3D-LFM: Lifting Foundation Model
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.
65826c3a914a31aa81333cad
Mosam Dabhi
admin_assigned
2023-12-20T09:33:50.470Z
62ce87c0ae10cf826e6e18e8
/avatars/33a1087d31b55c630d4bb17359798fac.svg
Mosam Dabhi
user
mosamdabhi
65826c3a914a31aa81333cae
Laszlo A. Jeni
admin_assigned
2023-12-20T09:34:10.741Z
64bbd2f865b648b2df859bc6
/avatars/698d98d8b42c478a3d7584b93c6dbcc2.svg
Laszlo Jeni
user
laszlo-ai
65826c3a914a31aa81333caf
Simon Lucey
3D-LFM: Lifting Foundation Model
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.
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.
permutation equivariance transformers 3D Lifting Foundation Model 3D-LFM 2D-3D lifting