This paper proposes an approach to build 3D scene graphs in arbitrary (indoor
and outdoor) environments. Such extension is challenging; the hierarchy of
concepts that describe an outdoor environment is more complex than for indoors,
and manually defining such hierarchy is time-consuming and does not scale.
Furthermore, the lack of training data prevents the straightforward application
of learning-based tools used in indoor settings. To address these challenges,
we propose two novel extensions. First, we develop methods to build a spatial
ontology defining concepts and relations relevant for indoor and outdoor robot
operation. In particular, we use a Large Language Model (LLM) to build such an
ontology, thus largely reducing the amount of manual effort required. Second,
we leverage the spatial ontology for 3D scene graph construction using Logic
Tensor Networks (LTN) to add logical rules, or axioms (e.g., "a beach contains
sand"), which provide additional supervisory signals at training time thus
reducing the need for labelled data, providing better predictions, and even
allowing predicting concepts unseen at training time. We test our approach in a
variety of datasets, including indoor, rural, and coastal environments, and
show that it leads to a significant increase in the quality of the 3D scene
graph generation with sparsely annotated data.

该论文提出了一种在任意环境（室内和室外）中构建 3D 场景图的方法。为了解决室内和室外环境的概念层次复杂性和训练数据不足的挑战，提出了构建空间本体和使用逻辑张量网络构建 3D 场景图的两个新扩展。通过使用大型语言模型构建空间本体，从而大大减少手动工作量；并利用逻辑张量网络添加逻辑规则或公理，提供附加的监督信号，减少标记数据的需求，并在训练时允许预测未见过的概念。在多个数据集上测试表明，该方法显著提高了使用稀疏注释数据生成 3D 场景图的质量。