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

Neural rendering methods have significantly advanced photo-realistic 3D scenerendering in various academic and industrial applications. The recent 3DGaussian Splatting method has achieved the state-of-the-art rendering qualityand speed combining the benefits of both primitive-based representations andvolumetric representations. However, it often leads to heavily redundantGaussians that try to fit every training view, neglecting the underlying scenegeometry. Consequently, the resulting model becomes less robust to significantview changes, texture-less area and lighting effects. We introduce Scaffold-GS,which uses anchor points to distribute local 3D Gaussians, and predicts theirattributes on-the-fly based on viewing direction and distance within the viewfrustum. Anchor growing and pruning strategies are developed based on theimportance of neural Gaussians to reliably improve the scene coverage. We showthat our method effectively reduces redundant Gaussians while deliveringhigh-quality rendering. We also demonstrates an enhanced capability toaccommodate scenes with varying levels-of-detail and view-dependentobservations, without sacrificing the rendering speed.

Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering