As Neural Radiance Field (NeRF) implementations become faster, more efficient and accurate, their applicability to real world mapping tasks becomes more accessible. Traditionally, 3D mapping, or scene reconstruction, has relied on expensive LiDAR sensing. Photogrammetry can perform image-based 3D reconstruction but is computationally expensive and requires extremely dense image representation to recover complex geometry and photorealism. NeRFs perform 3D scene reconstruction by training a neural network on sparse image and pose data, achieving superior results to photogrammetry with less input data. This paper presents an evaluation of two NeRF scene reconstructions for the purpose of estimating the diameter of a vertical PVC cylinder. One of these are trained on commodity iPhone data and the other is trained on robot-sourced imagery and poses. This neural-geometry is compared to state-of-the-art lidar-inertial SLAM in terms of scene noise and metric-accuracy.

翻译：随着神经辐射场（NeRF）实现方法变得更快、更高效和更精确，它们在实际地图构建任务中的适用性日益增强。传统上，三维地图构建或场景重建依赖于昂贵的激光雷达传感。摄影测量技术能够执行基于图像的三维重建，但计算成本高昂，且需要极其密集的图像表示才能恢复复杂的几何结构和照片级真实感。NeRF通过在稀疏图像和姿态数据上训练神经网络来执行三维场景重建，以更少的输入数据实现了优于摄影测量的结果。本文评估了两种NeRF场景重建方法，旨在估算垂直PVC圆柱体的直径。其中一种使用消费级iPhone数据进行训练，另一种则使用机器人采集的图像和姿态进行训练。我们将这种神经几何重建方法与最先进的激光雷达-惯性SLAM在场景噪声和度量精度方面进行了比较。