  State-of-the-art navigation methods leverage a spatial memory to generalize
to new environments, but their occupancy maps are limited to capturing the
geometric structures directly observed by the agent. We propose occupancy
anticipation, where the agent uses its egocentric RGB-D observations to infer
the occupancy state beyond the visible regions. In doing so, the agent builds
its spatial awareness more rapidly, which facilitates efficient exploration and
navigation in 3D environments. By exploiting context in both the egocentric
views and top-down maps our model successfully anticipates a broader map of the
environment, with performance significantly better than strong baselines.
Furthermore, when deployed for the sequential decision-making tasks of
exploration and navigation, our model outperforms state-of-the-art methods on
the Gibson and Matterport3D datasets. Our approach is the winning entry in the
2020 Habitat PointNav Challenge. Project page:
http://vision.cs.utexas.edu/projects/occupancy_anticipation/
