English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We proposed a novel graph convolutional neural network that could construct acoarse, sparse latent point cloud from a dense, raw point cloud. With a novelnon-isotropic convolution operation defined on irregular geometries, the modelthen can reconstruct the original point cloud from this latent cloud with finedetails. Furthermore, we proposed that it is even possible to perform particlesimulation using the latent cloud encoded from some simulated particle cloud(e.g. fluids), to accelerate the particle simulation process. Our model hasbeen tested on ShapeNetCore dataset for Auto-Encoding with a limited latentdimension and tested on a synthesis dataset for fluids simulation. We alsocompare the model with other state-of-the-art models, and severalvisualizations were done to intuitively understand the model.

Irregular Convolutional Auto-Encoder on Point Clouds