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Found 7 papers, 0 papers with code
Continuous Levels of Detail for Light Field Networks
Furthermore, we use saliency-based importance sampling which enables our light field networks to distribute their capacity, particularly limited at lower LODs, towards representing the details viewers are most likely to focus on.
VIINTER: View Interpolation with Implicit Neural Representations of Images
We present VIINTER, a method for view interpolation by interpolating the implicit neural representation (INR) of the captured images.
Progressive Multi-scale Light Field Networks
Our progressive multi-scale light field network addresses aliasing by encoding smaller anti-aliased representations at its lower levels of detail.
PRIF: Primary Ray-based Implicit Function
We introduce a new implicit shape representation called Primary Ray-based Implicit Function (PRIF).
OmniSyn: Synthesizing 360 Videos with Wide-baseline Panoramas
Immersive maps such as Google Street View and Bing Streetside provide true-to-life views with a massive collection of panoramas.
Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching
Federated learning (FL) is a popular paradigm for private and collaborative model training on the edge.
SIGNET: Efficient Neural Representation for Light Fields
We present a novel neural representation for light field content that enables compact storage and easy local reconstruction with high fidelity.
