  We leverage unsupervised learning of depth, egomotion, and camera intrinsics
to improve the performance of single-image semantic segmentation, by enforcing
3D-geometric and temporal consistency of segmentation masks across video
frames. The predicted depth, egomotion, and camera intrinsics are used to
provide an additional supervision signal to the segmentation model,
significantly enhancing its quality, or, alternatively, reducing the number of
labels the segmentation model needs. Our experiments were performed on the
ScanNet dataset.
