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Range segmentation of large building exteriors: A hierarchical robust approach

机译:大型建筑外墙的范围分割:一种分层的稳健方法

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摘要

There are three main challenging issues associated with processing range data of large-scale outdoor scene: (a) significant disparity in the size of features, (b) existence of complex and multiple structures; and (c) high uncertainty in data due to the construction error or moving objects. Existing range segmentation methods in computer vision literature have been generally developed for laboratory-sized objects or shapes with simple geometric features and do not address these issues. This paper studies the main problems involved in segmenting the range data of large building exteriors and presents a robust hierarchical segmentation strategy to extract fine as well as large details from such data. The proposed method employs a high breakdown robust estimator in a coarse-to-fine approach to deal with the existing discrepancies in size and sampling rates of various features of large outdoor objects. The segmentation algorithm is tested on several outdoor range datasets obtained by different laser rangescanners. The results show that the proposed method is an accurate and computationally cost-effective tool that facilitates automatic generation of 3D models of large-scale objects in general and building exteriors in particular.
机译:与大型室外场景的处理范围数据相关的三个主要挑战性问题是:(a)特征尺寸的巨大差异;(b)复杂和多重结构的存在; (c)由于施工错误或移动物体而导致数据的高度不确定性。计算机视觉文献中的现有范围分割方法通常是针对具有简单几何特征的实验室大小的物体或形状而开发的,无法解决这些问题。本文研究了分割大型建筑物外部范围数据所涉及的主要问题,并提出了一种鲁棒的分层分割策略,可以从此类数据中提取精细和大型细节。所提出的方法在从粗到精的方法中采用了高击穿鲁棒估计器,以处理大型室外物体各种特征的大小和采样率方面的现有差异。在由不同激光测距仪获得的几个室外范围数据集上测试了分割算法。结果表明,所提出的方法是一种精确且具有计算成本效益的工具,可促进自动生成大型物体的3D模型,特别是建筑物外部。

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