Clustering Quality Measures for Point Cloud Segmentation Tasks

Walczak, Jakub; Wojciechowski, Adam

doi:10.1007/978-3-030-00692-1_16

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…In fact, the dataset is supposed to be transformed so that the major planes are aligned with the coordinate system axes. It can be seen from Figure 3a that an indoor misaligned set usually has a larger Axis Aligned Bounding Box (AABB) than the properly oriented point cloud ( Figure 3b) [43]. In view of the above, a proper alignment procedure can be formulated as an optimization task, where the objective function takes into account the AABB dimensions (Equation (4)).…”

Section: Initial Point Cloud Alignment (Preprocessing)mentioning

confidence: 99%

Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression

2019

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Point cloud segmentation for planar surface detection is a valid problem of automatic laser scans analysis. It is widely exploited for many industrial remote sensing tasks, such as LIDAR city scanning, creating inventories of buildings, or object reconstruction. Many current methods rely on robustly calculated covariance and centroid for plane model estimation or global energy optimization. This is coupled with point cloud division strategies, based on uniform or regular space subdivision. These approaches result in many redundant divisions, plane maladjustments caused by outliers, and excessive number of processing iterations. In this paper, a new robust method of point clouds segmentation, based on histogram-driven hierarchical space division, inspired by kd-tree is presented. The proposed partition method produces results with a smaller oversegmentation rate. Moreover, state-of-the-art partitions often lead to nodes of low cardinality, which results in the rejection of many points. In the proposed method, the point rejection rate was reduced. Point cloud subdivision is followed by resilient plane estimation, using Mahalanobis distance with respect to seven cardinal points. These points were established based on eigenvectors of the covariance matrix of the considered point cluster. The proposed method shows high robustness and yields good quality metrics, much faster than a FAST-MCD approach. The overall results indicate improvements in terms of plane precision, plane recall, under-, and the over-segmentation rate with respect to the reference benchmark methods. Plane precision for the S3DIS dataset increased on average by 2.6pp and plane recall-by 3pp. Both over-and under-segmentation rates fell by 3.2pp and 4.3pp.been widely discussed in the literature. In this research, attention was paid to the area of modelfitting-based on plane detection methods for indoor scans, since this group of methods is currently the most successful [10][11][12][13]. Though thoroughly studied, model-based approaches still face generic problems related the difficulty in modelling the outlying points and performance in point clouds segmentation [14,15]. The attention was paid to indoor scans which similarly to all human-made structures may be usually reliably decomposed into geometrical primitives, particularly planes.This paper introduces a new model-fitting based method for indoor scans, relying on Mahalanobis distance (MD) and histogram-driven kd-like point cloud division. The hierarchical, well-balanced point cloud subdivision process enables a shallow yet sufficient partition, preventing the adjacent planar point sets from unintended splitting. The proposed strategy of adapting MD for determination of the set of core points, especially around corners and edges, results in a robust plane model fitting, ensuring higher resistance to outlying points and better than the state-of-the-art precision methods in detecting planar clusters. Related Works Space OrganizationEach point cloud segmentation method, regardless of the category it ...

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Section: Initial Point Cloud Alignment (Preprocessing)mentioning

confidence: 99%

Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression

2019

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“…Precision animal husbandry refers to the scientific breeding and management of live animals by arranging regular daily ration based on information technology. As an important aspect of intelligent agriculture, precision animal husbandry can improve the output benefit of animal husbandry products and ensure product quality and safety [1][2][3][4]. Large-scale, standardized breeding can effectively improve the output and profit of pigs, cattle, and sheep.…”

Section: Introductionmentioning

confidence: 99%

Parameter Measurement of Live Animals Based on the Mirror of Multiview Point Cloud

Zhang

Qin

Wang

2021

Computational Intelligence and Neuroscience

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Scale and standardization are essential to the prosperity of the breeding industry. During large-scale, standardized breeding, the selective breeding of good livestock breeds hinges on the accurate measurement of body parameters for live animals. However, the complex shooting environment brings several urgent problems, such as the missing of many local data in the point cloud and the difficulty in the automatic acquisition of body data. To solve these problems, this paper proposes a method for parameter measurement of live animals based on the mirror of multiview point cloud. Firstly, the acquisition and stitching principles were given for the multiview point cloud data on body parameters of live animals. Next, the authors presented a way to make up for the data missing areas in the point cloud. Finally, this paper acquires the body mirror data of live animals and scientifically calculates the body parameters. The proposed measurement method was proved effective through experiments.

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Normal Grouping Density Separation (NGDS): A Novel Object-Driven Indoor Point Cloud Partition Method

Walczak

Andrzejczak

Scherer

et al. 2020

Lecture Notes in Computer Science

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In this paper, we introduce the InLUT3D point cloud dataset, a comprehensive resource designed to advance the field of scene understanding in indoor environments. The dataset covers diverse spaces within the W7 faculty buildings of Lodz University of Technology, characterised by high-resolution laser-based point clouds and manual labelling. Alongside the dataset, we propose metrics and benchmarking guidelines essential for ensuring trustworthy and reproducible results in algorithm evaluation. We anticipate that the introduction of the InLUT3D dataset and its associated benchmarks will catalyse future advancements in 3D scene understanding, facilitating methodological rigour and inspiring new approaches in the field.

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Clustering Quality Measures for Point Cloud Segmentation Tasks

Cited by 3 publications

References 27 publications

Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression

Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression

Parameter Measurement of Live Animals Based on the Mirror of Multiview Point Cloud

Normal Grouping Density Separation (NGDS): A Novel Object-Driven Indoor Point Cloud Partition Method

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