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Abstract Garment bagging is the plastic deformation of the knee, elbow and other parts of the garment when the human body moves. The current methods and indexes for evaluating the bagging performance of woven garments are not comprehensive enough. In view of this situation, 16 pieces of fabrics were made into pants-tube for actual wearing and bagging. The camera and the 3D laser scanner were used to acquire images and point cloud data of garments respectively. Then feature extraction was performed on the processed data to get two traditional geometric quantization indexes, bagging height and bagging volume. Furthermore, two new geometric quantification indexes were proposed: warp and weft maximum bagging rate. And several kinds of surface quantization indexes were also could be extracted, namely mean, average deviation, roughness, variance and torsion. At the same time, the subjective evaluation results had been obtained by the bagging images and the bagging 3D curved surfaces. After that, correlation analysis and ridge regression analysis were carried out between the two groups of indexes and the subjective score results. The results showed that, the two new geometric quantitative indexes were highly correlated with the subjective score, and the correlation coefficient were ?0.883 and ?0.923, respectively. The surface quantization indexes regression equation and the geometric quantification index regression equation were both accurate in predicting the subjective score, and the adjusted R2 reached 92.7% and 91.3%. The 3D laser scanning technology could be used to evaluate the garment bagging accurately.
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