Gloss-aware Color Correction for 3D Printing

Jorge Condor1     Michal Piovarci2     Bernd Bickel2     Piotr Didyk1

1Universit√† della Svizzera italiana, Switzerland    2ISTA, Austria

Abstract:

Color and gloss are fundamental aspects of surface appearance. State-of-the-art fabrication techniques can manipulate both properties of the printed 3D objects. However, in the context of appearance reproduction, perceptual aspects of color and gloss are usually handled separately, even though previous perceptual studies suggest their interaction. Our work is motivated by previous studies demonstrating a perceived color shift due to a change in the object's gloss, i.e., two samples with the same color but different surface gloss appear as they have different colors. In this paper, we conduct new experiments which support this observation and provide insights into the magnitude and direction of the perceived color change. We use the observations as guidance to design a new method that estimates and corrects the color shift enabling the fabrication of objects with the same perceived color but different surface gloss. We formulate the problem as an optimization procedure solved using differentiable rendering. We evaluate the effectiveness of our method in perceptual experiments with 3D objects fabricated using a multi-material 3D printer and demonstrate potential applications.

Video (coming soon)

Citation

Jorge Condor, Michal Piovarci, Bernd Bickel, Piotr Didyk, Gloss-aware Color Correction for 3D Printing, SIGGRAPH Conference Papers (2023)

@inproceedings{Condor2023,
  author = { Jorge Condor and Michal Piovar\v{c}i and Bernd Bickel and Piotr Didyk},
  title = {Gloss-aware Color Correction for 3D Printing},
  booktitle = {SIGGRAPH 2023 Conference Papers},
  year = {2023},
}

Acknowledgements

We thank Matthew S Zurawski for the 3D model of the car speed shape, Creative Tools3 and Stanford Computer Graphics Laboratory for the geometry of the bunny, and Havran et al. [2016] for the geometry of the ghost. This research has been supported by the Swiss National Science Foundation (SNSF, Grant 200502), the FWF Lise Meitner (Grant M 3319), and an academic gift from Meta.