STREAMED VERTICAL RECTANGLE DETECTION IN TERRESTRIAL LASER SCANS FOR FACADE DATABASE PRODUCTION
STREAMED VERTICAL RECTANGLE DETECTION IN TERRESTRIAL LASER SCANS FOR FACADE DATABASE PRODUCTION
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DOI：
10.5194/isprsannals-i-3-99-2012
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发表时间：
2012-07
期刊：
影响因子：
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通讯作者：
Jérôme Demantké;B. Vallet;N. Paparoditis
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文献类型：
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作者：
Jérôme Demantké;B. Vallet;N. Paparoditis
可靠且准确的立面数据库将成为自动驾驶车辆本地化、注册和精细建筑建模等应用的重要资产。移动测绘设备现在提供创建此类数据库所需的数据，但应设计有效的方法来处理通过此类方式收集的大量数据（数小时内每秒采集一百万个点）。另一个重要的限制是在许多不同类型的城市场景中存在大量对象。本文提出了一种克服这两个问题的方法： – 立面检测算法是流式的：数据按照获取的顺序进行处理。更准确地说，输入数据被分成重叠的块，依次分析这些块以提取立面部分。然后合并紧密重叠的部分以恢复完整的立面矩形。 – 分析每个点邻域的几何形状，以定义该点属于垂直平面斑块的概率。然后，在采样步骤和假设验证中，将该概率注入到 RANdom SAmple Consensus (RANSAC) 算法中，以支持最可靠的候选者。这确保了立面检测过程中针对异常值的鲁棒性更强。这样，无需任何有关数据的先验知识即可检测到主要的垂直矩形。唯一的假设是外墙大致是平面和垂直的。该方法已在巴黎的大型数据集上成功进行了测试。尽管树木遮挡了一些外墙的大面积，但仍检测到外墙。检测到的立面矩形的稳健性和准确性使其可用于定位应用以及同一城市或整个城市模型的其他扫描的注册。
A reliable and accurate facade database would be a major asset in applications such as localization of autonomous vehicles, registration and fine building modeling. Mobile mapping devices now provide the data required to create such a database, but efficient methods should be designed in order to tackle the enormous amount of data collected by such means (a million point per second for hours of acquisition). Another important limitation is the presence of numerous objects in urban scenes of many different types. This paper proposes a method that overcomes these two issues: – The facade detection algorithm is streamed: the data is processed in the order it was acquired. More precisely, the input data is split into overlapping blocks which are analysed in turn to extract facade parts. Close overlapping parts are then merged in order to recover the full facade rectangle. – The geometry of the neighborhood of each point is analysed to define a probability that the point belongs to a vertical planar patch. This probability is then injected in a RANdom SAmple Consensus (RANSAC) algorithm both in the sampling step and in the hypothesis validation, in order to favour the most reliable candidates. This ensures much more robustness against outliers during the facade detection. This way, the main vertical rectangles are detected without any prior knowledge about the data. The only assumptions are that the facades are roughly planar and vertical. The method has been successfully tested on a large dataset in Paris. The facades are detected despite the presence of trees occluding large areas of some facades. The robustness and accuracy of the detected facade rectangles makes them useful for localization applications and for registration of other scans of the same city or of entire city models.