BriefGPT.xyz
Feb, 2020
学习形状的隐式几何规则化
Implicit Geometric Regularization for Learning Shapes
HTML
PDF
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, Yaron Lipman
TL;DR
本文提出了一种从原始数据(即点云)中直接计算高保真度隐式神经表示的新范式,它鼓励神经网络在输入点云上消失并具有单位范数梯度的简单损失函数具有几何正则化特性,利用神经网络表示任务的表面形状的零水平集,避免不良零损失解,实验表明该方法与之前的方法相比具有更高的细节和保真度。
Abstract
Representing shapes as level sets of
neural networks
has been recently proved to be useful for different
shape analysis
and reconstruction tasks So far, such representations were computed using either: (i) pre-co
→