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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper deals with the highly challenging problem of reconstructing theshape of a refracting object from a single image of its resulting caustic. Dueto the ubiquity of transparent refracting objects in everyday life,reconstruction of their shape entails a multitude of practical applications.The recent Shape from Caustics (SfC) method casts the problem as the inverse ofa light propagation simulation for synthesis of the caustic image, that can besolved by a differentiable renderer. However, the inherent complexity of lighttransport through refracting surfaces currently limits the practicability withrespect to reconstruction speed and robustness. To address these issues, weintroduce Neural-Shape from Caustics (N-SfC), a learning-based extension thatincorporates two components into the reconstruction pipeline: a denoisingmodule, which alleviates the computational cost of the light transportsimulation, and an optimization process based on learned gradient descent,which enables better convergence using fewer iterations. Extensive experimentsdemonstrate the effectiveness of our neural extensions in the scenario ofquality control in 3D glass printing, where we significantly outperform thecurrent state-of-the-art in terms of computational speed and final surfaceerror.

N-SfC: Robust and Fast Shape Estimation from Caustic Images