Affordance detection is a challenging problem with a wide variety of robotic applications. Traditional affordance detection methods are limited to a predefined set of affordance labels, hence potentially restricting the adaptability of intelligent robots in complex and dynamic environments. In this paper, we present the Open-Vocabulary Affordance Detection (OpenAD) method, which is capable of detecting an unbounded number of affordances in 3D point clouds. By simultaneously learning the affordance text and the point feature, OpenAD successfully exploits the semantic relationships between affordances. Therefore, our proposed method enables zero-shot detection and can detect previously unseen affordances without a single annotation example. Intensive experimental results show that OpenAD works effectively on a wide range of affordance detection setups and outperforms other baselines by a large margin. Additionally, we demonstrate the practicality of the proposed OpenAD in real-world robotic applications with a fast inference speed (~100 ms).

本文介绍了一种名为Open-Vocabulary Affordance Detection （OpenAD）的方法，能够在三维点云中检测出大量未被预定义的功能，无需单一标注实例即可实现零-shot检测，并成功利用功能语义关系提高检测效率，在多种场景中均表现出优异的性能，具有实际应用价值。

3D 点云中的开放词汇能力检测