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：可操作性检测是一个具有挑战性的问题，在机器人领域有广泛的应用。传统的可操作性检测方法局限于预定义的可操作标签集合，这可能会限制智能机器人在复杂动态环境中的适应性。在本文中，我们提出了开放词汇可操作性检测（OpenAD）方法，该方法能够在三维点云中检测无限数量的可操作性。通过同时学习可操作性文本和点特征，OpenAD成功利用了可操作性之间的语义关系。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出之前未见过的可操作性。大量实验结果表明，OpenAD在多种可操作性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。我们的项目可在 https://openad2023.github.io 获取。