Project

G-ID: Identifying 3D Prints Using Slicing Parameters

3D printed objects inherently possess surface patterns due to the angle of the print path and the thickness of the trace the 3D printer lays down. G-ID exploits such features that would normally go unnoticed to identify unique instances of an object without the need to embed an obtrusive, additional tag. G-ID provides (a) a user interface for slicing individual instances of the same object with different settings and assigning labels to them. After (b) 3D printing, users can (c) identify each instance using

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 naked eye.

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.