论文标题
LIDAR数据中使用点密度和卷积神经网络中的树木注释
Tree Annotations in LiDAR Data Using Point Densities and Convolutional Neural Networks
论文作者
论文摘要
LIDAR提供高度准确的3D点云。但是，需要手动标记数据才能提供后续的有用信息。此类数据的手动注释是耗时，繁琐的和容易出错的，因此在本文中，我们提出了三种自动方法，用于在LIDAR数据中注释树。第一种方法需要高密度点云，并将某些LIDAR数据属性用于树识别目的，达到了几乎90％的精度。第二种方法在低密度激光雷达数据集上使用基于体素的3D卷积神经网络，并能够准确地识别大多数大型树，但由于素化过程而与较小的树木挣扎。第三种方法是PointNet ++方法的缩放版本，直接在室外点云上工作，并在ISPRS基准数据集上达到了82.1％的F_SCORE，可与最先进的方法相媲美，但效率提高。
LiDAR provides highly accurate 3D point clouds. However, data needs to be manually labelled in order to provide subsequent useful information. Manual annotation of such data is time consuming, tedious and error prone, and hence in this paper we present three automatic methods for annotating trees in LiDAR data. The first method requires high density point clouds and uses certain LiDAR data attributes for the purpose of tree identification, achieving almost 90% accuracy. The second method uses a voxel-based 3D Convolutional Neural Network on low density LiDAR datasets and is able to identify most large trees accurately but struggles with smaller ones due to the voxelisation process. The third method is a scaled version of the PointNet++ method and works directly on outdoor point clouds and achieves an F_score of 82.1% on the ISPRS benchmark dataset, comparable to the state-of-the-art methods but with increased efficiency.
