  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.
