  Analysis of indoor spaces requires topological information. In this paper, we
propose to extract topological information from room attributes using what we
call Iterative and adaptive graph Topology Learning (ITL). ITL progressively
predicts multiple relations between rooms; at each iteration, it improves node
embeddings, which in turn facilitates generation of a better topological graph
structure. This notion of iterative improvement of node embeddings and
topological graph structure is in the same spirit as \cite{chen2020iterative}.
However, while \cite{chen2020iterative} computes the adjacency matrix based on
node similarity, we learn the graph metric using a relational decoder to
extract room correlations. Experiments using a new challenging indoor dataset
validate our proposed method. Qualitative and quantitative evaluation for
layout topology prediction and floorplan generation applications also
demonstrate the effectiveness of ITL.
