  This paper presents a neural network based semantic plane detection method
utilizing polygon representations. The method can for example be used to solve
room layout estimations tasks. The method is built on, combines and further
develops several different modules from previous research. The network takes an
RGB image and estimates a wireframe as well as a feature space using an
hourglass backbone. From these, line and junction features are sampled. The
lines and junctions are then represented as an undirected graph, from which
polygon representations of the sought planes are obtained. Two different
methods for this last step are investigated, where the most promising method is
built on a heterogeneous graph transformer. The final output is in all cases a
projection of the semantic planes in 2D. The methods are evaluated on the
Structured 3D dataset and we investigate the performance both using sampled and
estimated wireframes. The experiments show the potential of the graph-based
method by outperforming state of the art methods in Room Layout estimation in
the 2D metrics using synthetic wireframe detections.
