• Demantké J
  • Vallet B
  • Paparoditis N
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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.




Demantké, J., Vallet, B., & Paparoditis, N. (2012). STREAMED VERTICAL RECTANGLE DETECTION IN TERRESTRIAL LASER SCANS FOR FACADE DATABASE PRODUCTION. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, I3, 99–104.

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