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A Multimodal-Based Point Cloud Segmentation Strategy for Shoe Upper Processing Boundaries Under Complex Interferences
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AI
DOI：10.3390/machines14050533.png)
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Abstract
En 中文
Accurate segmentation of shoe upper processing boundaries is crucial for automated trajectory generation and high-precision robotic control. However, developing a robust method is challenging due to the frequent style changes in High-Mix Low-Volume production. The reliance on large-scale annotated datasets renders traditional supervised methods impractical due to the prohibitive cost of annotation and retraining. To address these issues, a multimodal-based point cloud segmentation strategy is proposed for shoe upper processing boundaries. First, an unsupervised adaptive local spectral contrast filtering algorithm is designed to remove large amounts of background noise and isolate potential target regions by exploiting boundary color characteristics. Then, an unsupervised dynamic ellipsoidal neighborhood color-spatial region growing algorithm is developed based on geometric features of slender and closed boundary shapes to suppress interferences flanking the boundaries. Finally, a Siamese network is designed to perform few-shot matching against boundary templates exported from Shoemaster, effectively decoupling intrinsic boundary signals from complex extrinsic interferences to achieve precise segmentation. Experimental results demonstrate that the proposed method achieves a stable mean Intersection over Union (mIoU) of approximately 0.80. Compared to existing supervised and unsupervised baselines, this strategy exhibits superior generalization across diverse styles and effectively resolves the data dependency bottleneck.
Keywords:
precision manufacturing
point cloud segmentation
machine vision
process automation
data-efficient manufacturing