Sharing Ambient Objects Using Real-time Point Cloud Streaming in Web-based XR Remote Collaboration
Sharing Ambient Objects Using Real-time Point Cloud Streaming in Web-based XR Remote Collaboration
复制标题
在基于 Web 的 XR 远程协作中使用实时点云流共享环境对象
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Soo
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文献类型：
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作者：
Yongjae Lee;Byounghyun Yoo;Soo
扩展现实（XR）协作支持物理空间和虚拟空间之间的协作。最近的XR协作研究侧重于共享和理解感兴趣对象（OOI）及其周围环境对象（AOs）的总体情况，而不是简单地识别OOI的存在。整体情况的共享是使用三维（3D）模型实现的，该模型复制物理工作空间中存在的对象。有两种创建模型的方法：预重建和实时重建。预重建方法需要相当长的时间来精确地创建多边形网格，而实时重建方法需要相当长的时间来安装大量传感器以进行精确的3D扫描。此外，这些方法很难用于重建空间之外的协作，这使得它们在实际的XR协作中不切实际。本文提出的方法将构成物理工作空间的对象分离为OOI和AO，预先仅将OOI建模为多边形网格，并利用光检测和测距技术将AO重构为点云进行协作。重构的点云通过webbrtc与远程合作者共享，webbrtc是一种基于web的低延迟点对点网络技术。每个远程协作者收集交付的点云，形成一个虚拟空间，以便他们可以直观地了解本地站点的情况。由于我们的方法不为本地站点上存在的所有对象创建多边形网格，因此我们可以节省准备协作的时间。此外，我们可以通过消除在本地站点安装大量传感器的需要来提高XR协作的实用性。我们将介绍一个原型和一个示例场景来演示我们的方法的实用性。
Extended reality (XR) collaboration enables collaboration between physical and virtual spaces. Recent XR collaboration studies have focused on sharing and understanding the overall situation of the objects of interest (OOIs) and its surrounding ambient objects (AOs) rather than simply recognizing the existence of OOI. The sharing of the overall situation is achieved using three-dimensional (3D) models that replicate objects existing in the physical workspace. There are two approaches for creating the models: pre-reconstruction and real-time reconstruction. The pre-reconstruction approach takes considerable time to create polygon meshes precisely, and the real-time reconstruction approach requires a considerable time to install numerous sensors to perform accurate 3D scanning. In addition, these approaches are difficult to be used on the collaboration in a location beyond the reconstructed space, making them impractical to an actual XR collaboration. The approach proposed in this study separates the objects that form the physical workspace into OOI and AO, models only the OOI as a polygon mesh in advance, and reconstructs the AO into a point cloud using light detection and ranging technology for collaboration. The reconstructed point cloud is shared with remote collaborators through WebRTC, a web-based peer-to-peer networking technology with low latency. Each remote collaborator collects the delivered point cloud to form a virtual space, so that they can intuitively understand the situation at a local site. Because our approach does not create polygon meshes for all objects existing at the local site, we can save time to prepare for collaboration. In addition, we can improve the practicality of XR collaboration by eliminating the need to install numerous sensors at the local site. We introduce a prototype and an example scenario to demonstrate the practicality of our approach.