G-ID is a method that utilizes the subtle patterns left by the 3D printing process to distinguish and identify objects that otherwise look similar to the human eye. The key idea is to mark different instances of a 3D model by varying slicing parameters that do not change the model geometry but can be detected as machine-readable differences in the print. As a result, G-ID does not add anything to the object but exploits the patterns appearing as a byproduct of slicing, an essential step of the 3D printing pipeline.
We introduce the G-ID slicing & labeling interface that varies the settings for each instance, and the G-ID mobile app, which uses image processing techniques to retrieve the parameters and their associated labels from a photo of the 3D printed object. Finally, we evaluate our method’s accuracy under different lighting conditions, when objects were printed with different filaments and printers, and with pictures taken from various positions and angles.
More Information: https://hcie.csail.mit.edu/research/gid/gid.html
G-ID: Identifying 3D Prints Using Slicing Parameters.
In Proceedings of CHI 2020.
Mustafa Doga Dogan, MIT CSAIL
Faraz Faruqi, MIT CSAIL
Andrew Day Churchill, MIT CSAIL
Kenneth Friedman, MIT CSAIL
Leon Cheng, MIT CSAIL
Sriram Subramanian, University of Sussex
Stefanie Mueller, MIT CSAIL