HYVE:用于神经距离场的混合顶点编码器
IEEE transactions on visualization and computer graphics
|January 28, 2026
概括
这项研究引入了一种用于3D形状表示的新型神经网络,可以在一次通过中准确编码形状. 该方法实现了优异的表面重建,提高了效率,参数更少.
科学领域:
- 计算机视觉 计算机视觉
- 3D几何处理处理 3D几何处理
- 神经网络的神经网络的神经网络
背景情况:
- 神经形状表示使用神经网络来编码3D几何.
- 现有的方法通常需要隐性代码优化,这是计算密集的.
研究的目的:
- 介绍一个神经网络架构,用于在单个前向传递中准确的3D形状编码.
- 为了提高表面重建质量和计算效率.
主要方法:
- 一个多尺度的混合系统,结合了基于图形和基于voxel的组件.
- 基于点的特征在网格上的新投影,对点密度不敏感.
- 培训网络来解决eikonal方程,使用零级集合进行培训和推理.
- 对于未明确的表面正常值,损失函数的修改.
主要成果:
- 实现了更光滑,更详细的3D形状重建,特别是在面向点云的情况下.
- 在各种数据集的表面重建中表现优于现有的基线.
- 证明了更高的计算效率和更少的参数数量.
结论:
- 拟议的神经网络架构为3D形状表示和重建提供了一种高效和有效的方法.
- 混合系统和基于eikonal方程的培训提供了强大的和准确的结果,即使是复杂的几何形状.
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