点云
激光雷达
计算机科学
分割
卷积神经网络
卷积(计算机科学)
光学(聚焦)
人工智能
点(几何)
深度学习
操作员(生物学)
适应(眼睛)
模式识别(心理学)
钥匙(锁)
遥感
人工神经网络
计算机视觉
数据挖掘
地质学
数学
几何学
光学
物理
生物化学
基因
计算机安全
转录因子
抑制因子
化学
作者
Miguel Díaz-Medina,José Manuel Fuertes,Rafael J. Segura,Manuel Lucena,Carlos J. Ogáyar
标识
DOI:10.1016/j.cageo.2023.105453
摘要
The goal of this work is to study the adaptation of a semantic segmentation technique to use LiDAR point clouds in their original format as input. Until now, almost all the methods that have been proposed to apply deep learning models on point clouds keep their focus on generic point clouds, making use only of geometric or colour information. The deep learning architecture proposed in this work acts as an alternative to classical convolution models. We use the EdgeConv operator, applied to 3D data, which uses the nearest neighbors to extract local features. We show the results of an experimentation that reveals the influence of additional LiDAR information channels on the performance of the neural network, proving that the usage of channels such as intensity can improve the average accuracy up to 20% when compared to the exclusive usage of geometry channels. Unlike other related works, this one is based in the study of semantic segmentation applied to outdoor environments.
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