Exploiting Multiple Priors for Neural 3D Indoor Reconstruction
Federico Lincetto Gianluca Agresti Mattia Rossi Pietro Zanuttigh
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.