Abstract: In this paper, we propose an effective global relation learning algorithm to
recommend an appropriate location of a building unit for in-game customization
of residential home complex. Given a construction layout, we propose a visual
context-aware graph generation network that learns the implicit global
relations among the scene components and infers the location of a new building
unit. The proposed network takes as input the scene graph and the corresponding
top-view depth image. It provides the location recommendations for a
newly-added building units by learning an auto-regressive edge distribution
conditioned on existing scenes. We also introduce a global graph-image matching
loss to enhance the awareness of essential geometry semantics of the site.
Qualitative and quantitative experiments demonstrate that the recommended
location well reflects the implicit spatial rules of components in the
residential estates, and it is instructive and practical to locate the building
units in the 3D scene of the complex construction.