  Inferring the information of 3D layout from a single equirectangular panorama
is crucial for numerous applications of virtual reality or robotics (e.g.,
scene understanding and navigation). To achieve this, several datasets are
collected for the task of 360 layout estimation. To facilitate the learning
algorithms for autonomous systems in indoor scenarios, we consider the
Matterport3D dataset with their originally provided depth map ground truths and
further release our annotations for layout ground truths from a subset of
Matterport3D. As Matterport3D contains accurate depth ground truths from
time-of-flight (ToF) sensors, our dataset provides both the layout and depth
information, which enables the opportunity to explore the environment by
integrating both cues.
