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

We introduce a learning-based depth map fusion framework that accepts a setof depth and confidence maps generated by a Multi-View Stereo (MVS) algorithmas input and improves them. This is accomplished by integrating volumetricvisibility constraints that encode long-range surface relationships acrossdifferent views into an end-to-end trainable architecture. We also introduce adepth search window estimation sub-network trained jointly with the largerfusion sub-network to reduce the depth hypothesis search space along each ray.Our method learns to model depth consensus and violations of visibilityconstraints directly from the data; effectively removing the necessity offine-tuning fusion parameters. Extensive experiments on MVS datasets showsubstantial improvements in the accuracy of the output fused depth andconfidence maps.

V-FUSE: Volumetric Depth Map Fusion with Long-Range Constraints