Representing visual signals by coordinate-based deep fully-connected networks
has been shown advantageous in fitting complex details and solving inverse
problems than discrete grid-based representation. However, acquiring such a
continuous Implicit Neural Representation (INR) requires tedious per-scene
training on tons of signal measurements, which limits its practicality. In this
paper, we present a generic INR framework that achieves both data and training
efficiency by learning a Neural Implicit Dictionary (NID) from a data
collection and representing INR as a functional combination of basis sampled
from the dictionary. Our NID assembles a group of coordinate-based subnetworks
which are tuned to span the desired function space. After training, one can
instantly and robustly acquire an unseen scene representation by solving the
coding coefficients. To parallelly optimize a large group of networks, we
borrow the idea from Mixture-of-Expert (MoE) to design and train our network
with a sparse gating mechanism. Our experiments show that, NID can improve
reconstruction of 2D images or 3D scenes by 2 orders of magnitude faster with
up to 98% less input data. We further demonstrate various applications of NID
in image inpainting and occlusion removal, which are considered to be
challenging with vanilla INR. Our codes are available in
this https URL

该研究论文提出了一种能够通过基于坐标的子网络来构建神经隐式词典 (NID) 并代表内在神经表征的框架，通过优化结果能够更快地重建 2D/3D 图像并解决遮挡问题。