  Reconstructing 3D indoor scenes from 2D images is an important task in many
computer vision and graphics applications. A main challenge in this task is
that large texture-less areas in typical indoor scenes make existing methods
struggle to produce satisfactory reconstruction results. We propose a new
method, named NeuRIS, for high quality reconstruction of indoor scenes. The key
idea of NeuRIS is to integrate estimated normal of indoor scenes as a prior in
a neural rendering framework for reconstructing large texture-less shapes and,
importantly, to do this in an adaptive manner to also enable the reconstruction
of irregular shapes with fine details. Specifically, we evaluate the
faithfulness of the normal priors on-the-fly by checking the multi-view
consistency of reconstruction during the optimization process. Only the normal
priors accepted as faithful will be utilized for 3D reconstruction, which
typically happens in the regions of smooth shapes possibly with weak texture.
However, for those regions with small objects or thin structures, for which the
normal priors are usually unreliable, we will only rely on visual features of
the input images, since such regions typically contain relatively rich visual
features (e.g., shade changes and boundary contours). Extensive experiments
show that NeuRIS significantly outperforms the state-of-the-art methods in
terms of reconstruction quality.
