A multimodal synthetic 3D point cloud dataset of shoe uppers for robotic processing boundary segmentation

Summary

Abstract Robotic processing of shoe uppers requires accurate segmentation of thin processing boundaries in 3D point clouds. However, acquiring and annotating large collections of real-world scans is costly. We present a multimodal synthetic 3D point cloud dataset of shoe uppers for robotic processing boundary segmentation. The dataset contains 1,067 samples, including 1,000 synthetic point clouds and 67 real-world test scans for independent evaluation. The synthetic data generation pipeline includes texture and appearance augmentation, region-aware geometric deformation, and sensor degradation simulation. Both branches provide three-dimensional coordinates, RGB attributes, and pointwise binary processing boundary labels. The synthetic branch additionally includes panel identifiers, while the real-world labels were manually refined. Technical validation with standard point cloud segmentation backbones assesses dataset usability under a controlled synthetic-to-real evaluation protocol. The released point clouds form a complete, directly usable dataset. The accompanying code and assets support regeneration and adaptation of synthetic subsets.