TY - GEN
T1 - Open-vocabulary Affordance Detection in 3d Point Clouds
AU - Nguyen, Toan
AU - Vu, Minh Nhat
AU - Vuong, An
AU - Nguyen, Dzung
AU - Vo, Thieu
AU - Le, Ngan
AU - Nguyen, Anh
PY - 2023/12/14
Y1 - 2023/12/14
N2 - 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. Our project is available at https://openad2023.github.io.
AB - 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. Our project is available at https://openad2023.github.io.
UR - https://ieeexplore.ieee.org/abstract/document/10341553
U2 - 10.1109/IROS55552.2023.10341553
DO - 10.1109/IROS55552.2023.10341553
M3 - Conference Proceedings with Oral Presentation
T3 - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
SP - 5692
EP - 5698
BT - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
T2 - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Y2 - 1 October 2023 through 5 October 2023
ER -