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

Recent advances in deep generative models have led to immense progress in 3Dshape synthesis. While existing models are able to synthesize shapesrepresented as voxels, point-clouds, or implicit functions, these methods onlyindirectly enforce the plausibility of the final 3D shape surface. Here wepresent a 3D shape synthesis framework (SurfGen) that directly appliesadversarial training to the object surface. Our approach uses a differentiablespherical projection layer to capture and represent the explicit zeroisosurface of an implicit 3D generator as functions defined on the unit sphere.By processing the spherical representation of 3D object surfaces with aspherical CNN in an adversarial setting, our generator can better learn thestatistics of natural shape surfaces. We evaluate our model on large-scaleshape datasets, and demonstrate that the end-to-end trained model is capable ofgenerating high fidelity 3D shapes with diverse topology.

SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators