Open-vocabulary Affordance Detection in 3d Point Clouds

Toan Nguyen, Minh Nhat Vu, An Vuong, Dzung Nguyen, Thieu Vo, Ngan Le, Anh Nguyen

Publikation: Beitrag in Buch oder TagungsbandVortrag mit Beitrag in TagungsbandBegutachtung

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.
OriginalspracheEnglisch
Titel2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Seiten5692-5698
DOIs
PublikationsstatusVeröffentlicht - 14 Dez. 2023
Veranstaltung2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Detroit, USA/Vereinigte Staaten
Dauer: 1 Okt. 20235 Okt. 2023

Publikationsreihe

Name2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Konferenz

Konferenz2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Land/GebietUSA/Vereinigte Staaten
StadtDetroit
Zeitraum1/10/235/10/23

Research Field

  • Complex Dynamical Systems

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