  We present a novel approach that converts partial and noisy RGB-D scans into
high-quality 3D scene reconstructions by inferring unobserved scene geometry.
Our approach is fully self-supervised and can hence be trained solely on
real-world, incomplete scans. To achieve self-supervision, we remove frames
from a given (incomplete) 3D scan in order to make it even more incomplete;
self-supervision is then formulated by correlating the two levels of
partialness of the same scan while masking out regions that have never been
observed. Through generalization across a large training set, we can then
predict 3D scene completion without ever seeing any 3D scan of entirely
complete geometry. Combined with a new 3D sparse generative neural network
architecture, our method is able to predict highly-detailed surfaces in a
coarse-to-fine hierarchical fashion, generating 3D scenes at 2cm resolution,
more than twice the resolution of existing state-of-the-art methods as well as
outperforming them by a significant margin in reconstruction quality.
