Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate (spatial location ( x, y, z ) and viewing direction ( θ, ϕ )) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Extensive experiments have demonstrated its effectiveness in several video-related tasks, such as video compression and video inpainting. These simple yet essential changes could help the network easily fit high-frequency details.
The whole method includes conventional modules, like positional embedding, MLPs and CNNs, while also introduces AdaIN to enhance intermediate features. It naturally inherits the advantages of image-wise methods, and achieves excellent reconstruction performance with fast decoding speed. Instead, we propose a patch-wise solution, PS-NeRV, which represents videos as a function of patches and the corresponding patch coordinate. However, we argue that both the above pixel-wise and image-wise strategies are not favorable to video data. While some recent works have tried to directly reconstruct the whole image with CNNs. Classical INRs methods generally utilize MLPs to map input coordinates to output pixels.
#CALLSIGN PATCH STYLIZER HOW TO#
We study how to represent a video with implicit neural representations (INRs).
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