Computer Graphics
TU Braunschweig

Shape from Caustics: Reconstruction of 3D-Printed Glass from Simulated Caustic Images


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.


Author(s):Marc Kassubeck, Florian Bürgel, Susana Castillo, Sebastian Stiller, Marcus Magnor
Published:to appear
Type:Article in conference proceedings
Book:IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Presented at:IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2020
Project(s): Physical Parameter Estimation from Images 


@inproceedings{kassubeck2020shape,
  title = {Shape from Caustics: Reconstruction of 3D-Printed Glass from Simulated Caustic Images},
  author = {Kassubeck, Marc and B{\"u}rgel, Florian and Castillo, Susana  and Stiller, Sebastian and Magnor, Marcus},
  booktitle = {{IEEE}/{CVF} Winter Conference on Applications of Computer Vision ({WACV})},
  year = {2021}
}

Authors