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English(EN) Curve Skeletonization in Continuous domain for Meshes and Point Clouds

新的CSCD框架增强了连续域中的3D曲线骨架提取

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新的连续域曲线骨架提取框架CSCD，旨在克服离散方法的局限性。该框架提供了两种实现方式：用于网格的CSCD-M和用于点云的CSCD-PC。CSCD-M被认为是首个用于曲线骨架提取的内在方法，在Thingi10k数据集等基准测试中表现出与现有方法相当的性能。CSCD-PC在定性结果上也优于其他最先进的方法。 AI

影响 这项研究通过提供更准确、更鲁棒的形状表示，有望改进AI应用中的3D对象分析和处理。

排序理由 该集群包含一篇详细介绍新技术框架及其实现的学术论文。

在 arXiv cs.CV 阅读 →

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新的CSCD框架增强了连续域中的3D曲线骨架提取

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jai Bardhan, Ramya Hebbalaguppe, Aravind Udupa ·

    连续域曲面骨架提取用于网格和点云

    arXiv:2605.25921v1 Announce Type: cross Abstract: Advancements in 3D curve skeletonization are accelerating progress across a wide range of applications. However, developing robust skeletonization algorithms that capture intricate object details remains challenging. Skeletonizati…

  2. arXiv cs.CV TIER_1 English(EN) · Aravind Udupa ·

    连续域曲线骨架提取用于网格和点云

    Advancements in 3D curve skeletonization are accelerating progress across a wide range of applications. However, developing robust skeletonization algorithms that capture intricate object details remains challenging. Skeletonization via Local Separators (LS) offers an efficient g…

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