摘要
Robotics&Machine Learning Daily News Daily News的新闻记者兼工作人员新闻编辑-研究人员在Robotics S中详细描述了新的数据。根据NewsRx编辑在摄影测量和遥感研究所的新闻报道，Research指出：“神经辐射场(NeRFs)已经成为一个快速发展的研究领域，具有革命性的摄影测量工作流程的潜力。作为输入，NERF需要多视图图像和相应的相机姿态，因为我们将作为内部方向。新闻记者从Pho Togrametry和遥感研究所获得了一句话：“在典型的NeRF工作流程中，摄像机的姿态和内部方位是用Motion(SfM)的结构预先估计的。但由此产生的新视图的质量取决于不同的参数，如可用图像的数量和分布，此外，SfM是一个耗时的预处理步骤，其鲁棒性和质量依赖于图像内容，且SfM尺度因子的不确定性阻碍了后续需要度量信息的步骤。我们评估了NeRFs在工业机器人应用中的潜力。本文提出了一种替代SfM预处理的方法:用安装在工业机器人末端执行器上的标定摄像机捕获输入图像,并根据机器人运动学确定精确的摄像机位姿,然后通过比较M与地面真值来研究新视点的质量。基于ENSEMBL E方法的内部质量度量。为了评估的目的，我们获得了多个用于重建典型工业应用的姿态挑战数据集，如反射目标、不良纹理和精细结构。我们表明，基于机器人的姿态威慑在非要求的情况下达到了与SfM相似的精度，而在更具挑战性的情况下具有CL EAR优势。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in robotic s. According to news reporting out of the Institute of Photogrammetry and Remote Sensing by NewsRx editors, research stated, "Neural Radiance Fields (NeRFs) hav e become a rapidly growing research field with the potential to revolutionize ty pical photogrammetric workflows, such as those used for 3D scene reconstruction. As input, NeRFs require multi-view images with corresponding camera poses as we ll as the interior orientation." The news correspondents obtained a quote from the research from Institute of Pho togrammetry and Remote Sensing: "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, the accurac y of the related camera poses and interior orientation, but also the reflection characteristics of the depicted scene, is difficult to predict. In addition, SfM is a time-consuming pre-processing step, and its robustness and quality strongl y depend 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. To start with, we propose an alternative to SfM pre-processing: we capture the input imag es with a calibrated camera that is attached to the end effector of an industria l 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 the m to ground truth, and by computing an internal quality measure based on ensembl e methods. For evaluation purposes, we acquire multiple datasets that pose chall enges for reconstruction typical of industrial applications, like reflective obj ects, poor texture, and fine structures. We show that the robot-based pose deter mination reaches similar accuracy as SfM in non-demanding cases, while having cl ear advantages in more challenging scenarios."
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