Geometric Point Quality Assessment for the Automated, Markerless and Robust Registration of Unordered TLS Point Clouds

Weinmann, Martin; Jutzi, Boris

doi:10.5194/isprsannals-ii-3-w5-89-2015

Cited by 21 publications

(10 citation statements)

References 49 publications

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“…Measurement noise, which is the primary cause of systematic modelling errors and contributing to the increased measurement uncertainty [14], depends to a large extent on the characteristics of the scanned scene. Hence, measurement noises should be subjected to dedicated filtration algorithms [15]. The primary features facilitating the classification of points as measurement noise by filtration algorithms are the range of object occurrence, satisfied neighbourhood condition, and the intensity of the reflected beam.…”

Section: Introductionmentioning

confidence: 99%

TLS Measurement during Static Load Testing of a Railway Bridge

Gawronek¹,

Makuch²

2019

IJGI

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Terrestrial laser scanning (TLS) technology has become increasingly popular in investigating displacement and deformation of natural and anthropogenic objects. Regardless of the accuracy of deformation identification, TLS provides remote comprehensive information about the measured object in a short time. These features of TLS were why TLS measurement was used for a static load test of an old, steel railway bridge. The results of the measurement using the Z+F Imager 5010 scanner and traditional surveying methods (for improved georeferencing) were compared to results of precise reflectorless tacheometry and precise levelling. The analyses involved various procedures for the determination of displacement from 3D data (black & white target analysis, point cloud analysis, and mesh surface analysis) and the need to pre-process the 3D data was considered (georeferencing, automated filtering). The results demonstrate that TLS measurement can identify vertical displacement in line with the results of traditional measurements down to ±1 mm.

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Section: Introductionmentioning

confidence: 99%

TLS Measurement during Static Load Testing of a Railway Bridge

Gawronek¹,

Makuch²

2019

IJGI

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“…Furthermore, the volumetric behavior could result from noise effects resulting from limitations of the used sensor in terms of beam divergence or measurement accuracy, but also from specific characteristics of the observed scene in terms of object materials, surface reflectivity and surface roughness [22]. Besides these influencing factors, the scanning geometry in terms of the distance and orientation of object surfaces with respect to the used sensor might have to be considered as well [84,85].…”

mentioning

confidence: 99%

A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

Weinmann

Mallet³

et al. 2017

Remote Sensing

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Abstract:In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as "tree points" and "other points". The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the "tree points". This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D points among which 17.6% are labeled as "tree points". The derived results clearly reveal a semantic classification of high accuracy (up to 90.77%) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h).

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“…Zhou et al (2016) fix this problem by fine-tuning the computation of the plane parameters instead of coarse estimation. Weinmann and Jutzi (2015) further exploit this scan grid to derive various metrics for evaluating the quality of each point in a TLS scan. Barnea and Filin (2013) utilize the mean-shift algorithm to segment three images generated by range, normal, and colors respectively, followed by a refinement.…”

Section: Image-based Segmentationmentioning

confidence: 99%

Fast Edge Detection and Segmentation of Terrestrial Laser Scans Through Normal Variation Analysis

Che

2017

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

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ABSTRACT:Terrestrial Laser Scanning (TLS) utilizes light detection and ranging (lidar) to effectively and efficiently acquire point cloud data for a wide variety of applications. Segmentation is a common procedure of post-processing to group the point cloud into a number of clusters to simplify the data for the sequential modelling and analysis needed for most applications. This paper presents a novel method to rapidly segment TLS data based on edge detection and region growing. First, by computing the projected incidence angles and performing the normal variation analysis, the silhouette edges and intersection edges are separated from the smooth surfaces. Then a modified region growing algorithm groups the points lying on the same smooth surface. The proposed method efficiently exploits the gridded scan pattern utilized during acquisition of TLS data from most sensors and takes advantage of parallel programming to process approximately 1 million points per second. Moreover, the proposed segmentation does not require estimation of the normal at each point, which limits the errors in normal estimation propagating to segmentation. Both an indoor and outdoor scene are used for an experiment to demonstrate and discuss the effectiveness and robustness of the proposed segmentation method.

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Geometric Point Quality Assessment for the Automated, Markerless and Robust Registration of Unordered TLS Point Clouds

Cited by 21 publications

References 49 publications

TLS Measurement during Static Load Testing of a Railway Bridge

TLS Measurement during Static Load Testing of a Railway Bridge

A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

Fast Edge Detection and Segmentation of Terrestrial Laser Scans Through Normal Variation Analysis

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