  We present a novel neural surface reconstruction method called NeuralRoom for
reconstructing room-sized indoor scenes directly from a set of 2D images.
Recently, implicit neural representations have become a promising way to
reconstruct surfaces from multiview images due to their high-quality results
and simplicity. However, implicit neural representations usually cannot
reconstruct indoor scenes well because they suffer severe shape-radiance
ambiguity. We assume that the indoor scene consists of texture-rich and flat
texture-less regions. In texture-rich regions, the multiview stereo can obtain
accurate results. In the flat area, normal estimation networks usually obtain a
good normal estimation. Based on the above observations, we reduce the possible
spatial variation range of implicit neural surfaces by reliable geometric
priors to alleviate shape-radiance ambiguity. Specifically, we use multiview
stereo results to limit the NeuralRoom optimization space and then use reliable
geometric priors to guide NeuralRoom training. Then the NeuralRoom would
produce a neural scene representation that can render an image consistent with
the input training images. In addition, we propose a smoothing method called
perturbation-residual restrictions to improve the accuracy and completeness of
the flat region, which assumes that the sampling points in a local surface
should have the same normal and similar distance to the observation center.
Experiments on the ScanNet dataset show that our method can reconstruct the
texture-less area of indoor scenes while maintaining the accuracy of detail. We
also apply NeuralRoom to more advanced multiview reconstruction algorithms and
significantly improve their reconstruction quality.
