WavePlanes: A compact Wavelet representation for Dynamic Neural Radiance Fields
CoRR(2023)
摘要
Dynamic Neural Radiance Fields (Dynamic NeRF) enhance NeRF technology to
model moving scenes. However, they are resource intensive and challenging to
compress. To address this issue, this paper presents WavePlanes, a fast and
more compact explicit model. We propose a multi-scale space and space-time
feature plane representation using N-level 2-D wavelet coefficients. The
inverse discrete wavelet transform reconstructs N feature signals at varying
detail, which are linearly decoded to approximate the color and density of
volumes in a 4-D grid. Exploiting the sparsity of wavelet coefficients, we
compress a Hash Map containing only non-zero coefficients and their locations
on each plane. This results in a compressed model size of 12 MB. Compared with
state-of-the-art plane-based models, WavePlanes is up to 15x smaller, less
computationally demanding and achieves comparable results in as little as one
hour of training - without requiring custom CUDA code or high performance
computing resources. Additionally, we propose new feature fusion schemes that
work as well as previously proposed schemes while providing greater
interpretability. Our code is available at:
https://github.com/azzarelli/waveplanes/
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