title: 3D-LFM: Lifting Foundation Model
summary: The lifting of 3D structure and camera from 2D landmarks is at the
  cornerstone of the entire discipline of computer vision. Traditional methods
  have been confined to specific rigid objects, such as those in
  Perspective-n-Point (PnP) problems, but deep learning has expanded our
  capability to reconstruct a wide range of object classes (e.g. C3PDO and PAUL)
  with resilience to noise, occlusions, and perspective distortions. All these
  techniques, however, have been limited by the fundamental need to establish
  correspondences across the 3D training data -- significantly limiting their
  utility to applications where one has an abundance of "in-correspondence" 3D
  data. Our approach harnesses the inherent permutation equivariance of
  transformers to manage varying number of points per 3D data instance,
  withstands occlusions, and generalizes to unseen categories. We demonstrate
  state of the art performance across 2D-3D lifting task benchmarks. Since our
  approach can be trained across such a broad class of structures we refer to it
  simply as a 3D Lifting Foundation Model (3D-LFM) -- the first of its kind.
paper_authors._id: 65826c3a914a31aa81333cad
paper_authors.name: Mosam Dabhi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-20T09:33:50.470Z
paper_authors._id: 62ce87c0ae10cf826e6e18e8
paper_authors.avatarUrl: /avatars/33a1087d31b55c630d4bb17359798fac.svg
paper_authors.fullname: Mosam Dabhi
paper_authors.type: user
paper_authors.user: mosamdabhi
paper_authors._id: 65826c3a914a31aa81333cae
paper_authors.name: Laszlo A. Jeni
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-20T09:34:10.741Z
paper_authors._id: 64bbd2f865b648b2df859bc6
paper_authors.avatarUrl: /avatars/698d98d8b42c478a3d7584b93c6dbcc2.svg
paper_authors.fullname: Laszlo Jeni
paper_authors.type: user
paper_authors.user: laszlo-ai
paper_authors._id: 65826c3a914a31aa81333caf
paper_authors.name: Simon Lucey
paper_title: 3D-LFM: Lifting Foundation Model
paper_summary: The lifting of 3D structure and camera from 2D landmarks is at the
  cornerstone of the entire discipline of computer vision. Traditional methods
  have been confined to specific rigid objects, such as those in
  Perspective-n-Point (PnP) problems, but deep learning has expanded our
  capability to reconstruct a wide range of object classes (e.g. C3PDO and PAUL)
  with resilience to noise, occlusions, and perspective distortions. All these
  techniques, however, have been limited by the fundamental need to establish
  correspondences across the 3D training data -- significantly limiting their
  utility to applications where one has an abundance of "in-correspondence" 3D
  data. Our approach harnesses the inherent permutation equivariance of
  transformers to manage varying number of points per 3D data instance,
  withstands occlusions, and generalizes to unseen categories. We demonstrate
  state of the art performance across 2D-3D lifting task benchmarks. Since our
  approach can be trained across such a broad class of structures we refer to it
  simply as a 3D Lifting Foundation Model (3D-LFM) -- the first of its kind.
paper_ai_summary: A 3D Lifting Foundation Model (3D-LFM) uses permutation-equivariant transformers to perform 2D-3D lifting for various object categories, achieving state-of-the-art performance.
paper_ai_keywords: permutation equivariance transformers 3D Lifting Foundation Model 3D-LFM 2D-3D lifting