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

In this paper, we introduce the adaptive Wasserstein curvature denoising(AWCD), an original processing approach for point cloud data. By collectingcurvatures information from Wasserstein distance, AWCD consider more precisestructures of data and preserves stability and effectiveness even for data withnoise in high density. This paper contains some theoretical analysis about theWasserstein curvature and the complete algorithm of AWCD. In addition, wedesign digital experiments to show the denoising effect of AWCD. According tocomparison results, we present the advantages of AWCD against traditionalalgorithms.

AWCD: An Efficient Point Cloud Processing Approach via Wasserstein Curvature