Implicit Neural Representations with Periodic Activation Functions

CVPR 2020 Vincent SitzmannJulien N. P. MartelAlexander W. BergmanDavid B. LindellGordon Wetzstein

Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional representations. However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations... (read more)

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