通过混合现实进行3D重建：一种用于室内SLAM的即时盲区检测器
3D Reconstruction by Looking: Instantaneous Blind Spot Detector for Indoor SLAM through Mixed Reality
AI总结：
通过混合现实技术实现室内SLAM的即时盲区检测，提升3D重建精度与效率。
摘要：
室内SLAM常常面临场景漂移、双墙和盲区等问题，特别是在传感器（如激光雷达和相机）附近有物体的狭小空间中进行重建任务时更为明显。在数据采集过程中实时可视化点云配准可能有助于缓解这些问题，但仍然存在无法深入比较扫描数据与实际物理环境的显著限制。这些挑战阻碍了重建产品的质量，通常需要重新访问和重新扫描。为此，我们开发了LiMRSF（激光雷达-MR-RGB传感器融合）系统，使用户能够通过混合现实（MR）头显查看现场的点云配准。该定制框架将点云网格可视化为全息图，无缝匹配通过见光眼镜实时呈现的场景，并自动突出重叠时检测到的错误。这些全息元素通过TCP服务器传输到MR头显中，其中对其进行校准以与世界坐标系和物理位置对齐。这使用户能够即时查看局部重建产品，使他们能够快速识别盲区和错误，并在现场采取及时行动。我们的盲区检测器在LiMRSF系统中实现了75.76%的误差检测精度（通过LiMRSF系统具有可接受的高保真度监控，最高SSIM为0.5619，PSNR为14.1004，最低MSE为0.0389，在五个不同的简化网格模型部分中，用户通过LiMRSF设备的见光眼镜可视化）。该方法确保了创建详细、高质量的3D模型数据集，潜在应用在建筑信息建模（BIM）中，但不局限于此。
英文摘要：
Indoor SLAM often suffers from issues such as scene drifting, double walls, and blind spots, particularly in confined spaces with objects close to the sensors (e.g. LiDAR and cameras) in reconstruction tasks. Real-time visualization of point cloud registration during data collection may help mitigate these issues, but a significant limitation remains in the inability to in-depth compare the scanned data with actual physical environments. These challenges obstruct the quality of reconstruction products, frequently necessitating revisit and rescan efforts. For this regard, we developed the LiMRSF (LiDAR-MR-RGB Sensor Fusion) system, allowing users to perceive the in-situ point cloud registration by looking through a Mixed-Reality (MR) headset. This tailored framework visualizes point cloud meshes as holograms, seamlessly matching with the real-time scene on see-through glasses, and automatically highlights errors detected while they overlap. Such holographic elements are transmitted via a TCP server to an MR headset, where it is calibrated to align with the world coordinate, the physical location. This allows users to view the localized reconstruction product instantaneously, enabling them to quickly identify blind spots and errors, and take prompt action on-site. Our blind spot detector achieves an error detection precision with an F1 Score of 75.76% with acceptably high fidelity of monitoring through the LiMRSF system (highest SSIM of 0.5619, PSNR of 14.1004, and lowest MSE of 0.0389 in the five different sections of the simplified mesh model which users visualize through the LiMRSF device see-through glasses). This method ensures the creation of detailed, high-quality datasets for 3D models, with potential applications in Building Information Modeling (BIM) but not limited.
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。
