Identifying 3D prints using slicing parameters

Abstract

In this thesis, we propose a method for identifying 3D prints using slicing parameters. Called "GID," the method utilizes the subtle patterns left by the 3D printing process to identify objects. 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 unobtrusive textures resulting as a byproduct of slicing, an essential step of the 3D printing pipeline. We introduce the G-ID slicing & labeling user interface that varies the settings for each instance, and the G-ID mobile application, which uses image processing techniques to retrieve the parameters and their associated labels from a photo of the 3D printed object. We also evaluate our method's accuracy under different lighting conditions, with objects printed using different filaments and printers, and with pictures taken from various positions and angles.