Bilateral grid is a new data structure that enables fast edge-aware image processing. It enables edge-aware image manipulations such as local tone mapping on high resolution images in real time.

Source: [Chen et al.](https://people.csail.mit.edu/sparis/publi/2007/siggraph/Chen_07_Bilateral_Grid.pdf)

Image source: [Chen et al.](https://people.csail.mit.edu/sparis/publi/2007/siggraph/Chen_07_Bilateral_Grid.pdf)

Bilateral Grid

Deep Bilateral Learning for Real-Time Image Enhancement

WEGL

Wasserstein Embedding for Graph Learning

**MeshGraphNet** is a framework for learning mesh-based simulations using [graph neural networks](https://paperswithcode.com/methods/category/graph-models). The model can be trained to pass messages on a mesh graph and to adapt the mesh discretization during forward simulation. The model uses an Encode-Process-Decode architecture trained with one-step supervision, and can be applied iteratively to generate long trajectories at inference time. The encoder transforms the input mesh $M^{t}$ into a graph, adding extra world-space edges. The processor performs several rounds of message passing along mesh edges and world edges, updating all node and edge embeddings. The decoder extracts the acceleration for each node, which is used to update the mesh to produce $M^{t+1}$.

Source	Wasserstein Embedding for Graph Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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What is: Wasserstein Embedding for Graph Learning?

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