混合现实头戴式显示器用于利用虚拟种子进行增强型室内点云分割

Juan C. Navares-Vázquez, Pedro Arias, Lucía Díaz-Vilariño, Jesús Balado
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引用次数: 0

摘要

与传统 3D 扫描仪相比,混合现实(MR)头戴式显示器(HMD)具有一系列迄今尚未充分利用的优势。这些优势是磁共振头戴式显示器所固有的,尽管最初并不是为此类应用而设计的,但包括手部自由移动、手部跟踪能力和实时网格可视化。本研究利用这些特性来增强室内扫描过程。主要创新点在于手动定位磁共振虚拟种子的概念化,以便通过区域生长方法进行室内点云分割。所提出的方法可通过 HoloLens 2 平台有效实施。设计了一个应用程序,可根据用户在 MR-HMD 显示屏上的视觉焦点远程放置虚拟标签。这种非侵入式界面进一步丰富了标签的快速保存和删除功能,并通过将标签叠加到真实世界的物体上增强了标签的可视化。为了评估所建议方法的实用性,我们进行了一项全面的真实世界案例研究,研究面积达 330 平方米。值得注意的是,这项调查显示了显著的效率,20 个虚拟标签被迅速部署,每个标签只需要 2 秒钟就能精确定位。随后,这些虚拟标签被用作点云分割区域生长算法的种子。虚拟标签定位的精确度非常高,平均误差为 2.4 ± 1.8 厘米。重要的是,用户体验得到显著提升,种子定位得到改善,最终的分割结果也更加精确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mixed reality head mounted displays for enhanced indoor point cloud segmentation with virtual seeds

Mixed Reality (MR) Head Mounted Displays (HMDs) offer a hitherto underutilized set of advantages compared to conventional 3D scanners. These benefits, inherent to MR-HMDs albeit not originally intended for such applications, encompass the freedom of hand movement, hand tracking capabilities, and real-time mesh visualization. This study leverages these attributes to enhance indoor scanning process. The primary innovation lies in the conceptualization of manual-positioned MR virtual seeds for the purpose of indoor point cloud segmentation via a region-growing approach. The proposed methodology is effectively implemented using the HoloLens 2 platform. An application is designed to enable the remote placement of virtual tags based on the user's visual focus on the MR-HMD display. This non-intrusive interface is further enriched with expedited tag saving and deletion functionalities, as well as augmented tag visualization through overlaying them on real-world objects. To assess the practicality of the proposed method, a comprehensive real-world case study spanning an area of 330 s2 is conducted. Remarkably, the survey demonstrates remarkable efficiency, with 20 virtual tags swiftly deployed, each requiring a mere 2 s for precise positioning. Subsequently, these virtual tags are employed as seeds in a region-growing algorithm for point cloud segmentation. The accuracy of virtual tag positioning is found to be exceptional, with an average error of 2.4 ± 1.8 cm. Importantly, the user experience is significantly enhanced, leading to improved seed positioning and, consequently, more accurate final segmentation results.

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