Neural Radiance Fields (NeRFs) have become a rapidly growing research field with the potential to revolutionize typical photogrammetric workflows, such as those used for 3D scene reconstruction. As input, NeRFs require multi-view images with corresponding camera poses as well as the interior orientation. In the typical NeRF workflow, the camera poses and the interior orientation are estimated in advance with Structure from Motion (SfM). But the quality of the resulting novel views, which depends on different parameters such as the number and distribution of available images, as well as the accuracy of the related camera poses and interior orientation, is difficult to predict. In addition, SfM is a time-consuming pre-processing step, and its quality strongly depends on the image content. Furthermore, the undefined scaling factor of SfM hinders subsequent steps in which metric information is required. In this paper, we evaluate the potential of NeRFs for industrial robot applications. We propose an alternative to SfM pre-processing: we capture the input images with a calibrated camera that is attached to the end effector of an industrial robot and determine accurate camera poses with metric scale based on the robot kinematics. We then investigate the quality of the novel views by comparing them to ground truth, and by computing an internal quality measure based on ensemble methods. For evaluation purposes, we acquire multiple datasets that pose challenges for reconstruction typical of industrial applications, like reflective objects, poor texture, and fine structures. We show that the robot-based pose determination reaches similar accuracy as SfM in non-demanding cases, while having clear advantages in more challenging scenarios. Finally, we present first results of applying the ensemble method to estimate the quality of the synthetic novel view in the absence of a ground truth.

翻译：神经辐射场（NeRF）已成为一个快速发展的研究领域，有潜力彻底改变典型摄影测量工作流程（例如用于三维场景重建的流程）。作为输入，NeRF需要多视角图像及其对应的相机位姿，以及内方位参数。在典型的NeRF工作流程中，相机位姿和内方位参数通过运动恢复结构（SfM）预先估计。但由此生成的新视角质量（取决于可用图像的数量和分布，以及相关相机位姿和内方位参数的精度）难以预测。此外，SfM是一个耗时的预处理步骤，其质量强烈依赖于图像内容。而且，SfM未定义的缩放因子会阻碍后续需要度量信息的步骤。在本文中，我们评估了NeRF在工业机器人应用中的潜力。我们提出了一种替代SfM预处理的方法：使用安装在工业机器人末端执行器上的已标定相机捕获输入图像，并基于机器人运动学确定具有度量尺度的精确相机位姿。随后，我们通过将新视角与真实值对比，并基于集成方法计算内部质量度量，来研究新视角的质量。为了评估，我们采集了多个数据集，这些数据集包含工业应用中典型的重建挑战，例如反光物体、纹理贫乏和精细结构。我们表明，在非苛刻情况下，基于机器人的位姿确定能达到与SfM相近的精度，而在更具挑战性的场景中则具有明显优势。最后，我们给出了在缺乏真实值情况下，应用集成方法估计合成新视角质量的初步结果。