated by this method is highly similar to the real armhole segmentation line，and the RMSE and MAE are both less than 0.200cm . The sleeve template generated by surface deployment algorithm based on angle protection has high precision，with the average deviation of the key dimensions of the sleeve being 0.238cm ，and the average absolute error value of sleeve template being 0.296cm ，which further verifies the accuracy of the armhole dividing line. This provides an effective technological path for the automated generation of garment sleeve templates，and can enhance the efficiency of clothing design and development.

Key words： garment point clouds； armhole segmentation line recognition； dynamic graphconvolutional network;curve fitting; surface deployment； sleeve body template generation

0引言

隨著服裝產(chǎn)業(yè)數(shù)字化和智能化的快速發(fā)展，服裝行業(yè)的生產(chǎn)與銷售模式正在經(jīng)歷深刻的變革。（剩余10365字）