Exploiting Multiple Priors for Neural 3D Indoor Reconstruction
Federico Lincetto (University of Padova),* Gianluca Agresti (Sony Europe B.V.), Mattia Rossi (SONY Europe B.V.), Pietro Zanuttigh (University of Padova)
The 34th British Machine Vision Conference
Abstract
Neural implicit modeling permits to achieve impressive 3D reconstruction results on small objects, while it exhibits significant limitations in large indoor scenes. In this work, we 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. Experimental results show that our approach produces state-of-the-art 3D reconstructions in challenging indoor scenarios.Video
Citation
@inproceedings{Lincetto_2023_BMVC,
author = {Federico Lincetto and Gianluca Agresti and Mattia Rossi and Pietro Zanuttigh},
title = {Exploiting Multiple Priors for Neural 3D Indoor Reconstruction},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0571.pdf}
}
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