title: LRM: Large Reconstruction Model for Single Image to 3D
summary: We propose the first Large Reconstruction Model (LRM) that predicts the 3D
  model of an object from a single input image within just 5 seconds. In contrast
  to many previous methods that are trained on small-scale datasets such as
  ShapeNet in a category-specific fashion, LRM adopts a highly scalable
  transformer-based architecture with 500 million learnable parameters to
  directly predict a neural radiance field (NeRF) from the input image. We train
  our model in an end-to-end manner on massive multi-view data containing around
  1 million objects, including both synthetic renderings from Objaverse and real
  captures from MVImgNet. This combination of a high-capacity model and
  large-scale training data empowers our model to be highly generalizable and
  produce high-quality 3D reconstructions from various testing inputs including
  real-world in-the-wild captures and images from generative models. Video demos
  and interactable 3D meshes can be found on this website:
  https://yiconghong.me/LRM/.
paper_authors._id: 654c5a9c08031bb3deb8f326
paper_authors.name: Yicong Hong
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Yicong Hong
paper_authors.type: user
paper_authors.user: YicongHong
paper_authors._id: 654c5a9c08031bb3deb8f327
paper_authors.name: Kai Zhang
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paper_authors.fullname: Kai Zhang
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paper_authors.user: Kai-46
paper_authors._id: 654c5a9c08031bb3deb8f328
paper_authors.name: Jiuxiang Gu
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paper_authors.fullname: Jiuxiang Gu
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paper_authors.user: JoshuaGu
paper_authors._id: 654c5a9c08031bb3deb8f329
paper_authors.name: Sai Bi
paper_authors._id: 654c5a9c08031bb3deb8f32a
paper_authors.name: Yang Zhou
paper_authors._id: 654c5a9c08031bb3deb8f32b
paper_authors.name: Difan Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: Difan Liu
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paper_authors.user: smebliu
paper_authors._id: 654c5a9c08031bb3deb8f32c
paper_authors.name: Feng Liu
paper_authors._id: 654c5a9c08031bb3deb8f32d
paper_authors.name: Kalyan Sunkavalli
paper_authors._id: 654c5a9c08031bb3deb8f32e
paper_authors.name: Trung Bui
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-09T09:48:12.552Z
paper_authors._id: 6036eb46413a78f892e733a0
paper_authors.avatarUrl: /avatars/b15925f89631323c176ecbd0eab99feb.svg
paper_authors.fullname: Trung Bui
paper_authors.type: user
paper_authors.user: Trung
paper_authors._id: 654c5a9c08031bb3deb8f32f
paper_authors.name: Hao Tan
paper_title: LRM: Large Reconstruction Model for Single Image to 3D
paper_summary: We propose the first Large Reconstruction Model (LRM) that predicts the 3D
  model of an object from a single input image within just 5 seconds. In contrast
  to many previous methods that are trained on small-scale datasets such as
  ShapeNet in a category-specific fashion, LRM adopts a highly scalable
  transformer-based architecture with 500 million learnable parameters to
  directly predict a neural radiance field (NeRF) from the input image. We train
  our model in an end-to-end manner on massive multi-view data containing around
  1 million objects, including both synthetic renderings from Objaverse and real
  captures from MVImgNet. This combination of a high-capacity model and
  large-scale training data empowers our model to be highly generalizable and
  produce high-quality 3D reconstructions from various testing inputs including
  real-world in-the-wild captures and images from generative models. Video demos
  and interactable 3D meshes can be found on this website:
  https://yiconghong.me/LRM/.
paper_ai_keywords: Large Reconstruction Model (LRM) transformer-based architecture neural radiance field (NeRF) end-to-end manner massive multi-view data synthetic renderings Objaverse real captures MVImgNet high-capacity model high-quality 3D reconstructions in-the-wild captures generative models video demos interactable 3D meshes