title: Find Any Part in 3D
summary: We study open-world part segmentation in 3D: segmenting any part in any
  object based on any text query. Prior methods are limited in object categories
  and part vocabularies. Recent advances in AI have demonstrated effective
  open-world recognition capabilities in 2D. Inspired by this progress, we
  propose an open-world, direct-prediction model for 3D part segmentation that
  can be applied zero-shot to any object. Our approach, called Find3D, trains a
  general-category point embedding model on large-scale 3D assets from the
  internet without any human annotation. It combines a data engine, powered by
  foundation models for annotating data, with a contrastive training method. We
  achieve strong performance and generalization across multiple datasets, with up
  to a 3x improvement in mIoU over the next best method. Our model is 6x to over
  300x faster than existing baselines. To encourage research in general-category
  open-world 3D part segmentation, we also release a benchmark for general
  objects and parts. Project website: https://ziqi-ma.github.io/find3dsite/
paper_authors._id: 673f0a21c46b16fa202d60c8
paper_authors.name: Ziqi Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:53:23.057Z
paper_authors._id: 672fba6df51af868287e029b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/672fba6df51af868287e029b/cdwIFU3U0uk_s7AOn90fF.jpeg
paper_authors.fullname: Ziqi
paper_authors.type: user
paper_authors.user: ziqima
paper_authors._id: 673f0a21c46b16fa202d60c9
paper_authors.name: Yisong Yue
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:52:57.125Z
paper_authors._id: 6350307b14fb199c76589589
paper_authors.avatarUrl: /avatars/41ed1cda4fc5a37e98c5fdb3fdbfe09c.svg
paper_authors.fullname: Yisong
paper_authors.type: user
paper_authors.user: yisongyue
paper_authors._id: 673f0a21c46b16fa202d60ca
paper_authors.name: Georgia Gkioxari
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:52:51.130Z
paper_authors._id: 634ededbd049354d7ee4b557
paper_authors.avatarUrl: /avatars/4d53f3653696793f56c817b16069a99f.svg
paper_authors.fullname: Georgia Gkioxari
paper_authors.type: user
paper_authors.user: gkioxari
paper_title: Find Any Part in 3D
paper_summary: We study open-world part segmentation in 3D: segmenting any part in any
  object based on any text query. Prior methods are limited in object categories
  and part vocabularies. Recent advances in AI have demonstrated effective
  open-world recognition capabilities in 2D. Inspired by this progress, we
  propose an open-world, direct-prediction model for 3D part segmentation that
  can be applied zero-shot to any object. Our approach, called Find3D, trains a
  general-category point embedding model on large-scale 3D assets from the
  internet without any human annotation. It combines a data engine, powered by
  foundation models for annotating data, with a contrastive training method. We
  achieve strong performance and generalization across multiple datasets, with up
  to a 3x improvement in mIoU over the next best method. Our model is 6x to over
  300x faster than existing baselines. To encourage research in general-category
  open-world 3D part segmentation, we also release a benchmark for general
  objects and parts. Project website: https://ziqi-ma.github.io/find3dsite/
paper_ai_summary: A model named Find3D achieves strong performance and generalization in open-world 3D part segmentation using a combination of foundation models and contrastive training without human annotation, outperforming existing methods in speed and accuracy.
paper_ai_keywords: open-world part segmentation point embedding data engine foundation models contrastive training mIoU general-category benchmark