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 be able to 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 (~100ms). Our project is available at https://openad2023.github.io.

翻译：功能检测是一个具有挑战性的问题，在机器人领域有着广泛的应用。传统的功能检测方法局限于预定义的功能标签集，这可能会限制智能机器人在复杂动态环境中的适应性。本文提出了一种开放词汇功能检测方法OpenAD，该方法能够在3D点云中检测任意数量的功能。通过同时学习功能文本和点特征，OpenAD成功利用了功能之间的语义关系。因此，我们提出的方法能够实现零样本检测，无需任何标注示例即可检测先前未见过的功能。大量实验结果表明，OpenAD在多种功能检测设置下均能有效工作，且性能大幅优于其他基线方法。此外，我们通过快速的推理速度（约100毫秒）证明了所提出的OpenAD在实际机器人应用中的实用性。项目网址为https://openad2023.github.io。