Neural Radiance Fields (NeRFs) are trained using a set of camera poses and associated images as input to estimate density and color values for each position. The position-dependent density learning is of particular interest for photogrammetry, enabling 3D reconstruction by querying and filtering the NeRF coordinate system based on the object density. While traditional methods like Structure from Motion are commonly used for camera pose calculation in pre-processing for NeRFs, the HoloLens offers an interesting interface for extracting the required input data directly. We present a workflow for high-resolution 3D reconstructions almost directly from HoloLens data using NeRFs. Thereby, different investigations are considered: Internal camera poses from the HoloLens trajectory via a server application, and external camera poses from Structure from Motion, both with an enhanced variant applied through pose refinement. Results show that the internal camera poses lead to NeRF convergence with a PSNR of 25\,dB with a simple rotation around the x-axis and enable a 3D reconstruction. Pose refinement enables comparable quality compared to external camera poses, resulting in improved training process with a PSNR of 27\,dB and a better 3D reconstruction. Overall, NeRF reconstructions outperform the conventional photogrammetric dense reconstruction using Multi-View Stereo in terms of completeness and level of detail.

翻译：神经辐射场（NeRFs）通过输入一组相机位姿及关联图像进行训练，以估计每个位置处的密度与颜色值。这种位置相关密度学习对摄影测量领域具有特殊意义，可通过基于物体密度对NeRF坐标系统进行查询与滤波实现三维重建。尽管运动恢复结构等传统方法常用于NeRF预处理阶段的相机位姿计算，但HoloLens提供了直接提取所需输入数据的独特接口。我们提出了一种利用NeRFs从HoloLens数据近乎直接实现高分辨率三维重建的工作流程，并开展了多项对比研究：通过服务器应用获取的HoloLens轨迹内部相机位姿，以及通过运动恢复结构获取的外部相机位姿，两者均采用经位姿优化的增强变体进行处理。结果表明：采用绕x轴简单旋转的内部相机位姿可使NeRF以25 dB的峰值信噪比（PSNR）收敛并实现三维重建；位姿优化可生成与外部相机位姿相当的训练质量，使训练过程的PSNR提升至27 dB并显著改善三维重建效果。总体而言，NeRF重建在完整性与细节层次上均优于传统多视角立体摄影测量稠密重建方法。