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

Multilayer perceptrons (MLPs) have been successfully used to represent 3Dshapes implicitly and compactly, by mapping 3D coordinates to the correspondingsigned distance values or occupancy values. In this paper, we propose a novelpositional encoding scheme, called Spline Positional Encoding, to map the inputcoordinates to a high dimensional space before passing them to MLPs, forhelping to recover 3D signed distance fields with fine-scale geometric detailsfrom unorganized 3D point clouds. We verified the superiority of our approachover other positional encoding schemes on tasks of 3D shape reconstruction frominput point clouds and shape space learning. The efficacy of our approachextended to image reconstruction is also demonstrated and evaluated.

Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields