VMV2023

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https://diglib.eg.org/handle/10.2312/3543878

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Browsing VMV2023 by Subject "CCS Concepts: Computing methodologies → Image-based rendering; Shape modeling; Machine learning"

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    N-SfC: Robust and Fast Shape Estimation from Caustic Images
    (The Eurographics Association, 2023) Kassubeck, Marc; Kappel, Moritz; Castillo, Susana; Magnor, Marcus; Guthe, Michael; Grosch, Thorsten
    This 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.