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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >3-D Object Classification in Heterogeneous Point Clouds via Bag-of-Words and Joint Distribution Adaption
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3-D Object Classification in Heterogeneous Point Clouds via Bag-of-Words and Joint Distribution Adaption

机译：通过词袋和联合分布自适应在异构点云中进行3D对象分类

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摘要

Object classification model used in point clouds requires to be retrained when applying on heterogeneous point clouds that are collected from different kinds of laser scanning systems. The model training procedure needs supervised information from the corresponding laser scanning systems. However, the acquisition of supervised information is labor-intensive and time-consuming. This letter proposes a new framework to exploit objects from point clouds with supervised information (source domain) to directly classify objects from heterogeneous point clouds with no supervised information (target domain). More specifically, to alleviate occlusions and point density variations, intraclass variations in object classification, the proposed framework integrates a bag-of-words model to effectively describe 3-D objects of point clouds. To alleviate the difference of point clouds collected from various devices, a joint distribution adaption model is exploited to build an effective feature transformation to accomplish the adaption in the source and target domains. The proposed framework is validated on data sets, which contain three kinds of point clouds. Extensive experiments show the effectiveness of the proposed framework on classifying 3-D objects in heterogeneous point clouds.
机译：在将点云中使用的对象分类模型应用于从不同种类的激光扫描系统中收集的异构点云时，需要对其进行重新训练。模型训练过程需要来自相应激光扫描系统的监督信息。然而，获得监督信息是劳动密集型且耗时的。这封信提出了一个新框架，以利用来自具有监督信息（源域）的点云中的对象来直接分类来自没有监督信息（目标域）的异构点云中的对象。更具体地说，为缓解遮挡和点密度变化，对象分类中的类内变化，建议的框架集成了词袋模型以有效描述点云的3D对象。为了减轻从各种设备收集的点云的差异，利用联合分布自适应模型来建立有效的特征转换，以在源域和目标域中完成自适应。该框架在包含三种点云的数据集上得到了验证。大量实验表明，所提出的框架在异构点云中对3D对象进行分类的有效性。

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