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

Point clouds are often sparse and incomplete. Existing shape completionmethods are incapable of generating details of objects or learning the complexpoint distributions. To this end, we propose a cascaded refinement networktogether with a coarse-to-fine strategy to synthesize the detailed objectshapes. Considering the local details of partial input with the global shapeinformation together, we can preserve the existing details in the incompletepoint set and generate the missing parts with high fidelity. We also design apatch discriminator that guarantees every local area has the same pattern withthe ground truth to learn the complicated point distribution. Quantitative andqualitative experiments on different datasets show that our method achievessuperior results compared to existing state-of-the-art approaches on the 3Dpoint cloud completion task. Our source code is available athttps://github.com/xiaogangw/cascaded-point-completion.git.

Cascaded Refinement Network for Point Cloud Completion