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title
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PartSTAD: 2D-to-3D Part Segmentation Task Adaptation
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site name
author
updated
2026-03-06 18:29:19
raw text
PartSTAD: 2D-to-3D Part Segmentation Task Adaptation More Research PartSTAD PartSTAD: 2D-to-3D Part Segmentation Task Adaptation ECCV 2024 Hyunjin Kim 1 , Minhyuk Sung 2 , 1 KRAFTON Inc, 2 KAIST arXiv Code Abstract We introduce PartSTAD , a method designed for the task adaptation of 2D-to-3D segmentation lifting. Recent studies have highlighted the advantages of utilizing 2D segmentation models to achieve high-quality 3D segmentation through few-shot adaptation. However, previous approaches have focused on adapting 2D segmentation models for domain shift to rendered images and synthetic text descriptions, rather than optimizing the model specifically for 3D segmentation. Our proposed task adaptation method finetunes a 2D bounding box prediction model with an objective function for 3D segmentation. We introduce weights for 2D bounding boxes for adaptive merging and learn the weights using a small additional neural network. Add...
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2026-03-06 18:29:19
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