SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided Geometric Linearization
<|reference_start|>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.<|reference_end|>
@article{younes2024sparsecraft:,
    title={SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided
  Geometric Linearization},
    author={Mae Younes, Amine Ouasfi, Adnane Boukhayma},
    journal={arXiv preprint arXiv:2407.14257},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14257},
    primaryClass={cs.CV}
}