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This papers presents a multi-scale method that computes robust geometric features on lidar point clouds in order to retrieve the optimal neighborhood size for each point. Three dimensionality features are calculated on spherical neighborhoods at various radius sizes. Based on combinations of the eigenvalues of the local structure tensor, they describe the shape of the neighborhood, indicating whether the local geometry is more linear (1D), planar (2D) or volumetric (3D). Two radius-selection criteria have been tested and compared for finding automatically the optimal neighborhood radius for each point. Besides, such procedure allows a dimensionality labelling, giving significant hints for classification and segmentation purposes. The method is successfully applied to 3D point clouds from airborne, terrestrial, and mobile mapping systems since no a priori knowledge on the distribution of the 3D points is required. Extracted dimensionality features and labellings are then favorably compared to those computed from constant size neighborhoods.

International audience; Automatic 3D point cloud registration is a main issue in computer vision and remote sensing. One of the most commonly adopted solution is the well-known Iterative Closest Point (ICP) algorithm. This standard approach performs a fine registration of two overlapping point clouds by iteratively estimating the transformation parameters, assuming good a priori alignment is provided. A large body of literature has proposed many variations in order to improve each step of the process (namely selecting, matching, rejecting, weighting and minimizing). The aim of this paper is to demonstrate how the knowledge of the shape that best fits the local geometry of each 3D point neighborhood can improve the speed and the accuracy of each of these steps. We first present the geometrical features that are the basis of this work. These low-level attributes indeed describe the neighborhood shape around each 3D point. They allow to retrieve the optimal size for analyzing the neighborhoods at various scales as well as the privileged local dimension (linear, planar, or volumetric). Several variations of each step of the ICP process are then proposed and analyzed by introducing these features. Such variants are compared on real datasets, as well with the original algorithm in order to retrieve the most efficient algorithm for the whole process. The method is therefore successfully applied to various 3D lidar point clouds from airborne, terrestrial, and mobile mapping systems. Improvements are noticed for two of the five ICP steps, while concluding our features may not be relevant for very dissimilar object samplings.

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.