Curvature-Variation-Inspired Sampling for Point Cloud Classification and Segmentation

Zhu, Lei; Chen, Weinan; Lin, Xubin; He, Li; Guan, Yisheng

doi:10.1109/lsp.2022.3200585

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…However, the uniform downsampling method, which is used in PPF, has its shortcomings. According to [29], contour features are not apparent, and the matching effect in feature prominent parts is unsatisfactory. To address this issue, we propose a new downsampling method, based on curvature, that highlights geometric features during the downsampling process.…”

Section: Proposed Methodsmentioning

confidence: 99%

6D Pose Estimation Method Using Curvature-Enhanced Point-Pair Features

Liu,

Feng

2023

IEEE Access

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Pose estimation has garnered significant attention in recent years and has found extensive application in fields such as autonomous driving, robotics, and augmented reality. In the current research, point cloud recognition algorithms based on point-pair-features have been shown to be effective in recognizing objects and pose estimation, but redundant points included in the characterization of model features degrade the recognition performance and computational efficiency of the algorithms. To address this issue, this paper introduces curvature features to filter out unnecessary points and enhance the expression of model features. The resulting global model description is stored in a hash table, and the estimated pose is obtained through the combination of curvature-weighted voting and the Iterative Closest Point (ICP) algorithm for optimization. Additionally, a background removal technique is proposed for fixed usage scenarios, which significantly improves operational efficiency in real-world situations.Experimental results using various datasets and real environments demonstrate that the proposed approach reduces redundancy, improves point-pair feature (PPF) expression, and enhances recognition rate and matching speed by 4.7% and 46.7%, respectively.INDEX TERMS pose estimation, point pair features, curvature, point cloud.

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Section: Proposed Methodsmentioning

confidence: 99%

6D Pose Estimation Method Using Curvature-Enhanced Point-Pair Features

Liu,

Feng

2023

IEEE Access

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“…Data points that have closer spatial curvatures are more likely to possess similar attribute values. Therefore, when applying Gaussian mixture models for spatial clustering, it is advisable to incorporate relevant spatial curvature constraints to optimize the clustering algorithm [22][23][24].…”

Section: Curvature-based Spatial Neighborhood Information Functionmentioning

confidence: 99%

Clustering and Segmentation of Adhesive Pests in Apple Orchards Based on GMM-DC

Wang,

Liu,

Ren

et al. 2023

Agronomy

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The segmentation of individual pests is a prerequisite for pest feature extraction and identification. To address the issue of pest adhesion in the apple orchard pest identification process, this research proposed a pest adhesion image segmentation method based on Gaussian Mixture Model with Density and Curvature Weighting (GMM-DC). First, in the HSV color space, an image was desaturated by adjusting the hue and inverting to mitigate threshold crossing points. Subsequently, threshold segmentation and contour selection methods were used to separate the image background. Next, a shape factor was introduced to determine the regions and quantities of adhering pests, thereby determining the number of model clustering clusters. Then, point cloud reconstruction was performed based on the color and spatial distribution features of the pests. To construct the GMM-DC segmentation model, a spatial density (SD) and spatial curvature (SC) information function were designed and embedded in the GMM. Finally, experimental analysis was conducted on the collected apple orchard pest images. The results showed that GMM-DC achieved an average accurate segmentation rate of 95.75%, an average over-segmentation rate of 2.83%, and an average under-segmentation rate of 1.42%. These results significantly outperformed traditional image segmentation methods. In addition, the original and improved Mask R-CNN models were used as recognition models, and the mean Average Precision was used as the evaluation metric. Recognition experiments were conducted on pest images with and without the proposed method. The results show the mean Average Precision for pest images segmented with the proposed method as 92.43% and 96.75%. This indicates an improvement of 13.01% and 12.18% in average recognition accuracy, respectively. The experimental results demonstrate that this method provides a theoretical and methodological foundation for accurate pest identification in orchards.

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Sampling point planning method for aero-engine blade profile based on CMM trigger probe

Shi,

Luo

2024

Int J Adv Manuf Technol

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Curvature-Variation-Inspired Sampling for Point Cloud Classification and Segmentation

Cited by 9 publications

References 21 publications

6D Pose Estimation Method Using Curvature-Enhanced Point-Pair Features

6D Pose Estimation Method Using Curvature-Enhanced Point-Pair Features

Clustering and Segmentation of Adhesive Pests in Apple Orchards Based on GMM-DC

Sampling point planning method for aero-engine blade profile based on CMM trigger probe

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