Mustafa Doga Dogan
Massachusetts Institute of Technology, Cambridge, MA, USA
We present G-ID, 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 by-product of slicing, an essential step of the 3D printing pipeline.<br>We introduce the G-ID slicing and 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.
https://doi.org/10.1145/3313831.3376202
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)