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Found 6 papers, 1 papers with code
Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression
Light field is a type of image data that captures the 3D scene information by recording light rays emitted from a scene at various orientations.
Learning-based Spatial and Angular Information Separation for Light Field Compression
Light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations.
JAWS: Just A Wild Shot for Cinematic Transfer in Neural Radiance Fields
This paper presents JAWS, an optimization-driven approach that achieves the robust transfer of visual cinematic features from a reference in-the-wild video clip to a newly generated clip.
Distilled Low Rank Neural Radiance Field with Quantization for Light Field Compression
While existing compression methods encode the set of light field sub-aperture images, our proposed method learns an implicit scene representation in the form of a Neural Radiance Field (NeRF), which also enables view synthesis.
A learning-based view extrapolation method for axial super-resolution
Axial light field resolution refers to the ability to distinguish features at different depths by refocusing.
Learning Fused Pixel and Feature-Based View Reconstructions for Light Fields
In this paper, we present a learning-based framework for light field view synthesis from a subset of input views.
