ABSTRACT: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.

Nanostructured surfaces are common in nature and exhibit properties such as antireflectivity (moth eyes), self-cleaning (lotus leaf), iridescent colors (butterfly wings), and water harvesting (desert beetles). We now understand such properties and can mimic some of these natural structures in the laboratory. However, these synthetic structures are limited since they are not easily mass produced over large areas due to the limited scalability of current technologies such as UV-lithography, the high cost of infrastructure, and the difficulty in nonplanar surfaces. Here, we report a solution process based on block copolymer (BCP) self-assembly to fabricate subwavelength structures on large areas of optical and curved surfaces with feature sizes and spacings designed to efficiently scatter visible light. Si nanopillars (SiNPs) with diameters of ∼115 ± 19 nm, periodicity of 180 ± 18 nm, and aspect ratio of 2-15 show a reduction in reflectivity by a factor of 100, <0.16% between 400 and 900 nm at an angle of incidence of 30°. Significantly, the reflectivity remains below 1.75% up to incident angles of 75°. Modeling the efficiency of a SiNP PV suggests a 24.6% increase in efficiency, representing a 3.52% (absolute) or 16.7% (relative) increase in electrical energy output from the PV system compared to AR-coated device.

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.

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