SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided Geometric Linearization
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929427575734272 |
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| author | Younes, Mae Ouasfi, Amine Boukhayma, Adnane |
| author_facet | Younes, Mae Ouasfi, Amine Boukhayma, Adnane |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14257 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided Geometric Linearization Younes, Mae Ouasfi, Amine Boukhayma, Adnane Computer Vision and Pattern Recognition 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. |
| title | SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided Geometric Linearization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.14257 |