YOWO：您只走一次以联合映射室内场景并注册天花板安装的摄像头
YOWO: You Only Walk Once to Jointly Map An Indoor Scene and Register Ceiling-mounted Cameras
AI总结：
YOWO通过一次遍历场景，联合映射室内场景并注册天花板安装摄像头，提升位置感知应用性能。
摘要：
使用安装在天花板上的摄像头（CMCs）进行室内视觉捕捉开辟了广泛的应用前景。然而，将CMCs与目标场景布局进行配准是一项具有挑战性的任务。尽管使用专用工具进行手动配准效率低下且成本高昂，但使用视觉定位进行自动配准在存在视觉歧义时可能会产生较差的结果。为了解决这些问题，我们提出了一种新的解决方案，用于联合映射室内场景并将其与CMCs配准到场景布局。我们的方法是给移动代理配备一个头戴式RGB-D摄像头，使其一次遍历整个场景，并同步CMCs以捕捉这个移动代理。视点视频生成世界坐标代理轨迹和场景布局，而CMCs的视频提供伪尺度代理轨迹和CMCs相对姿态。通过将所有轨迹与其对应的时刻戳相关联，CMCs的相对姿态可以对齐到世界坐标系下的场景布局。基于此初始化，我们定制了一个因子图，以实现对自身摄像头姿态、场景布局和CMCs姿态的联合优化。我们还开发了一个新数据集，设立了协作场景映射和CMC配准的第一个基准（https://sites.google.com/view/yowo/home）。实验结果表明，我们的方法不仅在统一框架内有效完成了两项任务，还联合提升了它们的性能。因此，我们提供了一个可靠的工具，以促进下游位置感知应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
