  Indoor scene augmentation has become an emerging topic in the field of
computer vision and graphics with applications in augmented and virtual
reality. However, current state-of-the-art systems using deep neural networks
require large datasets for training. In this paper we introduce GSACNet, a
contextual scene augmentation system that can be trained with limited scene
priors. GSACNet utilizes a novel parametric data augmentation method combined
with a Graph Attention and Siamese network architecture followed by an
Autoencoder network to facilitate training with small datasets. We show the
effectiveness of our proposed system by conducting ablation and comparative
studies with alternative systems on the Matterport3D dataset. Our results
indicate that our scene augmentation outperforms prior art in scene synthesis
with limited scene priors available.
