3D object reconstruction from 2-3 images using geometry reasoning with SparseNeuS
3D object reconstruction from 2-3 images using geometry reasoning with SparseNeuS
SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse views
arXiv paper abstract https://arxiv.org/abs/2206.05737
arXiv PDF paper https://arxiv.org/pdf/2206.05737.pdf
... introduce SparseNeuS, a novel neural rendering based method for the task of surface reconstruction from multi-view images.
... task ... more difficult when only sparse images ... as input ... Moreover ... inability of generalizing to unseen new scenes
... Contrarily, SparseNeuS can generalize to new scenes and work well with sparse images (as few as 2 or 3).
SparseNeuS adopts signed distance function (SDF) as the surface representation, and learns generalizable priors from image features by introducing geometry encoding volumes for generic surface prediction.
... strategies ... for ... reconstruction, including 1) ... recover the surfaces in a coarse-to-fine manner; 2) a multi-scale color blending scheme ... 3) ... control the inconsistent regions caused by occlusion and noise.
... approach ... outperforms the state-of-the-art methods, but also exhibits good efficiency, generalizability, and flexibility.
Please like and share this post if you enjoyed it using the buttons at the bottom!
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact
Web site with my other posts by category https://morrislee1234.wixsite.com/website
Коментарі