  Spatial computing experiences are constrained by the real-world surroundings
of the user. In such experiences, augmenting virtual objects to existing scenes
require a contextual approach, where geometrical conflicts are avoided, and
functional and plausible relationships to other objects are maintained in the
target environment. Yet, due to the complexity and diversity of user
environments, automatically calculating ideal positions of virtual content that
is adaptive to the context of the scene is considered a challenging task.
Motivated by this problem, in this paper we introduce SceneGen, a generative
contextual augmentation framework that predicts virtual object positions and
orientations within existing scenes. SceneGen takes a semantically segmented
scene as input, and outputs positional and orientational probability maps for
placing virtual content. We formulate a novel spatial Scene Graph
representation, which encapsulates explicit topological properties between
objects, object groups, and the room. We believe providing explicit and
intuitive features plays an important role in informative content creation and
user interaction of spatial computing settings, a quality that is not captured
in implicit models. We use kernel density estimation (KDE) to build a
multivariate conditional knowledge model trained using prior spatial Scene
Graphs extracted from real-world 3D scanned data. To further capture
orientational properties, we develop a fast pose annotation tool to extend
current real-world datasets with orientational labels. Finally, to demonstrate
our system in action, we develop an Augmented Reality application, in which
objects can be contextually augmented in real-time.
