However, deploying a room-layout model using 360-images in a new target domain remains a challenging problem.

A large part of the current success of deep learning lies in the effectiveness of data - more precisely: labelled data. Yet, labelling a dataset with human annotation continues to carry high costs, especially for videos. While in the image domain, recent methods have allowed to generate meaningful (pseudo-) labels for unlabelled datasets without supervision, this development is missing for the video domain where learning feature representations is the current focus.