Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Active Semantic Labeling of Street View Point Clouds | |
Zhou Y(周洋)1,2![]() ![]() ![]() | |
2019-07 | |
Conference Name | IEEE International Conference on Multimedia and Expo 2019 |
Conference Date | 2019-7-8~12 |
Conference Place | Shanghai, China |
Abstract | Semantic 3D models have shown their importance in many fields such as autonomous driving. However, it remains a tough task to assign semantic labels to various scenes. In this paper, we propose an Active Learning based method for semantic labeling of street view point clouds with a small amount of annotated data samples. The proposed method takes a point cloud and registrated images as the input, and yields a point cloud with semantic labels. We iteratively fine-tunes a network with the ever-enlarging training set to exploit the semantic information of the scene, and fuse the semantic labels in 3D space. To deal with the imbalanced data in street view scenes, a label biased criterion for query selection is proposed to help select images to efficiently improve the performance of the network and the quality of the semantic model. Experimental result shows that the proposed method demands limited human labor and works well in assigning semantic labels to the imbalanced scenes like street view scenes. |
Keyword | Semantic Street View Active Learning |
Indexed By | EI |
Language | 英语 |
Document Type | 会议论文 |
Identifier | http://ir.ia.ac.cn/handle/173211/23566 |
Collection | 模式识别国家重点实验室_机器人视觉 |
Corresponding Author | Shen SH(申抒含) |
Affiliation | 1.中国科学院自动化研究所 2.中国科学院大学 |
First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
Recommended Citation GB/T 7714 | Zhou Y,Shen SH,Hu ZY. Active Semantic Labeling of Street View Point Clouds[C],2019. |
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File Name/Size | DocType | Version | Access | License | ||
0561_final.pdf(2070KB) | 会议论文 | 开放获取 | CC BY-NC-SA | View Download |
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