Georeferenced Point Clouds: A Survey of Features and Point Cloud Management

Otepka, Johannes; Ghuffar, Sajid; Waldhauser, Christoph; Hochreiter, Ronald; Pfeifer, Norbert

doi:10.3390/ijgi2041038

Cited by 87 publications

(65 citation statements)

References 76 publications

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“…First, a number of attributes is computed for each point, using the paradigm of point cloud processing (Otepka et al, 2013). From these attributes different images are computed ("gridding") at a pixel size of 1m.…”

Section: Methodsmentioning

confidence: 99%

Potential of Full Waveform Airborne Laser Scanning Data for Urban Area Classification – Transfer of Classification Approaches Between Missions

Tran

Nguyen

Milenković

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.

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Section: Methodsmentioning

confidence: 99%

Potential of Full Waveform Airborne Laser Scanning Data for Urban Area Classification – Transfer of Classification Approaches Between Missions

Tran

Nguyen

Milenković

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…A pre-processing step is necessary to obtain a highly representative signal of the value measured as defined in [78]. Indeed, to avoid external influential sources that degrade the information, this step demands adapted techniques to minimize errors including noise, outliers and misalignment.…”

Section: Point Cloud Data Acquisition and Pre-processingmentioning

confidence: 99%

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

Poux

Neuville

Wersch³

et al. 2017

Geosciences

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Digital investigations of the real world through point clouds and derivatives are changing how curators, cultural heritage researchers and archaeologists work and collaborate. To progressively aggregate expertise and enhance the working proficiency of all professionals, virtual reconstructions demand adapted tools to facilitate knowledge dissemination. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. In this paper, we review the state of the art of point cloud integration within archaeological applications, giving an overview of 3D technologies for heritage, digital exploitation and case studies showing the assimilation status within 3D GIS. Identified issues and new perspectives are addressed through a knowledge-based point cloud processing framework for multi-sensory data, and illustrated on mosaics and quasi-planar objects. A new acquisition, pre-processing, segmentation and ontology-based classification method on hybrid point clouds from both terrestrial laser scanning and dense image matching is proposed to enable reasoning for information extraction. Experiments in detection and semantic enrichment show promising results of 94% correct semantization. Then, we integrate the metadata in an archaeological smart point cloud data structure allowing spatio-semantic queries related to CIDOC-CRM. Finally, a WebGL prototype is presented that leads to efficient communication between actors by proposing optimal 3D data visualizations as a basis on which interaction can grow.

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“…The idea of using symmetrically placed neighbors is not new, see (Otepka et al, 2013) for an overview. For example (Chaudhuri, 1996) defines the barycentric neighborhood, where the distance between neighbors and their centroid becomes as small as possible.…”

Section: Interpolation Of the Height Mapmentioning

confidence: 99%

Roof Reconstruction From Airborne Laser Scanning Data Based on Image Processing Methods

Goebbels¹,

Pohle-Fröhlich²

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The paper presents a new data-driven approach to generate CityGML building models from airborne laser scanning data. The approach is based on image processing methods applied to an interpolated height map and avoids shortcomings of established methods for plane detection like Hough transform or RANSAC algorithms on point clouds. The improvement originates in an interpolation algorithm that generates a height map from sparse point cloud data by preserving ridge lines and step edges of roofs. Roof planes then are detected by clustering the height map's gradient angles, parameterizations of planes are estimated and used to filter out noise around ridge lines. On that basis, a raster representation of roof facets is generated. Then roof polygons are determined from region outlines, connected to a roof boundary graph, and simplified. Whereas the method is not limited to churches, the method's performance is primarily tested for church roofs of the German city of Krefeld because of their complexity. To eliminate inaccuracies of spires, contours of towers are detected additionally, and spires are rendered as solids of revolution. In our experiments, the new data-driven method lead to significantly better building models than the previously applied model-driven approach.

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Georeferenced Point Clouds: A Survey of Features and Point Cloud Management

Cited by 87 publications

References 76 publications

Potential of Full Waveform Airborne Laser Scanning Data for Urban Area Classification – Transfer of Classification Approaches Between Missions

Potential of Full Waveform Airborne Laser Scanning Data for Urban Area Classification – Transfer of Classification Approaches Between Missions

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

Roof Reconstruction From Airborne Laser Scanning Data Based on Image Processing Methods

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