An automatic method for the measurement of coarse particle movement in a mountain riverbed

Walicka, Agata; Pfeifer, Norbert; Borkowski, Andrzej; Jóźków, Grzegorz

doi:10.1016/j.measurement.2021.109029

Cited by 6 publications

(6 citation statements)

References 49 publications

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“…Although research has been done on the 3D point cloud registration, they have been studied on a limited set of data in a specific scenario [41]. In addition, most of the research is based on data collected by themselves and designed for a particular application and not accessible to other researchers.…”

Section: B Research Aimsmentioning

confidence: 99%

“…In addition, most of the research is based on data collected by themselves and designed for a particular application and not accessible to other researchers. Recently, real data sets in remote sensing and photogrammetry have been presented for point cloud registration [41], [42]. These data are produced by different methods and have a variety of environments.…”

Section: B Research Aimsmentioning

confidence: 99%

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A Novel 3-D Local DAISY-Style Descriptor to Reduce the Effect of Point Displacement Error in Point Cloud Registration

Ghorbani

Ebadi

Sedaghat

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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3D point clouds are widely considered for applications in different fields. Various methods have been proposed to generate point cloud data: LIDAR and image matching from static and mobile platforms, including, e.g., Terrestrial Laser S canning (TLS ). With multiple point clouds from stationary platforms, point cloud registration is a crucial and fundamental issue. A standard approach is a point-based registration, which relies on pairs of corresponding points in twopoint clouds. Therefore, a necessary step in point-based registration is the construction of 3D local descriptors. One of the (many) challenges that will specifically affect the performance of local descriptors with local spatial information is the point displacement error. This error is caused by the difference in the distributions of points surrounding a (potentially) corresponding center point in the two-point clouds. It can occur for various reasons such as i) distortions caused by the sensors recording the data, ii) moving objects, iii) varying density of point cloud, iv) change of viewing angle, and v) different of the sensors. The purpose of this article is to develop a new 3D local descriptor reducing the effect of this type of error in point cloud coarse registration. The approach includes an improved Local Reference Frame (LRF) and a new geometric arrangement in point cloud space for the 3D local descriptor. Inspired by the 2D DAIS Y descriptor, a geometric arrangement is created to reduce the effect of the point displacement error. in addi tion, directional histograms are considered as features. Investigations are performed for point clouds from challenging environments, which are publicly available. The results of this study show the high performance of the proposed approach for point cloud registration, especially in more challenging and noisy environments.

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Section: B Research Aimsmentioning

confidence: 99%

Section: B Research Aimsmentioning

confidence: 99%

A Novel 3-D Local DAISY-Style Descriptor to Reduce the Effect of Point Displacement Error in Point Cloud Registration

Ghorbani

Ebadi

Sedaghat

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…However, the processing of DEMs does not allow for obtaining complete information about all changes taking place in the river bed. For instance, the thorough investigation of the sediment transport, including the movement path of each grain, requires the studies to be performed directly on the point cloud [7]- [9], [5]. What is more, a number of the applications require segmentation of individual grains from the entire data.…”

Section: Examples Of Such Studies Include Estimation Of the Volume Ofmentioning

confidence: 99%

“…The analysis can provide information about movement parameters, including translation and rotation parameters, for each grain. However, the individual grains need to be segmented to achieve the goal [7].…”

Section: Examples Of Such Studies Include Estimation Of the Volume Ofmentioning

confidence: 99%

“…Delineating one small point cloud per particles offers the possibility to monitor sediment transport on the level of individual grains, as suggested in [8]. It requires multiple measurement campaigns and a method to match point clouds of (moved) grains between epochs [7]. The point cloud based approach allows matching the shape, not only under translation but also under rotation, in order to automatically and objectively obtain individual particle movement.…”

Section: Examples Of Such Studies Include Estimation Of the Volume Ofmentioning

confidence: 99%

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Automatic Segmentation of Individual Grains From a Terrestrial Laser Scanning Point Cloud of a Mountain River Bed

Walicka

Pfeifer

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, we propose a method for instance segmentation of individual grains from a terrestrial laser scanning point cloud representing a mountain river bed. The method was designed as a classification followed by segmentation approach. The binary classification into either points representing river bed or grains is performed using the Random Forest algorithm. The point cloud is classified based only on geometrical features calculated for a local, spherical neighborhood. A multi-size neighborhood approach was used together with the feature selection method that is based on correlation analysis. The final classification was performed using a set of features calculated for the neighborhood size of 5 cm, 15 cm, and 20 cm. The achieved classification results have the overall accuracy of 85% to 95%, depending on the test site. The segmentation is performed using the DBSCAN algorithm in order to cluster the point cloud based on Euclidean distances between points. The performed experiments showed that the proposed method enables us to correctly delineate 67% to 88% of grains, depending on the test site. However, the resulting point cloud-based completeness expressed as Jaccard index is similar for each of the test sites and is approximately 88%. Moreover, the proposed method proved that it is robust to the shadowing effect.

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Pro+: Automated protrusion and critical shear stress estimates from 3D point clouds of gravel beds

Yager,

Shim,

Hodge

et al. 2024

Earth Surf Processes Landf

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The dimensionless critical shear stress (τ*c) needed for the onset of sediment motion is important for a range of studies from river restoration projects to landscape evolution calculations. Many studies simply assume a τ*c value within the large range of scatter observed in gravel‐bedded rivers because direct field estimates are difficult to obtain. Informed choices of reach‐scale τ*c values could instead be obtained from force balance calculations that include particle‐scale bed structure and flow conditions. Particle‐scale bed structure is also difficult to measure, precluding wide adoption of such force‐balance τ*c values. Recent studies have demonstrated that bed grain size distributions (GSD) can be determined from detailed point clouds (e.g. using G3Point open‐source software). We build on these point cloud methods to introduce Pro+, software that estimates particle‐scale protrusion distributions and τ*c for each grain size and for the entire bed using a force‐balance model. We validated G3Point and Pro+ using two laboratory flume experiments with different grain size distributions and bed topographies. Commonly used definitions of protrusion may not produce representative τ*c distributions, and Pro+ includes new protrusion definitions to better include flow and bed structure influences on particle mobility. The combined G3Point/Pro+ provided accurate grain size, protrusion and τ*c distributions with simple GSD calibration. The largest source of error in protrusion and τ*c distributions were from incorrect grain boundaries and grain locations in G3Point, and calibration of grain software beyond comparing GSD is likely needed. Pro+ can be coupled with grain identifying software and relatively easily obtainable data to provide informed estimates of τ*c. These could replace arbitrary choices of τ*c and potentially improve channel stability and sediment transport estimates.

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An automatic method for the measurement of coarse particle movement in a mountain riverbed

Cited by 6 publications

References 49 publications

A Novel 3-D Local DAISY-Style Descriptor to Reduce the Effect of Point Displacement Error in Point Cloud Registration

A Novel 3-D Local DAISY-Style Descriptor to Reduce the Effect of Point Displacement Error in Point Cloud Registration

Automatic Segmentation of Individual Grains From a Terrestrial Laser Scanning Point Cloud of a Mountain River Bed

Pro+: Automated protrusion and critical shear stress estimates from 3D point clouds of gravel beds

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