A Convolutional Learning System for Object Classification in 3-D Lidar Data
A Convolutional Learning System for Object Classification in 3-D Lidar Data
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DOI：
10.1109/tnn.2010.2044802
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发表时间：
2010-05-01
影响因子：
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通讯作者：
Prokhorov, Danil
中科院分区：
文献类型：
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作者：
Prokhorov, Danil
在本文中，提出了一种卷积学习系统，用于对以 3D 形式表示为激光反射点云的分段对象进行分类。讨论了几个新颖之处：1）将现有的卷积神经网络（CNN）框架扩展到多视图设置中直接处理 3D 数据，这可能有助于考虑旋转不变性；2）通过采用随机元下降（SMD）方法来提高 CNN 训练效果；3）将无监督和监督训练相结合以增强 CNN 的性能。 CNN 的性能通过分段室外环境中对象的两类数据集进行说明。
In this brief, a convolutional learning system for classification of segmented objects represented in 3-D as point clouds of laser reflections is proposed. Several novelties are discussed: 1) extension of the existing convolutional neural network (CNN) framework to direct processing of 3-D data in a multiview setting which may be helpful for rotation-invariant consideration, 2) improvement of CNN training effectiveness by employing a stochastic meta-descent (SMD) method, and 3) combination of unsupervised and supervised training for enhanced performance of CNN. CNN performance is illustrated on a two-class data set of objects in a segmented outdoor environment.