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

While object semantic understanding is essential for most service robotictasks, 3D object classification is still an open problem. Learning fromartificial 3D models alleviates the cost of annotation necessary to approachthis problem, but most methods still struggle with the differences existingbetween artificial and real 3D data. We conjecture that the cause of thoseissue is the fact that many methods learn directly from point coordinates,instead of the shape, as the former is hard to center and to scale undervariable occlusions reliably. We introduce spherical kernel point convolutionsthat directly exploit the object surface, represented as a graph, and a votingscheme to limit the impact of poor segmentation on the classification results.Our proposed approach improves upon state-of-the-art methods by up to 36% whentransferring from artificial objects to real objects.

Sim2Real 3D Object Classification using Spherical Kernel Point Convolution and a Deep Center Voting Scheme