Incremental 3D Semantic Scene Graph Prediction from RGB Sequences
This addresses the need for efficient scene understanding in robotics or AR/VR, though it is incremental as it builds on existing 3D estimation methods.
The paper tackles the problem of building consistent 3D semantic scene graphs from RGB image sequences in real-time, proposing a framework that outperforms state-of-the-art methods on the 3RScan dataset.
3D semantic scene graphs are a powerful holistic representation as they describe the individual objects and depict the relation between them. They are compact high-level graphs that enable many tasks requiring scene reasoning. In real-world settings, existing 3D estimation methods produce robust predictions that mostly rely on dense inputs. In this work, we propose a real-time framework that incrementally builds a consistent 3D semantic scene graph of a scene given an RGB image sequence. Our method consists of a novel incremental entity estimation pipeline and a scene graph prediction network. The proposed pipeline simultaneously reconstructs a sparse point map and fuses entity estimation from the input images. The proposed network estimates 3D semantic scene graphs with iterative message passing using multi-view and geometric features extracted from the scene entities. Extensive experiments on the 3RScan dataset show the effectiveness of the proposed method in this challenging task, outperforming state-of-the-art approaches.