New Approaches for Estimating the Local Point Density and its Impact on Lidar Data Segmentation

Lari, Zahra; Habib, Ayman

doi:10.14358/pers.79.2.195

Cited by 24 publications

(16 citation statements)

References 8 publications

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“…Finally, this point cloud characterization procedure is concluded by the estimation of local point density variations along locally classified and represented planar neighborhoods. In this research, these variations are quantified using cylindrical buffers established based on a novel approach proposed by [53], while considering the 3D relationship among the points belonging to a local planar neighborhood and noise level in the point cloud. For individual points belonging to planar neighborhoods, the diameter of cylindrical buffer defined for local point density estimation is specified by the distance between the query point and its furthest neighboring point within that neighborhood.…”

Section: Point Cloud Characterization and Planar Features Detectionmentioning

confidence: 99%

A New Approach for Realistic 3D Reconstruction of Planar Surfaces from Laser Scanning Data and Imagery Collected Onboard Modern Low-Cost Aerial Mapping Systems

2017

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Abstract:Over the past few years, accurate 3D surface reconstruction using remotely-sensed data has been recognized as a prerequisite for different mapping, modelling, and monitoring applications. To fulfill the needs of these applications, necessary data are generally collected using various digital imaging systems. Among them, laser scanners have been acknowledged as a fast, accurate, and flexible technology for the acquisition of high density 3D spatial data. Despite their quick accessibility, the acquired 3D data using these systems does not provide semantic information about the nature of scanned surfaces. Hence, reliable processing techniques are employed to extract the required information for 3D surface reconstruction. Moreover, the extracted information from laser scanning data cannot be effectively utilized due to the lack of descriptive details. In order to provide a more realistic and accurate perception of the scanned scenes using laser scanning systems, a new approach for 3D reconstruction of planar surfaces is introduced in this paper. This approach aims to improve the interpretability of the extracted planar surfaces from laser scanning data using spectral information from overlapping imagery collected onboard modern low-cost aerial mapping systems, which are widely adopted nowadays. In this approach, the scanned planar surfaces using laser scanning systems are initially extracted through a novel segmentation procedure, and then textured using the acquired overlapping imagery. The implemented texturing technique, which intends to overcome the computational inefficiency of the previously-developed 3D reconstruction techniques, is performed in three steps. In the first step, the visibility of the extracted planar surfaces from laser scanning data within the collected images is investigated and a list of appropriate images for texturing each surface is established. Successively, an occlusion detection procedure is carried out to identify the occluded parts of these surfaces in the field of view of captured images. In the second step, visible/non-occluded parts of the planar surfaces are decomposed into segments that will be textured using individual images. Finally, a rendering procedure is accomplished to texture these parts using available images. Experimental results from overlapping laser scanning data and imagery collected onboard aerial mapping systems verify the feasibility of the proposed approach for efficient realistic 3D surface reconstruction.

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Section: Point Cloud Characterization and Planar Features Detectionmentioning

confidence: 99%

A New Approach for Realistic 3D Reconstruction of Planar Surfaces from Laser Scanning Data and Imagery Collected Onboard Modern Low-Cost Aerial Mapping Systems

2017

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“…Then, a PCA procedure is used to identify the nature of the defined local neighborhood-i.e., determine whether it is part of a planar, pole-like, or rough region [45]. Depending on the identified class, the corresponding LPD-pnts{m for thin pole-like features, pnts{m 2 for planar and cylindrical features, and pnts{m 3 for rough regions-and the corresponding LPS are estimated according to the established measures in Lari and Habib [38]. This step starts by forming a set of seed points that are randomly distributed within the point cloud in question.…”

Section: Data Structuring and Characterizationmentioning

confidence: 99%

“…Therefore, prior research has addressed the possibility of deriving QC measures that are not based on reference data. More specifically, Belton, Nurunnabi et al, and Lari and Habib [36][37][38] developed QC measures that make hypotheses regarding possible segmentation problems, propose procedures for detecting instances of such problems, and develop mitigation approaches to fix such problems without the need for having reference data. Over-segmentation-where a single planar/pole-like feature is segmented into more than one region, and under segmentation-where multiple planar/pole-like features are segmented as one region are key segmentation problems that have been considered by prior literature.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data

Habib

Lin

2016

Remote Sensing

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3D modeling of a given site is an important activity for a wide range of applications including urban planning, as-built mapping of industrial sites, heritage documentation, military simulation, and outdoor/indoor analysis of airflow. Point clouds, which could be either derived from passive or active imaging systems, are an important source for 3D modeling. Such point clouds need to undergo a sequence of data processing steps to derive the necessary information for the 3D modeling process. Segmentation is usually the first step in the data processing chain. This paper presents a region-growing multi-class simultaneous segmentation procedure, where planar, pole-like, and rough regions are identified while considering the internal characteristics (i.e., local point density/spacing and noise level) of the point cloud in question. The segmentation starts with point cloud organization into a kd-tree data structure and characterization process to estimate the local point density/spacing. Then, proceeding from randomly-distributed seed points, a set of seed regions is derived through distance-based region growing, which is followed by modeling of such seed regions into planar and pole-like features. Starting from optimally-selected seed regions, planar and pole-like features are then segmented. The paper also introduces a list of hypothesized artifacts/problems that might take place during the region-growing process. Finally, a quality control process is devised to detect, quantify, and mitigate instances of partially/fully misclassified planar and pole-like features. Experimental results from airborne and terrestrial laser scanning as well as image-based point clouds are presented to illustrate the performance of the proposed segmentation and quality control framework.

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“…Kim and Habib (2007) utilize cylinder neighbourhood concept for the segmentation of planar patches using parametric-domain methods successfully. Filin and Pfeifer (2006), Lari et al (2012) and Lari and Habib (2013) improve their planar surface segmentation results by considering the noise level and the physical shape of the associated surface. However, all mentioned papers take advantage of the parametric-domain methods that are computationally not efficient.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of planar surfaces in LiDAR point clouds of an electrical substation by exploring the structure of points neighbourhood

Arastounia

Lichti

2014

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

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ABSTRACT:According to the Department of Energy of the USA, today's electrical distribution system is 97.97% reliable. However, power outages and interruptions still impact many people. Many power outages are caused by animals coming into contact with the conductive elements of the electrical substations. This can be prevented by covering the conductive electrical objects with insulating materials. The design of these custom-built insulating covers requires a 3D as-built plan of the substation. This research aims to develop automated methods to create such a 3D as-built plan using terrestrial LiDAR data for which objects first need to be recognized in the LiDAR point clouds. This paper reports on the application of a new algorithm for the segmentation of planar surfaces found at electrical substations. The proposed approach is a region growing method that aggregates points based on their proximity to each other and their neighbourhood dispersion direction. PCA (principal components analysis) is also employed to segment planar surfaces in the electrical substation. In this research two different laser scanners, Leica HDS 6100 and Faro Focus 3D , were utilized to scan an electrical substation in Airdrie, a city located in north of Calgary, Canada. In this research, three subsets incorporating one subset of Leica dataset with approximately 1.7 million points and two subsets of the Faro dataset with 587 and 79 thousand points were utilized. The performance of our proposed method is compared with the performance of PCA by performing check point analysis and investigation of computational speed. Both methods managed to detect a great proportion of planar points (about 70%). However, the proposed method slightly outperformed PCA. 95% of the points that were segmented by both methods as planar points did actually lie on a planar surface. This exhibits the high ability of both methods to identify planar points. The results also indicate that the computational speed of our method is superior to that of PCA by 50%. It is concluded that our proposed method achieves better results with higher computational speed than PCA in the segmentation of planar surfaces.

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New Approaches for Estimating the Local Point Density and its Impact on Lidar Data Segmentation

Cited by 24 publications

References 8 publications

A New Approach for Realistic 3D Reconstruction of Planar Surfaces from Laser Scanning Data and Imagery Collected Onboard Modern Low-Cost Aerial Mapping Systems

A New Approach for Realistic 3D Reconstruction of Planar Surfaces from Laser Scanning Data and Imagery Collected Onboard Modern Low-Cost Aerial Mapping Systems

Multi-Class Simultaneous Adaptive Segmentation and Quality Control of Point Cloud Data

Segmentation of planar surfaces in LiDAR point clouds of an electrical substation by exploring the structure of points neighbourhood

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