Nguyen, Toan, Vu, Minh Nhat, Vuong, An, Nguyen, Dzung, Vo, Thieu, Le, Ngan and Nguyen, Anh 
ORCID: 0000-0002-1449-211X
(2023)
Open-Vocabulary Affordance Detection in 3D Point Clouds.
In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023-10-1 - 2023-10-5.
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Abstract
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.
| Item Type: | Conference or Workshop Item (Unspecified) |
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| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 15 Dec 2023 11:31 |
| Last Modified: | 17 Mar 2024 19:09 |
| DOI: | 10.1109/iros55552.2023.10341553 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3177437 |
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