title: SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided
    Geometric Linearization
summary: 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.
paper_authors._id: 669dbc0c52cf1c619d135d82
paper_authors.name: Mae Younes
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-26T07:40:57.775Z
paper_authors._id: 66a258e621c857b355dadf44
paper_authors.avatarUrl: /avatars/4e29b228bbe244ec4adecafbd8bfc53b.svg
paper_authors.fullname: Mae Younes
paper_authors.type: user
paper_authors.user: maeyounes
paper_authors._id: 669dbc0c52cf1c619d135d83
paper_authors.name: Amine Ouasfi
paper_authors._id: 669dbc0c52cf1c619d135d84
paper_authors.name: Adnane Boukhayma
paper_title: SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided
    Geometric Linearization
paper_summary: 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.
paper_ai_keywords: implicit neural representation Signed Distance Function (SDF) radiance field ray marching volumetric rendering multi-view stereo (MVS) implicit neuralf field level-set SparseCraft