Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles
This work addresses the problem of organizing and exploiting rich semantic information for next-generation intelligent vehicles by providing a structured, graph-based scene representation.
This paper introduces the Road Scene Graph, a dataset designed for intelligent vehicles that represents road scenes not only with object proposals but also with their pairwise relationships. This topological graph structure aims to make scene data explainable, fully-connected, and easily processable by Graph Convolutional Networks (GCNs).
Rich semantic information extraction plays a vital role on next-generation intelligent vehicles. Currently there is great amount of research focusing on fundamental applications such as 6D pose detection, road scene semantic segmentation, etc. And this provides us a great opportunity to think about how shall these data be organized and exploited. In this paper we propose road scene graph,a special scene-graph for intelligent vehicles. Different to classical data representation, this graph provides not only object proposals but also their pair-wise relationships. By organizing them in a topological graph, these data are explainable, fully-connected, and could be easily processed by GCNs (Graph Convolutional Networks). Here we apply scene graph on roads using our Road Scene Graph dataset, including the basic graph prediction model. This work also includes experimental evaluations using the proposed model.