SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided
  Geometric Linearization
We present a novel approach for recovering 3D shape and view dependent
appearance from a few colored images, enabling efficient 3D reconstruction and
novel view synthesis. Our method learns an implicit neural representation in
the form of a Signed Distance Function (SDF) and a radiance field. The model is
trained progressively through ray marching enabled volumetric rendering, and
regularized with learning-free multi-view stereo (MVS) cues. Key to our
contribution is a novel implicit neural shape function learning strategy that
encourages our SDF field to be as linear as possible near the level-set, hence
robustifying the training against noise emanating from the supervision and
regularization signals. Without using any pretrained priors, our method, called
SparseCraft, achieves state-of-the-art performances both in novel-view
synthesis and reconstruction from sparse views in standard benchmarks, while
requiring less than 10 minutes for training.
669dbc0c52cf1c619d135d82
Mae Younes
claimed_verified
2024-07-26T07:40:57.775Z
66a258e621c857b355dadf44
/avatars/4e29b228bbe244ec4adecafbd8bfc53b.svg
Mae Younes
user
maeyounes
669dbc0c52cf1c619d135d83
Amine Ouasfi
669dbc0c52cf1c619d135d84
Adnane Boukhayma
SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided
  Geometric Linearization
We present a novel approach for recovering 3D shape and view dependent
appearance from a few colored images, enabling efficient 3D reconstruction and
novel view synthesis. Our method learns an implicit neural representation in
the form of a Signed Distance Function (SDF) and a radiance field. The model is
trained progressively through ray marching enabled volumetric rendering, and
regularized with learning-free multi-view stereo (MVS) cues. Key to our
contribution is a novel implicit neural shape function learning strategy that
encourages our SDF field to be as linear as possible near the level-set, hence
robustifying the training against noise emanating from the supervision and
regularization signals. Without using any pretrained priors, our method, called
SparseCraft, achieves state-of-the-art performances both in novel-view
synthesis and reconstruction from sparse views in standard benchmarks, while
requiring less than 10 minutes for training.
A novel method, SparseCraft, uses implicit neural representations like SDF and radiance field for efficient 3D reconstruction and view synthesis, trained with ray marching and MVS cues.
Signed Distance Function (SDF) radiance field ray marching volumetric rendering multi-view stereo (MVS) implicit neural shape function