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

The goal of inverse rendering is to decompose geometry, lights, and materialsgiven pose multi-view images. To achieve this goal, we propose neural directand joint inverse rendering, NDJIR. Different from prior works which relies onsome approximations of the rendering equation, NDJIR directly addresses theintegrals in the rendering equation and jointly decomposes geometry: signeddistance function, lights: environment and implicit lights, materials: basecolor, roughness, specular reflectance using the powerful and flexible volumerendering framework, voxel grid feature, and Bayesian prior. Our methoddirectly uses the physically-based rendering, so we can seamlessly export anextracted mesh with materials to DCC tools and show material conversionexamples. We perform intensive experiments to show that our proposed method candecompose semantically well for real object in photogrammetric setting and whatfactors contribute towards accurate inverse rendering.

NDJIR: Neural Direct and Joint Inverse Rendering for Geometry, Lights, and Materials of Real Object