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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Using three-dimensional spatial information, this method constructs the detection condition, compares the conditions to be detected, formed by the points in the file to be simplified, then determines the redundancy of the points. Moving windows are used to promote the operation of the algorithm and generate many tiny approximate vertical planes, from which the simplified points will be generated. By selecting experimental data on rabbit and horse and comparing the methods based on mesh and curvature, the proposed method increased the simplification rate of rabbit and horse from 3.022&#x0025; and 11.123&#x0025; (mesh method), and 5.704&#x0025; and 15.316&#x0025; (curvature method), to 83.387&#x0025; and 84.296&#x0025;, respectively, while the standard deviation was reduced from 0.01051 and 0.0157 (mesh method), and 0.0817 and 0.0013 (curvature method), to 0.02179 and 0.01507, respectively. For the simplification of multiple objects, the proposed method increased the simplification rate from 89.113&#x0025; and 91.826&#x0025; (mesh method), and 84.79&#x0025; and 88.91&#x0025; (curvature method), to 93.458&#x0025; and 96.916&#x0025;, respectively, reducing the time by approximately 20 s.

A New Simplification Algorithm for Point Cloud Based on the Vertical Plane Constraint and Moving Window