Multi-view Self-supervised Object Segmentation
Ma Wenxuan1,2; ZhengLiming1,2; Cai Yinghao1; Lu Tao1; Wang Shuo1
2023-10
Conference Name2023 IEEE International Conference on Robotics and Biomimetics
Conference Date2023-10
Conference PlaceSamui, Thailand
Abstract
Robots often operate in open-world environments,  where the capability to generalize to new scenarios is crucial for robotic applications such as navigation and manipulation. In this paper, we propose a novel multi-view self-supervised framework (MVSS) to adapt off-the-shelf segmentation methods in a self-supervised manner by leveraging multi-view consistency. Pixel-level and object-level correspondences are established through unsupervised camera pose estimation and cross-frame object association to learn feature embeddings that the same object are close to each other and embeddings from different objects are separated. Experimental results show that it only needs to observe the RGB-D sequence once without any  annotation, our proposed method is able to adapt existing methods in new scenarios to achieve performance close to that of supervised segmentation methods.
Keyword具身视觉感知 自监督学习 机器人视觉
Indexed ByEI
Language英语
IS Representative Paper
Sub direction classification智能机器人
planning direction of the national heavy laboratory视觉信息处理
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57251
Collection多模态人工智能系统全国重点实验室_智能机器人系统研究
Corresponding AuthorCai Yinghao
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Ma Wenxuan,ZhengLiming,Cai Yinghao,et al. Multi-view Self-supervised Object Segmentation[C],2023.
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