Related papers: Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles

Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles

URL: http://arxiv.org/abs/2011.13588v1
Date: Fri, 27 Nov 2020 07:33:11 GMT
Title: Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles
Authors: Yafu Tian, Alexander Carballo, Ruifeng Li and Kazuya Takeda
Abstract summary: We propose road scene graph,a special scene-graph for intelligent vehicles. It 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.
Score: 72.04891523115535
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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