The concept of 3D scene graphs is increasingly recognized as a powerful semantic and hierarchical representation of the environment. Current approaches often address this at a coarse, object-level resolution. In contrast, our goal is to develop a representation that enables robots to directly interact with their environment by identifying both the location of functional interactive elements and how these can be used. To achieve this, we focus on detecting and storing objects at a finer resolution, focusing on affordance-relevant parts. The primary challenge lies in the scarcity of data that extends beyond instance-level detection and the inherent difficulty of capturing detailed object features using robotic sensors. We leverage currently available 3D resources to generate 2D data and train a detector, which is then used to augment the standard 3D scene graph generation pipeline. Through our experiments, we demonstrate that our approach achieves functional element segmentation comparable to state-of-the-art 3D models and that our augmentation enables task-driven affordance grounding with higher accuracy than the current solutions.

本研究解决了当前3D场景图在功能交互方面的不足，提升了机器人与环境的直接交互能力。我们提出了一种新方法，通过细粒度的物体检测和存储，提高了功能性元素的分割精度，相较于现有方法具有更高的任务驱动可用性。实验结果表明，该方法在功能元素分割和准确性上均达到业界领先水平。

FunGraph：面向功能的3D场景图以实现基于语言的场景交互