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

Walicka, Agata; Pfeifer, Norbert

doi:10.1109/jstars.2022.3141892

Cited by 6 publications

(3 citation statements)

References 61 publications

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“…Once again, this limits bias and time spent in the field and allows remote areas to be characterized. G3Point shares some common objectives with the automatic method developed by Walicka and Pfeifer (2022), which directly segments grains from 3D point clouds. It uses a random forest algorithm to classify grains and then a DB-SCAN algorithm to segment each grain individually.…”

Section: Comparison Of G3point With Previous Methodsmentioning

confidence: 99%

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Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds

et al. 2022

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Abstract. The grain-scale morphology and size distribution of sediments are important factors controlling the erosion efficiency, sediment transport and the aquatic ecosystem quality. In turn, characterizing the spatial evolution of grain size and shape can help understand the dynamics of erosion and sediment transport in coastal, hillslope and fluvial environments. However, the size distribution of sediments is generally assessed using insufficiently representative field measurements, and determining the grain-scale shape of sediments remains a real challenge in geomorphology. Here we determine the size distribution and grain-scale shape of sediments located in coastal and river environments with a new methodology based on the segmentation and geometric fitting of 3D point clouds. Point cloud segmentation of individual grains is performed using a watershed algorithm applied here to 3D point clouds. Once the grains are segmented into several sub-clouds, each grain-scale morphology is determined by fitting a 3D geometrical model applied to each sub-cloud. If different geometrical models can be tested, this study focuses mostly on ellipsoids to describe the geometry of grains. G3Point is a semi-automatic approach that requires a trial-and-error approach to determine the best combination of parameter values. Validation of the results is performed either by comparing the obtained size distribution to independent measurements (e.g., hand measurements) or by visually inspecting the quality of the segmented grains. The main benefits of this semi-automatic and non-destructive method are that it provides access to (1) an un-biased estimate of surface grain-size distribution on a large range of scales, from centimeters to meters; (2) a very large number of data, mostly limited by the number of grains in the point cloud data set; (3) the 3D morphology of grains, in turn allowing the development of new metrics that characterize the size and shape of grains; and (4) the in situ orientation and organization of grains. The main limit of this method is that it is only able to detect grains with a characteristic size significantly greater than the resolution of the point cloud.

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Section: Comparison Of G3point With Previous Methodsmentioning

confidence: 99%

“…Building on this opportunity, Chen et al (2020) recently developed a deep-learning workflow to segment grains based on structure-from-motion (SfM) data. Walicka and Pfeifer (2022) also successfully applied a DBSCAN (density-based spatial clustering of applications with noise, see Ester et al, 1996) algorithm to segment grains.…”

Section: Introductionmentioning

confidence: 99%

Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds

et al. 2022

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“…Additionally, other famous techniques have been applied to processing obstacles in point clouds. These include density-based algorithms such as k-means clustering [16], Euclidean clustering [17], and density-based spatial clustering (DSC) [18]. These algorithms aim to group points within a certain threshold around a central point into clusters, which are then used to detect obstacles based on cluster characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Novel High-Precision Railway Obstacle Detection Algorithm Based on 3D LiDAR

Nan,

Zhu,

Zhang

et al. 2024

Sensors

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This article presents a high-precision obstacle detection algorithm using 3D mechanical LiDAR to meet railway safety requirements. To address the potential errors in the point cloud, we propose a calibration method based on projection and a novel rail extraction algorithm that effectively handles terrain variations and preserves the point cloud characteristics of the track area. We address the limitations of the traditional process involving fixed Euclidean thresholds by proposing a modulation function based on directional density variations to adjust the threshold dynamically. Finally, using PCA and local-ICP, we conduct feature analysis and classification of the clustered data to obtain the obstacle clusters. We conducted continuous experiments on the testing site, and the results showed that our system and algorithm achieved an STDR (stable detection rate) of over 95% for obstacles with a size of 15 cm × 15 cm × 15 cm in the range of ±25 m; at the same time, for obstacles of 10 cm × 10 cm × 10 cm, an STDR of over 80% was achieved within a range of ±20 m. This research provides a possible solution and approach for railway security via obstacle detection.

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Gravel automatic sieving method fusing macroscopic and microscopic characteristics

Gao,

Zhang,

et al. 2024

International Journal of Sediment Research

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

Cited by 6 publications

References 61 publications

Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds

Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds

A Novel High-Precision Railway Obstacle Detection Algorithm Based on 3D LiDAR

Gravel automatic sieving method fusing macroscopic and microscopic characteristics

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