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

Recent advances in differentiable rendering have enabled high-qualityreconstruction of 3D scenes from multi-view images. Most methods rely on simplerendering algorithms: pre-filtered direct lighting or learned representationsof irradiance. We show that a more realistic shading model, incorporating raytracing and Monte Carlo integration, substantially improves decomposition intoshape, materials & lighting. Unfortunately, Monte Carlo integration providesestimates with significant noise, even at large sample counts, which makesgradient-based inverse rendering very challenging. To address this, weincorporate multiple importance sampling and denoising in a novel inverserendering pipeline. This substantially improves convergence and enablesgradient-based optimization at low sample counts. We present an efficientmethod to jointly reconstruct geometry (explicit triangle meshes), materials,and lighting, which substantially improves material and light separationcompared to previous work. We argue that denoising can become an integral partof high quality inverse rendering pipelines.

Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising