  Scene graphs are a compact and explicit representation successfully used in a
variety of 2D scene understanding tasks. This work proposes a method to
incrementally build up semantic scene graphs from a 3D environment given a
sequence of RGB-D frames. To this end, we aggregate PointNet features from
primitive scene components by means of a graph neural network. We also propose
a novel attention mechanism well suited for partial and missing graph data
present in such an incremental reconstruction scenario. Although our proposed
method is designed to run on submaps of the scene, we show it also transfers to
entire 3D scenes. Experiments show that our approach outperforms 3D scene graph
prediction methods by a large margin and its accuracy is on par with other 3D
semantic and panoptic segmentation methods while running at 35 Hz.
