title: GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction
    and Generation
summary: We introduce GRM, a large-scale reconstructor capable of recovering a 3D
  asset from sparse-view images in around 0.1s. GRM is a feed-forward
  transformer-based model that efficiently incorporates multi-view information to
  translate the input pixels into pixel-aligned Gaussians, which are unprojected
  to create a set of densely distributed 3D Gaussians representing a scene.
  Together, our transformer architecture and the use of 3D Gaussians unlock a
  scalable and efficient reconstruction framework. Extensive experimental results
  demonstrate the superiority of our method over alternatives regarding both
  reconstruction quality and efficiency. We also showcase the potential of GRM in
  generative tasks, i.e., text-to-3D and image-to-3D, by integrating it with
  existing multi-view diffusion models. Our project website is at:
  https://justimyhxu.github.io/projects/grm/.
paper_authors._id: 65fd03a2023aaa2beed19999
paper_authors.name: Yinghao Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-22T09:48:36.739Z
paper_authors._id: 6555da9adcf410fd0753569c
paper_authors.avatarUrl: /avatars/ac58a796bb54f334fdb475a4b75c4d27.svg
paper_authors.fullname: Yinghao Xu
paper_authors.type: user
paper_authors.user: justimyhxu
paper_authors._id: 65fd03a2023aaa2beed1999a
paper_authors.name: Zifan Shi
paper_authors._id: 65fd03a2023aaa2beed1999b
paper_authors.name: Wang Yifan
paper_authors._id: 65fd03a2023aaa2beed1999c
paper_authors.name: Hansheng Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-22T09:50:43.634Z
paper_authors._id: 638067fcb334960c987fbeda
paper_authors.avatarUrl: /avatars/87f1eaaf6b3a9c0d47d6f406261ccc18.svg
paper_authors.fullname: Hansheng Chen
paper_authors.type: user
paper_authors.user: Lakonik
paper_authors._id: 65fd03a2023aaa2beed1999d
paper_authors.name: Ceyuan Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-22T09:58:37.119Z
paper_authors._id: 64accf3fc1da7c4dbc69392d
paper_authors.avatarUrl: /avatars/ec8ad2498f8a49a7a00c77a7f70d34bb.svg
paper_authors.fullname: Ceyuan
paper_authors.type: user
paper_authors.user: Ceyuan
paper_authors._id: 65fd03a2023aaa2beed1999e
paper_authors.name: Sida Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-22T09:58:43.680Z
paper_authors._id: 62986ca2b58e71e2ac9b8f01
paper_authors.avatarUrl: /avatars/83944db5f3dbb6f47c47c46fb2cb2849.svg
paper_authors.fullname: Sida Peng
paper_authors.type: user
paper_authors.user: pengsida
paper_authors._id: 65fd03a2023aaa2beed1999f
paper_authors.name: Yujun Shen
paper_authors._id: 65fd03a2023aaa2beed199a0
paper_authors.name: Gordon Wetzstein
paper_title: GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction
    and Generation
paper_summary: We introduce GRM, a large-scale reconstructor capable of recovering a 3D
  asset from sparse-view images in around 0.1s. GRM is a feed-forward
  transformer-based model that efficiently incorporates multi-view information to
  translate the input pixels into pixel-aligned Gaussians, which are unprojected
  to create a set of densely distributed 3D Gaussians representing a scene.
  Together, our transformer architecture and the use of 3D Gaussians unlock a
  scalable and efficient reconstruction framework. Extensive experimental results
  demonstrate the superiority of our method over alternatives regarding both
  reconstruction quality and efficiency. We also showcase the potential of GRM in
  generative tasks, i.e., text-to-3D and image-to-3D, by integrating it with
  existing multi-view diffusion models. Our project website is at:
  https://justimyhxu.github.io/projects/grm/.
paper_ai_summary: GRM, a transformer-based model, efficiently reconstructs 3D assets from sparse images using pixel-aligned Gaussians and achieves superior quality and speed compared to alternatives.
paper_ai_keywords: transformer-based model 3D Gaussians reconstruction quality efficiency text-to-3D image-to-3D multi-view diffusion models