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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.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
