Get 3D scene using multiple priors and regularizers with Lincetto
Get 3D scene using multiple priors and regularizers with Lincetto
Exploiting Multiple Priors for Neural 3D Indoor Reconstruction
arXiv paper abstract https://arxiv.org/abs/2309.07021
arXiv PDF paper https://arxiv.org/pdf/2309.07021.pdf
Neural implicit modeling permits to achieve impressive 3D reconstruction results on small objects, while it exhibits significant limitations in large indoor scenes.
… propose a novel neural implicit modeling method that leverages multiple regularization strategies to achieve better reconstructions of large indoor environments, while relying only on images.
A sparse but accurate depth prior is used to anchor the scene to the initial model.
A dense but less accurate depth prior is also introduced, flexible enough to still let the model diverge from it to improve the estimated geometry.
Then, a novel self-supervised strategy to regularize the estimated surface normals is presented. Finally, a learnable exposure compensation scheme permits to cope with challenging lighting conditions.
… approach produces state-of-the-art 3D reconstructions in challenging indoor scenarios.
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