  Leveraging 3D semantics for direct 3D reconstruction has a great potential
yet unleashed. For instance, by assuming that walls are vertical, and a floor
is planar and horizontal, we can correct distorted room shapes and eliminate
local artifacts such as holes, pits, and hills. In this paper, we propose FAWN,
a modification of truncated signed distance function (TSDF) reconstruction
methods, which considers scene structure by detecting walls and floor in a
scene, and penalizing the corresponding surface normals for deviating from the
horizontal and vertical directions. Implemented as a 3D sparse convolutional
module, FAWN can be incorporated into any trainable pipeline that predicts
TSDF. Since FAWN requires 3D semantics only for training, no additional
limitations on further use are imposed. We demonstrate, that FAWN-modified
methods use semantics more effectively, than existing semantic-based
approaches. Besides, we apply our modification to state-of-the-art TSDF
reconstruction methods, and demonstrate a quality gain in SCANNET, ICL-NUIM,
TUM RGB-D, and 7SCENES benchmarks.
