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神经-IMLS:自主监督的隐式移动最小方形网络用于表面重建.

Zixiong Wang, Pengfei Wang, Peng-Shuai Wang

    IEEE transactions on visualization and computer graphics
    |June 8, 2023
    PubMed
    概括

    神经IMLS使用一种新的自我监督方法从杂的点云中重建表面. 这种方法学习了一种耐噪声签名距离函数 (SDF),用于准确的3D形状生成.

    科学领域:

    • 计算机视觉 计算机视觉
    • 几何深度学习 几何深度学习
    • 3D重建的3D重建

    背景情况:

    • 从点云中重建表面是很困难的,尤其是在杂的现实世界扫描中,缺乏正常信息.
    • 现有的方法与噪音,缺失的数据和未定向的点云作斗争.

    研究的目的:

    • 介绍Neural-IMLS,一种新的自我监督方法,用于强大的表面重建.
    • 直接从未定向的点云中学习一个耐噪声的签名距离函数 (SDF).

    主要方法:

    • 使用双表示学习方法,结合多层感知器 (MLP) 和隐式移动最小平方 (IMLS).
    • 采用相互规范化机制,MLP和IMLS相互增强SDF和正常估计.
    • 自主监督学习框架直接处理原始,无定向的点云.

    主要成果:

    • 神经IMLS成功地从杂和不完整的点云中重建了忠实的形状.
    • 该方法在各种合成和真实扫描基准上显示出稳定性.
    • 由于协同的MLP-IMLS相互作用,可以实现精确的几何细节和利的特征.

    结论:

    • 神经IMLS为3D表面重建提供了强大且耐噪声的解决方案.

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  • 自主监督,双重表示的学习方法有效地处理具有挑战性的输入数据.
  • 学习的SDF准确地近似了底层表面,即使有大量的噪音和缺失的数据.