This work represents a large step into modern ways of fast 3D reconstruction based on RGB camera images. Utilizing a Microsoft HoloLens 2 as a multisensor platform that includes an RGB camera and an inertial measurement unit for SLAM-based camera-pose determination, we train a Neural Radiance Field (NeRF) as a neural scene representation in real-time with the acquired data from the HoloLens. The HoloLens is connected via Wifi to a high-performance PC that is responsible for the training and 3D reconstruction. After the data stream ends, the training is stopped and the 3D reconstruction is initiated, which extracts a point cloud of the scene. With our specialized inference algorithm, five million scene points can be extracted within 1 second. In addition, the point cloud also includes radiometry per point. Our method of 3D reconstruction outperforms grid point sampling with NeRFs by multiple orders of magnitude and can be regarded as a complete real-time 3D reconstruction method in a mobile mapping setup.

翻译：本工作代表了基于RGB相机图像快速三维重建现代化方法的重要进展。利用微软HoloLens 2作为多传感器平台（包含RGB相机和用于基于SLAM的相机位姿确定的惯性测量单元），我们通过HoloLens采集的数据实时训练神经辐射场（NeRF）作为神经场景表示。HoloLens通过WiFi连接至负责训练和三维重建的高性能PC。数据流传输结束后，训练停止并启动三维重建，提取场景的点云。借助我们专门设计的推理算法，可在1秒内提取五百万个场景点。此外，该点云还包含每个点的辐射度量信息。我们的三维重建方法相比NeRF的网格点采样实现了多个数量级的性能提升，可视为移动测绘场景中完整的实时三维重建方法。