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PINs: Progressive Implicit Networks for Multi-Scale Neural Representations. (arXiv:2202.04713v2 [cs.CV] UPDATED)
June 20, 2022, 1:13 a.m. | Zoe Landgraf, Alexander Sorkine Hornung, Ricardo Silveira Cabral
cs.CV updates on arXiv.org arxiv.org
Multi-layer perceptrons (MLP) have proven to be effective scene encoders when
combined with higher-dimensional projections of the input, commonly referred to
as \textit{positional encoding}. However, scenes with a wide frequency spectrum
remain a challenge: choosing high frequencies for positional encoding
introduces noise in low structure areas, while low frequencies result in poor
fitting of detailed regions. To address this, we propose a progressive
positional encoding, exposing a hierarchical MLP structure to incremental sets
of frequency encodings. Our model accurately reconstructs …
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