SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
This work addresses the need for efficient and accurate 3D scene understanding for robotics and AR/VR applications, though it is incremental in nature.
The paper tackles the problem of incrementally building 3D scene graphs from RGB-D sequences, achieving state-of-the-art performance with a 35 Hz runtime and accuracy comparable to 3D semantic and panoptic segmentation methods.
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.