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DeepMesh:可差异化的隔离表面提取

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    本研究介绍了DeepMesh,这是一种新的方法,可以从深隐性场中生成可微分的3D网格. 这一突破允许对3D形状表示进行端到端的训练,克服了以前方法的局限性.

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    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形状表示 3D形状表示

    背景情况:

    • 连续的深隐形场提供高分辨率的3D表面建模,没有网格限制.
    • 目前的方法在网格转换方面遇到了困难,原因是像行进立方体这样的非可区分算法.

    研究的目的:

    • 开发一种可微分的方法,从深暗场生成显式表面网格.
    • 为了实现对3D形状生成和操纵的端到端培训.

    主要方法:

    • 引入了一种新的方法来区分3D表面样本位置与隐含场的区别.
    • 开发了DeepMesh,这是一个端到端可微分网格表示,能够进行拓变化.

    主要成果:

    • 展示了一个可差异化的管道,用于将隐性字段转换为显式网格.
    • 在单视图3D重建,形状优化和完整场景重建方面取得了最先进的结果.

    结论:

    • DeepMesh为集成隐性场域表示与显式网状管道提供了重大进步.
    • 端到端可差分参数化在各种3D计算机视觉应用中提供了竞争优势.