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Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels
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2026-01-28 17:40:12
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Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels Segment3D Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels ECCV 2024 Rui Huang 1 , Songyou Peng 2 , Ayça Takmaz 2 , Federico Tombari 3 , Marc Pollefeys 2,4 , Shiji Song 1 , Gao Huang 1* , Francis Engelmann 2,3 1 Tsinghua University, 2 ETH Zurich, 3 Google, 4 Microsoft * Corresponding author contact: hr20 (at) mails (dot) tsinghua (dot) edu (dot) cn arXiv Video Code Demo Mask3D trained on manual labels. Segment3D trained on automatic labels. Segment3D (right) predicts accurate segmentation masks, improves over fully-supervised 3D segmentation methods e.g., Mask3D (left), and requires no manually labeled 3D training data at all . Abstract Current 3D scene segmentation methods are heavily dependent on manually annotated 3D training datasets. Such manual annotations are l...
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