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

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dc.contributor.authorKassubeck, Marcen_US
dc.contributor.authorKappel, Moritzen_US
dc.contributor.authorCastillo, Susanaen_US
dc.contributor.authorMagnor, Marcusen_US
dc.contributor.editorGuthe, Michaelen_US
dc.contributor.editorGrosch, Thorstenen_US
dc.date.accessioned2023-09-25T11:36:40Z
dc.date.available2023-09-25T11:36:40Z
dc.date.issued2023
dc.description.abstractThis paper handles the highly challenging problem of reconstructing the shape of a refracting object from a single image of its resulting caustic. Due to the ubiquity of transparent refracting objects in everyday life, reconstruction of their shape entails a multitude of practical applications. While we focus our attention on inline shape reconstruction in glass fabrication processes, our methodology could be adapted to scenarios where the limiting factor is a lack of input measurements to constrain the reconstruction problem completely. The recent Shape from Caustics (SfC) method casts this problem as the inverse of a light propagation simulation for synthesis of the caustic image, that can be solved by a differentiable renderer. However, the inherent complexity of light transport through refracting surfaces currently limits the practical application due to reconstruction speed and robustness. Thus, we introduce Neural-Shape from Caustics (N-SfC), a learning-based extension incorporating two components into the reconstruction pipeline: a denoising module, which both alleviates the light transport simulation cost, and also helps finding a better minimum; and an optimization process based on learned gradient descent, which enables better convergence using fewer iterations. Extensive experiments demonstrate that we significantly outperform the current state-of-the-art in both computational speed and final surface error.en_US
dc.description.sectionheadersRendering and Modelling
dc.description.seriesinformationVision, Modeling, and Visualization
dc.identifier.doi10.2312/vmv.20231224
dc.identifier.isbn978-3-03868-232-5
dc.identifier.pages33-41
dc.identifier.pages9 pages
dc.identifier.urihttps://doi.org/10.2312/vmv.20231224
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20231224
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Image-based rendering; Shape modeling; Machine learning
dc.subjectComputing methodologies → Image
dc.subjectbased rendering
dc.subjectShape modeling
dc.subjectMachine learning
dc.titleN-SfC: Robust and Fast Shape Estimation from Caustic Imagesen_US
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