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Journal Article
SMOTE3D: Geometry- and color-aware volumetric data augmentation for 3D fashion asset segmentation
- Authors
- Jiyoun Lim, Jeong-Woo Son, Alex Lee, Sun Joong Kim, Namkyung Lee, Wonjoo Park
- Citation
- ETRI Journal, Early Access
- ISSN
- 1225-6463
- Publisher
- John Wiley & Sons
- Language
- English
- Type
- Journal Article
- 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.
- Keyword
- 3D volumetric data, data augmentation, fashion asset segmentation, imbalanced learning, synthetic data
- KSP Keywords
- 3D data, Data Augmentation, Digital Twin, Imbalanced Learning, Point Cloud Data, Synthetic data, Volumetric data, Wilcoxon test, class imbalance, mean average precision, plug-in
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
