3D点云中的开放词汇可供性检测
Open-Vocabulary Affordance Detection in 3D Point Clouds
AI总结：
针对传统可供性检测受预定义标签限制的问题，提出可在3D点云中实现零样本开放词汇可供性检测的OpenAD方法，性能优于基线且推理速度快，适用于真实机器人场景。
摘要：
可供性检测是一个具有挑战性的问题，在机器人领域有广泛应用。传统可供性检测方法受限于预定义的可供性标签集合，可能会限制智能机器人在复杂动态环境中的适应性。本文提出了开放词汇可供性检测（OpenAD）方法，该方法能够在3D点云中检测数量不受限的可供性。通过同时学习可供性文本与点特征，OpenAD成功挖掘了可供性之间的语义关系。因此，本文提出的方法可实现零样本检测，无需任何标注样本就能检测此前未见过的可供性。大量实验结果表明，OpenAD在多种可供性检测设置下均能有效工作，且大幅优于其他基线方法。此外，我们还验证了所提OpenAD在真实世界机器人应用中的实用性，其推理速度很快（约100毫秒）。我们的项目可在https://openad2023.github.io获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
