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 Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As details are missing in most representations of structures, the lack ofcontrollability to details is one of the major weaknesses in structure-basedcontrollable point cloud generation. It is observable that definitions ofdetails and structures are subjective. Details can be treated as structures onsmall scales. To represent structures in different scales at the same time, wepresent a graph-based representation of structures called the MultiscaleStructure Graph(MSG). Given structures in multiple scales, similar patterns oflocal structures can be found at different scales, positions, and angles. Theknowledge learned from a local structure pattern shall be transferred to othersimilar patterns. An encoding and generation mechanism, namely the MultiscaleStructure-based Point Cloud Generator(MSPCG) is proposed, which cansimultaneously learn point cloud generation from local patterns withmiscellaneous spatial properties. By editing the MSG, the proposed methodsupports multiscale editions on point clouds. By generating point clouds fromlocal structures and learning simultaneously in multiple scales, our MSPCG hasbetter generalization ability and scalability. Trained on the ShapeNet, ourMSPCG is able to generate point clouds for unseen categories and generateindoor scenes from a given structure. The experimental results show that ourmethod significantly outperforms baseline methods.

Generate Point Clouds with Multiscale Details from Graph-Represented Structures