  Spherical cameras capture scenes in a holistic manner and have been used for
room layout estimation. Recently, with the availability of appropriate
datasets, there has also been progress in depth estimation from a single
omnidirectional image. While these two tasks are complementary, few works have
been able to explore them in parallel to advance indoor geometric perception,
and those that have done so either relied on synthetic data, or used small
scale datasets, as few options are available that include both layout
annotations and dense depth maps in real scenes. This is partly due to the
necessity of manual annotations for room layouts. In this work, we move beyond
this limitation and generate a 360 geometric vision (360V) dataset that
includes multiple modalities, multi-view stereo data and automatically
generated weak layout cues. We also explore an explicit coupling between the
two tasks to integrate them into a singleshot trained model. We rely on
depth-based layout reconstruction and layout-based depth attention,
demonstrating increased performance across both tasks. By using single 360
cameras to scan rooms, the opportunity for facile and quick building-scale 3D
scanning arises.
