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首页> 外文会议>International Conference on Knowledge-Based and Intelligent Information and Engineering Systems >Knowledge-Driven Method for Object Qualification in 3D Point Cloud Data
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Knowledge-Driven Method for Object Qualification in 3D Point Cloud Data

机译：3D点云数据对象资格的知识驱动方法

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摘要

The identification of objects in 3D point cloud data has always presented a real challenge. Such a process highly depends on human interpretation of the scene and its objects. Actual approaches are numerical based; in best cases, static models are used as a template for the detection process. By the presented work, we aim at extending the detection process by bringing the human expert knowledge about the scene, the objects, their characteristics and their relations onto the processing chain. To do, we present in this paper a knowledge-driven method for the detection of object and its qualification using OWL ontology. The knowledge contained by the ontology defines the constraints about the objects. Logic programs are used as rules to define constrains between objects. The processing of the scene is an iterative annotation process that combines 3D algorithms, geometric analysis, spatial analysis and especially specialist's knowledge. The created platform takes a set of 3D point clouds as input and produces as output a populated ontology corresponding to an indexed scene. The context of the study is the detection of railway objects materialized within the Germany Railway scene. Thus, the resulting enriched and populated ontology contains the annotations of objects in the point clouds, and can be used further on to feed a GIS system or an IFC file for architecture purposes.
机译：在3D点云数据中的对象识别始终呈现了真正的挑战。这种过程高度取决于人类对场景及其物体的解释。实际方法是基于数值的;在最佳情况下，静态模型用作检测过程的模板。通过所提出的工作，我们的目标是通过将人类专家知识，对象，特征及其关系带到加工链上来扩展检测过程。要做，我们在本文中展示了一种用于检测对象的知识驱动方法及其使用猫头鹰本体的资格。本体所包含的知识定义了对对象的约束。逻辑程序用作规则以在对象之间定义约束。场景的处理是一种迭代注释过程，其结合了3D算法，几何分析，空间分析，特别是专家的知识。创建的平台将一组3D点云作为输入，并作为输出与索引场景对应的填充本体。该研究的背景是检测在德国铁路场景中实现的铁路物体。因此，生成的丰富和填充的本体包含点云中的对象的注释，并且可以进一步用于为架构目的提供GIS系统或IFC文件。

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