A Fast Point Cloud Segmentation Algorithm Based on Region Growth

Ma, Xiaofeng; Luo, Wei; Chen, Mingquan; Li, Jiahui; Yan, Xin; Zhang, Xia; Wei, Wei

doi:10.1109/icocn.2019.8934726

Cited by 18 publications

(7 citation statements)

References 6 publications

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“…The RANSAC algorithm is a model-based method of point cloud segmentation; however, the algorithm must manually define or select a model, usually a plane, sphere, or other geometry that can be represented by algebraic formulas [46]. The region growth algorithm is a classic point cloud segmentation method that measures the similarity between point clouds by combining features between N points or N area units and merging them [47].…”

Section: Resultsmentioning

confidence: 99%

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A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm

Zhao,

Ning,

Jia

et al. 2024

Applied Sciences

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Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, the complex and unstructured characteristics of road surveying point cloud data lead to traditional 3D point cloud segmentation. When traditional 3D point cloud algorithms extract unstructured road surfaces, over-segmentation and under-segmentation often occur, which affects efficiency and accuracy. To solve these problems, this study introduces an enhanced road extraction method that integrates supervoxel and trajectory information into a traditional region growing algorithm. The method involves two main steps: first, a supervoxel data structure is applied to reconstruct the original MLS point cloud data, which diminishes the calculation time of the point cloud feature vector and accelerates the merging speed of a similar region; second, the trajectory information of the vehicle is used to optimize the seed selection strategy of the regio growing algorithm, which improves the accuracy of road surface extraction. Finally, two typical highway section tests (flat road and slope road) were conducted to validate the positioning performance of the proposed algorithm in an MLS point cloud. The results show that, compared with three kinds of traditional road surface segmentation algorithms, our method achieves an average extraction recall and precision of 99.1% and 96.0%, and by calculating the recall and precision, an F1 score of 97.5% can be obtained to evaluate the performance of the proposed method, for both datasets. Additionally, our method exhibits an average road surface extraction time that is 45.0%, 50.3%, and 55.8% faster than those of the other three automated segmentation algorithms.

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Section: Resultsmentioning

confidence: 99%

“…Sci. 2024, 14, x FOR PEER REVIEW 17 of the similarity between point clouds by combining features between N points or N area units and merging them [47].…”

Section: Flat Road Datasetmentioning

confidence: 99%

A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm

Zhao,

Ning,

Jia

et al. 2024

Applied Sciences

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“…This research selected the region growing segmentation for point cloud as a reference for comparing the effects of various cloud segmentation methods. The region growing segmentation method is recognized for its flexibility, preservation of similarity, extensibility, computational efficiency, and interpretability, making it one of the most favored methods in point cloud segmentation [ 37 ]. The algorithm selects the seed point as the starting point and expands the seed region by adding neighboring points that meet specific conditions [ 38 ].…”

Section: Resultsmentioning

confidence: 99%

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network

Liu,

Hua,

Xue

et al. 2023

Sensors

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The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig’s back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding.

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“…Point cloud segmentation is a crucial task in point cloud processing. Early point cloud segmentation techniques primarily use geometric information features based on ground segmentation to achieve good segmentation results through hand-crafted feature clustering, model fitting, and region growing [10][11][12]. Although such point cloud segmentation methods produce some segmentation results, they have several limitations.…”

Section: Related Workmentioning

confidence: 99%

Point Cloud Based Semantic Segmentation Method for Unmanned Shuttle Bus

Wu,

Duan,

Ren

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The complexity of application scenarios and the enormous volume of point cloud data make it difficult to quickly and effectively segment the scenario only based on the point cloud. In this paper, to address the semantic segmentation for safety driving of unmanned shuttle buses, an accurate and effective point cloud-based semantic segmentation method is proposed for specified scenarios (such as campus). Firstly, we analyze the characteristic of the shuttle bus scenarios and propose to use ROI selection to reduce the total points in computation, and then propose an improved semantic segmentation model based on Cylinder3D, which improves mean Intersection over Union (mIoU) by 1.3% over the original model on SemanticKITTI data; then, a semantic category division method is proposed for road scenario of shuttle bus and practical application requirements, and then we further simplify the model to improve the efficiency without losing the accuracy. Finally, the nuScenes dataset and the real gathered campus scene data are used to validate and analyze the proposed method. The experimental results on the nuScenes dataset and our data demonstrate that the proposed method performs better than other point cloud semantic segmentation methods in terms of application requirements for unmanned shuttle buses. Which has a higher accuracy (82.73% in mIoU) and a higher computational efficiency (inference speed of 90 ms).

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A Fast Point Cloud Segmentation Algorithm Based on Region Growth

Cited by 18 publications

References 6 publications

A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm

A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network

Point Cloud Based Semantic Segmentation Method for Unmanned Shuttle Bus

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