GPT-based Textile Pilling Classification Using 3D Point Cloud Data
This work addresses textile quality control for manufacturers, but it is incremental as it adapts existing methods to a new dataset.
The paper tackles textile pilling classification by introducing TextileNet8, the first publicly available eight-category 3D point cloud dataset for this task, and proposes the PointGPT+NN model, which achieves 91.8% overall accuracy and 92.2% mean per-class accuracy on this benchmark.
Textile pilling assessment is critical for textile quality control. We collect thousands of 3D point cloud images in the actual test environment of textiles and organize and label them as TextileNet8 dataset. To the best of our knowledge, it is the first publicly available eight-categories 3D point cloud dataset in the field of textile pilling assessment. Based on PointGPT, the GPT-like big model of point cloud analysis, we incorporate the global features of the input point cloud extracted from the non-parametric network into it, thus proposing the PointGPT+NN model. Using TextileNet8 as a benchmark, the experimental results show that the proposed PointGPT+NN model achieves an overall accuracy (OA) of 91.8% and a mean per-class accuracy (mAcc) of 92.2%. Test results on other publicly available datasets also validate the competitive performance of the proposed PointGPT+NN model. The proposed TextileNet8 dataset will be publicly available.