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/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

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

Zendy Tools

Zendy Open

The question of representation of 3D geometry is of vital importance when itcomes to leveraging the recent advances in the field of machine learning forgeometry processing tasks. For common unstructured surface meshesstate-of-the-art methods rely on patch-based or mapping-based techniques thatintroduce resampling operations in order to encode neighborhood information ina structured and regular manner. We investigate whether such resampling can beavoided, and propose a simple and direct encoding approach. It does not onlyincrease processing efficiency due to its simplicity - its direct nature alsoavoids any loss in data fidelity. To evaluate the proposed method, we perform anumber of experiments in the challenging domain of intrinsic, non-rigid shapecorrespondence estimation. In comparisons to current methods we observe thatour approach is able to achieve highly competitive results.

A Simple Approach to Intrinsic Correspondence Learning on Unstructured 3D Meshes