In this paper we present an automatic algorithm to detect basic shapes in unorganized point clouds. The algorithm decomposes the point cloud into a concise, hybrid structure of inherent shapes and a set of remaining points. Each detected shape serves as a proxy for a set of corresponding points. Our method is based on random sampling and detects planes, spheres, cylinders, cones and tori. For models with surfaces composed of these basic shapes only, for example, CAD models, we automatically obtain a representation solely consisting of shape proxies. We demonstrate that the algorithm is robust even in the presence of many outliers and a high degree of noise. The proposed method scales well with respect to the size of the input point cloud and the number and size of the shapes within the data. Even point sets with several millions of samples are robustly decomposed within less than a minute. Moreover, the algorithm is conceptually simple and easy to implement. Application areas include measurement of physical parameters, scan registration, surface compression, hybrid rendering, shape classification, meshing, simplification, approximation and reverse engineering.

Efficient RANSAC for Point-Cloud Shape Detection

Over the past years, several filters have been developed to extract bare-Earth points from point clouds. ISPRS Working Group III/3 conducted a test to determine the performance of these filters and the influence of point density thereon, and to identify directions for future research. Twelve selected datasets have been processed by eight participants. In this paper, the test results are presented. The paper describes the characteristics of the provided datasets and the used filter approaches. The filter performance is analysed both qualitatively and quantitatively. All filters perform well in smooth rural landscapes, but all produce errors in complex urban areas and rough terrain with vegetation. In general, filters that estimate local surfaces are found to perform best. The influence of point density could not well be determined in this experiment. Future research should be directed towards the usage of additional data sources, segment-based classification, and self-diagnosis of filter algorithms.

Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds

Recent advances in airborne light detection and ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. This technology is becoming a primary method for generating high-resolution digital terrain models (DTMs) that are essential to numerous applications such as flood modeling and landslide prediction. Airborne LIDAR systems usually return a three-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. In order to generate a DTM, measurements from nonground features such as buildings, vehicles, and vegetation have to be classified and removed. In this paper, a progressive morphological filter was developed to detect nonground LIDAR measurements. By gradually increasing the window size of the filter and using elevation difference thresholds, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Datasets from mountainous and flat urbanized areas were selected to test the progressive morphological filter. The results show that the filter can remove most of the nonground points effectively.

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A progressive morphological filter for removing nonground measurements from airborne LIDAR data

Airborne laser scanning (ALS) is an active remote sensing technique providing range data as 3D point clouds. This paper aims at presenting a survey of the literature related to such techniques, with emphasis on the new sensors called full-waveform lidar systems. Indeed, an emitted laser pulse interacts with complex natural and man-made objects leading to a temporal distortion of the returned energy profile. The new technology of full-waveform laser scanning systems permits one to digitize the complete waveform of each backscattered pulse. Full-waveform lidar data give more control to an end user in the interpretation process of the physical measurement and provide additional information about the structure and the physical backscattering characteristics of the illuminated surfaces. In this paper, the theoretical principles of full-waveform airborne laser scanning are first described. Afterwards, a review of the main sensors as well as signal processing techniques are presented. We then discuss the interpretation of full-waveform measures with special interest on vegetated and urban areas.

Full-waveform topographic lidar : State-of-the-art

In this study we use a technique referred to as Gaussian decomposition for processing and calibrating data acquired with a novel small-footprint airborne laser scanner that digitises the complete waveform of the laser pulses scattered back from the Earth's surface. This paper presents the theoretical basis for modelling the waveform as a series of Gaussian pulses. In this way the range, amplitude, and width are provided for each pulse. Using external reference targets it is also possible to calibrate the data. The calibration equation takes into account the range, the amplitude, and pulse width and provides estimates of the backscatter cross-section of each target. The applicability of this technique is demonstrated based on RIEGL LMS-Q560 data acquired over the city of Vienna.

Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner

Both airborne and terrestrial laser scanners are used to capture large point clouds of the objects under study Although for some applications, direct measurements in the point clouds may already suffice, most applications require an automatic processing of the point clouds to extract information on the shape of the recorded objects This processing often involves the recognition of specific geometric shapes or more general smooth surfaces This paper reviews several techniques that can be used to recognise such structures in point clouds Applications in industry, urban planning, water management and forestry document the usefulness of these techniques 1 This paper was written while the first author was with the Delft University of Technology

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Recognising structure in laser scanning point clouds

A point set obtained by laser altimetry represents points from not only the ground surface but also objects found on it. For civil works applications points representing the surface of non-ground objects have to be removed from the point set in a filtering process. This paper describes modifications made to an existing “slope based” filtering algorithm, and presents some results obtained from the use of the filter. The “slope based” filter operates on the assumption that terrain slopes do not rise above a certain threshold, and that features in the data that have slopes above this threshold do not belong to the natural terrain surface. However, this assumption limits the use of the filter to terrain with gentle slopes. To overcome this limitation, the filter was modified in manner that the threshold varies with respect to the slope of the terrain. The results of tests carried out using the modified filter confirm that the modification reduces the number of Type I errors (ground points in steep terrain are not filtered off). Further numerical comparison of the filter output with a reference data set for the same site (obtained photogrammetrically) show that the filter generates relatively minimal Type II errors. The output of the modified slope filter was also compared with the output from a filtering found in the commercial software package, “Terrascan”.

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Filtering of laser altimetry data using a slope adaptive filter

The extraction of points on the bare Earth from point clouds acquired by airborne laser scanning is the most time consuming and expensive part in the production of digital elevation models with laser scanning. Current algorithms for filtering point clouds assume the Earth’s surface to be continuous in all directions. This assumption leads to smoothed terrain representations in case of height discontinuities as they are often found in urban environments. This paper presents a new approach to filtering point clouds in which the point cloud is segmented into smooth segments that may still contain height discontinuities. The resulting segments are subsequently classified bare earth or object surfaces based on the geometric relationships with the surrounding segments. The paper demonstrates the advantages of segment-based classification with an analysis of data sets used in the ISPRS filter test.

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Filtering of airborne laser scanner data based on segmented point clouds

A method for detecting urban structures in an irregularly spaced point-cloud of an urban landscape is proposed. The method is especially designed for detecting structures that are extensions to the bare-earth (e.g., bridges, ramps, etc.,). The method involves a segmentation of a point-cloud followed by a classification. Both the segmentation and classification of the data are based on the analysis of a data structure in which the point-cloud is represented as an orthogonal set of profiles. Also proposed is a conceptual and logical model of the landscape for the structure detection problem.

G. Sithole

Papers

Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds

Recognising structure in laser scanning point clouds

Filtering of laser altimetry data using a slope adaptive filter

Filtering of airborne laser scanner data based on segmented point clouds

Automatic structure detection in a point-cloud of an urban landscape