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

Semantic labelling is highly correlated with geometry and radiancereconstruction, as scene entities with similar shape and appearance are morelikely to come from similar classes. Recent implicit neural reconstructiontechniques are appealing as they do not require prior training data, but thesame fully self-supervised approach is not possible for semantics becauselabels are human-defined properties.  We extend neural radiance fields (NeRF) to jointly encode semantics withappearance and geometry, so that complete and accurate 2D semantic labels canbe achieved using a small amount of in-place annotations specific to the scene.The intrinsic multi-view consistency and smoothness of NeRF benefit semanticsby enabling sparse labels to efficiently propagate. We show the benefit of thisapproach when labels are either sparse or very noisy in room-scale scenes. Wedemonstrate its advantageous properties in various interesting applicationssuch as an efficient scene labelling tool, novel semantic view synthesis, labeldenoising, super-resolution, label interpolation and multi-view semantic labelfusion in visual semantic mapping systems.

In-Place Scene Labelling and Understanding with Implicit Scene Representation