Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.16521 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918211900932096 |
|---|---|
| author | Yang, Fan Yamao, Sosuke Kusajima, Ikuo Moteki, Atsunori Masui, Shoichi Jiang, Shan |
| author_facet | Yang, Fan Yamao, Sosuke Kusajima, Ikuo Moteki, Atsunori Masui, Shoichi Jiang, Shan |
| contents | Using ceiling-mounted cameras (CMCs) for indoor visual capturing opens up a wide range of applications. However, registering CMCs to the target scene layout presents a challenging task. While manual registration with specialized tools is inefficient and costly, automatic registration with visual localization may yield poor results when visual ambiguity exists. To alleviate these issues, we propose a novel solution for jointly mapping an indoor scene and registering CMCs to the scene layout. Our approach involves equipping a mobile agent with a head-mounted RGB-D camera to traverse the entire scene once and synchronize CMCs to capture this mobile agent. The egocentric videos generate world-coordinate agent trajectories and the scene layout, while the videos of CMCs provide pseudo-scale agent trajectories and CMC relative poses. By correlating all the trajectories with their corresponding timestamps, the CMC relative poses can be aligned to the world-coordinate scene layout. Based on this initialization, a factor graph is customized to enable the joint optimization of ego-camera poses, scene layout, and CMC poses. We also develop a new dataset, setting the first benchmark for collaborative scene mapping and CMC registration (https://sites.google.com/view/yowo/home). Experimental results indicate that our method not only effectively accomplishes two tasks within a unified framework, but also jointly enhances their performance. We thus provide a reliable tool to facilitate downstream position-aware applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16521 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | YOWO: You Only Walk Once to Jointly Map An Indoor Scene and Register Ceiling-mounted Cameras Yang, Fan Yamao, Sosuke Kusajima, Ikuo Moteki, Atsunori Masui, Shoichi Jiang, Shan Computer Vision and Pattern Recognition Using ceiling-mounted cameras (CMCs) for indoor visual capturing opens up a wide range of applications. However, registering CMCs to the target scene layout presents a challenging task. While manual registration with specialized tools is inefficient and costly, automatic registration with visual localization may yield poor results when visual ambiguity exists. To alleviate these issues, we propose a novel solution for jointly mapping an indoor scene and registering CMCs to the scene layout. Our approach involves equipping a mobile agent with a head-mounted RGB-D camera to traverse the entire scene once and synchronize CMCs to capture this mobile agent. The egocentric videos generate world-coordinate agent trajectories and the scene layout, while the videos of CMCs provide pseudo-scale agent trajectories and CMC relative poses. By correlating all the trajectories with their corresponding timestamps, the CMC relative poses can be aligned to the world-coordinate scene layout. Based on this initialization, a factor graph is customized to enable the joint optimization of ego-camera poses, scene layout, and CMC poses. We also develop a new dataset, setting the first benchmark for collaborative scene mapping and CMC registration (https://sites.google.com/view/yowo/home). Experimental results indicate that our method not only effectively accomplishes two tasks within a unified framework, but also jointly enhances their performance. We thus provide a reliable tool to facilitate downstream position-aware applications. |
| title | YOWO: You Only Walk Once to Jointly Map An Indoor Scene and Register Ceiling-mounted Cameras |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.16521 |