Scene-unified image translation for visual localization | |
Sheng Han1,2; Wei Gao1,2; Yiming Wan1,2; Yihong Wu1,2 | |
2020-10 | |
会议日期 | 2020.10.25-2020.10.28 |
会议地点 | Abu Dhabi, United Arab Emirates |
英文摘要 | Visual localization is a key technology in the field of 3D robot vision. One of its major difficulties lies in how to deal with the challenges brought by the appearance changes of query images and database images caused by large time spans. Many methods focus on extracting more robust features from images to deal with the impact of complex scenes. In this paper, we explore the impact of image translation on visual localization tasks in complex scenes. We propose UniGAN - a modified image translation model, fusing semantic label constraints and finer reconstruction losses, to unify images captured under different environmental conditions to a standard scene more suitable for localization tasks. To estimate the 6-DOF camera pose, a two-stage localization framework composed of image retrieval and local matching is utilized. Experiments show that our method outperforms the state-of-the-art in terms of both accuracy and robustness to environmentally sensitive scenes. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/40601] |
专题 | 自动化研究所_模式识别国家重点实验室_机器人视觉团队 |
通讯作者 | Sheng Han |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
推荐引用方式 GB/T 7714 | Sheng Han,Wei Gao,Yiming Wan,et al. Scene-unified image translation for visual localization[C]. 见:. Abu Dhabi, United Arab Emirates. 2020.10.25-2020.10.28. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论