  In this paper, we present Retargetable AR, a novel AR framework that yields
an AR experience that is aware of scene contexts set in various real
environments, achieving natural interaction between the virtual and real
worlds. To this end, we characterize scene contexts with relationships among
objects in 3D space, not with coordinates transformations. A context assumed by
an AR content and a context formed by a real environment where users experience
AR are represented as abstract graph representations, i.e. scene graphs. From
RGB-D streams, our framework generates a volumetric map in which geometric and
semantic information of a scene are integrated. Moreover, using the semantic
map, we abstract scene objects as oriented bounding boxes and estimate their
orientations. With such a scene representation, our framework constructs, in an
online fashion, a 3D scene graph characterizing the context of a real
environment for AR. The correspondence between the constructed graph and an AR
scene graph denoting the context of AR content provides a semantically
registered content arrangement, which facilitates natural interaction between
the virtual and real worlds. We performed extensive evaluations on our
prototype system through quantitative evaluation of the performance of the
oriented bounding box estimation, subjective evaluation of the AR content
arrangement based on constructed 3D scene graphs, and an online AR
demonstration. The results of these evaluations showed the effectiveness of our
framework, demonstrating that it can provide a context-aware AR experience in a
variety of real scenes.
