3D Semantic Labeling of Photogrammetry Meshes Based on Active Learning | |
Mengqi Rong1,2; Shuhan Shen1,2; Zhanyi Hu1,2 | |
2021-01-10 | |
会议日期 | 2021-1-10 |
会议地点 | Milan, Italy |
DOI | 10.1109/ICPR48806.2021.9412358 |
英文摘要 | As different urban scenes are similar but still not completely consistent, coupled with the complexity of labeling directly in 3D, high-level understanding of 3D scenes has always been a tricky problem. In this paper, we propose a procedural approach for 3D semantic expression of urban scenes based on active learning. We first start with a small labeled image set to fine-tune a semantic segmentation network and then project its probability map onto a 3D mesh model for fusion, finally outputs a 3D semantic mesh model in which each facet has a semantic label and a heat model showing each facet’s confidence. Our key observation is that our algorithm is iterative, in each iteration, we use the output semantic model as a supervision to select several valuable images for annotation to co-participate in the fine-tuning for overall improvement. In this way, we reduce the workload of labeling but not the quality of 3D semantic model. Using urban areas from two different cities, we show the potential of our method and demonstrate its effectiveness. |
会议录出版者 | IEEE |
URL标识 | 查看原文 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/52438] |
专题 | 精密感知与控制研究中心_精密感知与控制 |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Mengqi Rong,Shuhan Shen,Zhanyi Hu. 3D Semantic Labeling of Photogrammetry Meshes Based on Active Learning[C]. 见:. Milan, Italy. 2021-1-10. |
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