Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions

• URL: http://arxiv.org/abs/2004.03967v1
• Date: Wed, 8 Apr 2020 12:25:25 GMT
• Title: Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions
• Authors: Johanna Wald, Helisa Dhamo, Nassir Navab, Federico Tombari
• Abstract summary: We focus on scene graphs, a data structure that organizes the entities of a scene in a graph. We propose a learned method that regresses a scene graph from the point cloud of a scene. We show the application of our method in a domain-agnostic retrieval task, where graphs serve as an intermediate representation for 3D-3D and 2D-3D matching.
• Score: 94.17683799712397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene understanding has been of high interest in computer vision. It encompasses not only identifying objects in a scene, but also their relationships within the given context. With this goal, a recent line of works tackles 3D semantic segmentation and scene layout prediction. In our work we focus on scene graphs, a data structure that organizes the entities of a scene in a graph, where objects are nodes and their relationships modeled as edges. We leverage inference on scene graphs as a way to carry out 3D scene understanding, mapping objects and their relationships. In particular, we propose a learned method that regresses a scene graph from the point cloud of a scene. Our novel architecture is based on PointNet and Graph Convolutional Networks (GCN). In addition, we introduce 3DSSG, a semi-automatically generated dataset, that contains semantically rich scene graphs of 3D scenes. We show the application of our method in a domain-agnostic retrieval task, where graphs serve as an intermediate representation for 3D-3D and 2D-3D matching.

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