论文标题
道路场景图：智能车辆的基于语义图的场景表示数据集
Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles
论文作者
论文摘要
丰富的语义信息提取在下一代智能车辆上起着至关重要的作用。当前，有大量的研究重点是基本应用，例如6D姿势检测，道路场景语义细分等，这为我们提供了一个很好的机会，可以考虑如何组织和利用这些数据。 在本文中，我们提出了道路场景图，这是一个专门用于智能车辆的场景图。与经典数据表示不同，该图不仅提供了对象建议，还提供了它们的配对关系。通过在拓扑图中组织它们，这些数据可以解释，完全连接，并且可以通过GCN（图卷积网络）轻松处理。在这里，我们使用道路场景图数据集在道路上应用场景图，包括基本图预测模型。这项工作还包括使用拟议模型的实验评估。
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.
