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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >A Novel Octree-Based 3-D Fully Convolutional Neural Network for Point Cloud Classification in Road Environment
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A Novel Octree-Based 3-D Fully Convolutional Neural Network for Point Cloud Classification in Road Environment

机译：一种基于八叉树的新型3-D全卷积神经网络，用于道路环境中的点云分类

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摘要

The automatic classification of 3-D point clouds is publicly known as a challenging task in a complex road environment. Specifically, each point is automatically classified into a unique category label, and then, the labels are used as clues for semantic analysis and scene recognition. Instead of heuristically extracting handcrafted features in traditional methods to classify all points, we put forward an end-to-end octree-based fully convolutional network (FCN) to classify 3-D point clouds in an urban road environment. There are four contributions in this paper. The first is that the integration and comprehensive uses of OctNet and FCN greatly decrease the computing time and memory demands compared with a dense 3-D convolutional neural network (CNN). The second is that the octree-based network is strengthened by means of modifying the cross-entropy loss function to solve the problems of an unbalanced category distribution. The third is that an Inception-ResNet block is united with our network, which enables our 3-D CNN to effectively learn how to classify scenes containing objects at multiple scales and improve classification accuracy. The last is that an open source data set (HuangshiRoad data set) with ten different classes is introduced for 3-D point cloud classification. Three representative data sets [Semantic3D, WHU_MLS (blocks I and II), and HuangshiRoad] with different covered areas and numbers of points and classes are selected to evaluate our proposed method. The experimental results show that the overall classification accuracy is appreciable, with 89.4% for Semantic3D, 82.9% for WHU_MLS block I, 91.4% for WHU_MLS block II, and 94% for HuangshiRoad. Our deep learning approach can efficiently classify 3-D dense point clouds in an urban road environment measured by a mobile laser scanning (MLS) system or static LiDAR.
机译：众所周知，在复杂的道路环境中，对3D点云进行自动分类是一项艰巨的任务。具体来说，将每个点自动分类为唯一的类别标签，然后将这些标签用作语义分析和场景识别的线索。我们没有采用传统方法试探性地提取手工特征来对所有点进行分类，而是提出了一种基于端到端八叉树的全卷积网络（FCN）来对城市道路环境中的3D点云进行分类。本文有四个贡献。首先，与密集的3D卷积神经网络（CNN）相比，OctNet和FCN的集成和广泛使用大大减少了计算时间和内存需求。第二个是通过修改交叉熵损失函数来增强基于八叉树的网络，以解决类别分布不平衡的问题。第三是Inception-ResNet块与我们的网络结合在一起，这使我们的3-D CNN可以有效地学习如何对包含多个尺度的物体的场景进行分类，并提高分类精度。最后是引入具有十个不同类别的开源数据集（HuangshiRoad数据集）用于3-D点云分类。选择三个具有代表性的数据集（Semantic3D，WHU_MLS（I和II块）和HuangshiRoad），它们具有不同的覆盖区域以及点和类的数量，以评估我们提出的方法。实验结果表明，总体分类准确率是可观的，Semantic3D为89.4％，WHU_MLS块I为82.9％，WHU_MLS块II为91.4％，黄石路为94％。我们的深度学习方法可以有效地对通过移动激光扫描（MLS）系统或静态LiDAR测量的城市道路环境中的3D密集点云进行分类。

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