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

Point cloud upsampling is to densify a sparse point set acquired from 3Dsensors, providing a denser representation for the underlying surface. Existingmethods divide the input points into small patches and upsample each patchseparately, however, ignoring the global spatial consistency between patches.In this paper, we present a novel method PC$^2$-PU, which explorespatch-to-patch and point-to-point correlations for more effective and robustpoint cloud upsampling. Specifically, our network has two appealing designs:(i) We take adjacent patches as supplementary inputs to compensate the lossstructure information within a single patch and introduce a Patch CorrelationModule to capture the difference and similarity between patches. (ii) Afteraugmenting each patch's geometry, we further introduce a Point CorrelationModule to reveal the relationship of points inside each patch to maintain thelocal spatial consistency. Extensive experiments on both synthetic and realscanned datasets demonstrate that our method surpasses previous upsamplingmethods, particularly with the noisy inputs. The code and data are at\url{https://github.com/chenlongwhu/PC2-PU.git}.

PC$^2$-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling