SMOTE3D: Geometry‐ and color‐aware volumetric data augmentation for 3D fashion asset segmentation
Jiyoun Lim, Jeong-Woo Son, Lee Alex, Sun Joong Kim, NamKyung Lee, Wonjoo Park · ETRI Journal · 2026
Abstract This study proposes a geometry‐ and color‐aware 3D data‐augmentation framework to enhance fashion asset segmentation for digital twin applications. This study focuses on converting 2D fashion videos into 3D point‐cloud data and augmenting the minority classes. The constructed dataset comprised 502 mannequin‐wearing scenes and additional single‐asset captures, totaling 628 instances across 16 categories and 104 items, all recorded using an iPhone 14 Pro. To address class imbalance, three augmentation strategies are introduced: SMOTE3D (normal‐aware coordinate interpolation with red–green–blue jitter), scene reconfiguration, and class‐consistent color shifts. Using OctFormer, OA‐CNNs, and PTv3, the augmented data consistently improved the accuracy, intersection over union (IoU), and mean average precision (mAP), yielding significant gains over a Mix3D plug‐in baseline, as confirmed by a paired Wilcoxon test.