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Shape From Caustics: Reconstruction of 3D-Printed Glass From Simulated Caustic Images
We present an efficient and effective computational framework for the inverse rendering problem of reconstructing the 3D shape of a piece of glass from its caustic image. Our approach is motivated by the needs of 3D glass printing, a nascent additive manufacturing technique that promises to revolutionize the production of optics elements, from lightweight mirrors to waveguides and lenses. One important problem is the reliable control of the manufacturing process by inferring the printed 3D glass shape from its caustic image. Towards this goal, we propose a novel general-purpose reconstruction algorithm based on differentiable light propagation simulation followed by a regularization scheme that takes the deposited glass volume into account. This enables incorporating arbitrary measurements of caustics into an efficient reconstruction framework. We demonstrate the effectiveness of our method and establish the influence of our hyperparameters using several sample shapes and parameter configurations.