论文标题
探索3D场景图生成的上下文信息
Explore Contextual Information for 3D Scene Graph Generation
论文作者
论文摘要
3D场景图（SGG）对计算机视觉引起了极大的兴趣。尽管3D SGG在粗分类和单个关系标签上的准确性逐渐改善，但现有作品的性能仍然不适合细粒度和多标签情况。在本文中，我们提出了一个框架，该框架完全探索了3D SGG任务的上下文信息，该框架试图同时满足细粒实体类，多个关系标签和高精度的要求。我们提出的方法由图形提取模块和图形上下文推理模块组成，从而实现了适当的信息还原特征提取，结构化组织和分层推断。我们的方法比3DSSG数据集上的先前方法实现了优越或竞争性的性能，尤其是在关系预测子任务上。
3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this paper, we propose a framework fully exploring contextual information for the 3D SGG task, which attempts to satisfy the requirements of fine-grained entity class, multiple relation labels, and high accuracy simultaneously. Our proposed approach is composed of a Graph Feature Extraction module and a Graph Contextual Reasoning module, achieving appropriate information-redundancy feature extraction, structured organization, and hierarchical inferring. Our approach achieves superior or competitive performance over previous methods on the 3DSSG dataset, especially on the relationship prediction sub-task.
