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标题
RESEARCH ON IMPROVED REGION GROWING POINT CLOUD ALGORITHM
相关领域
云计算 计算机科学 点云 算法 点(几何)
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DOI
10.5194/isprs-archives-XLII-3-W10-153-2020 doi
其它 The effective segmentation of point clouds is a prerequisite for surface reconstruction, blind spot repair, and so on. Among them, regional growth is widely used due to its simple and easy to implement algorithm. However, the traditional regional growth segmentation algorithm often causes problems such as over-segmentation or voiding of the segmentation result due to the instability of the local features of the point cloud or the unreasonable selection of the initial seed nodes. In view of the above shortcomings, this paper proposes an improved region growing point cloud algorithm. Firstly, by calculating the Gaussian curvature and the average curvature of the point cloud data and sorting them, and setting the minimum curvature point as the seed node, the total number of clusters is reduced, and the quality of the classification result is improved. Secondly, the growth of the point cloud region growth criterion is determined by combining the normal angles.
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如约而至 在 2021-06-30 14:27:04 发布,悬赏 10 积分
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    如约而至 如约而至 求助人 Lv6 进行了留言

    @852 天行健传的不是对的嚒?
    看错了 看到是2019的就
  • 2年前

    852 852 Lv12 进行了留言

    @如约而至 The effective segmentation of point clouds is a prerequisite for surface reconstruction, blind spot repair, and so on. Among them, regional growth is widely used due to its simple and easy to implement algorithm. However, the traditional regional growth segmentation algorithm often causes problems such as over-segmentation or voiding of the segmentation result due to the instability of the local features of the point cloud or the unreasonable selection of the initial seed nodes. In view of the above shortcomings, this paper proposes an improved region growing point cloud algorithm. Firstly, by calculating the Gaussian curvature and the average curvature of the point cloud data and sorting them, and setting the minimum curvature point as the seed node, the total number of clusters is reduced, and the quality of the classification result is improved. Secondly, the growth of the point cloud region growth criterion is determined by combining the normal angles.
    天行健传的不是对的嚒?
  • 2年前

    如约而至 如约而至 求助人 Lv6 驳回了 天行健 上传的文件

    说明
    The effective segmentation of point clouds is a prerequisite for surface reconstruction, blind spot repair, and so on. Among them, regional growth is widely used due to its simple and easy to implement algorithm. However, the traditional regional growth segmentation algorithm often causes problems such as over-segmentation or voiding of the segmentation result due to the instability of the local features of the point cloud or the unreasonable selection of the initial seed nodes. In view of the above shortcomings, this paper proposes an improved region growing point cloud algorithm. Firstly, by calculating the Gaussian curvature and the average curvature of the point cloud data and sorting them, and setting the minimum curvature point as the seed node, the total number of clusters is reduced, and the quality of the classification result is improved. Secondly, the growth of the point cloud region growth criterion is determined by combining the normal angles.
  • 2年前

    天行健 天行健 Lv7 上传了文件

    已驳回 out.pdf (1.39 MB)
  • 2年前

    如约而至 如约而至 求助人 Lv6 发起了本次求助

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