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May, 2023
StEik: 稳定神经符号距离函数优化和更精细的形状表示
StEik: Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation
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Huizong Yang, Yuxin Sun, Ganesh Sundaramoorthi, Anthony Yezzi
TL;DR
提出了一种基于偏微分方程的正则化方法和二次层结构的新范式,用于稳定和提高隐式神经形状表示的质量,并在多个基准数据集上展示了显著的改进。
Abstract
We present new insights and a novel paradigm (StEik) for learning
implicit neural representations
(INR) of shapes. In particular, we shed light on the popular
eikonal loss
used for imposing a signed distance func
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