G-ID: Identifying 3D Prints Using Slicing Parameters
Mustafa Doga Dogan, Faraz Faruqi, Andrew Day Churchill, Kenneth Friedman, Leon Cheng, Sriram Subramanian, Stefanie Mueller
Abstract
Figure 1. 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 the G-ID mobile app that uses image processing techniques to detect the slicing parameters and retrieve the associated labels.
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Install the CLIlune papers fulltext 66214b06-c66e-40a6-9ada-611e792d2e27Cited by top-tier papers11
- Augmented Object Intelligence with XR-ObjectsMustafa Doga Dogan, Eric J. Gonzalez, Karan Ahuja, Ruofei Du et al.UIST 2024 · 64 citations
- InfraredTags: Embedding Invisible AR Markers and Barcodes Using Low-Cost, Infrared-Based 3D Printing and Imaging ToolsMustafa Doga Dogan, Ahmad Taka, Michael Lu, Yunyi Zhu et al.CHI 2022 · 59 citations
- SensiCut: Material-Aware Laser Cutting Using Speckle Sensing and Deep LearningMustafa Doga Dogan, Steven Vidal Acevedo Colon, Varnika Sinha, Kaan Aksit et al.UIST 2021 · 40 citations
- OmniActions: Predicting Digital Actions in Response to Real-World Multimodal Sensory Inputs with LLMsJiahao Nick Li, Yan Xu, Tovi Grossman, Stephanie Santosa et al.CHI 2024 · 25 citations
- BrightMarker: 3D Printed Fluorescent Markers for Object TrackingMustafa Doga Dogan, Raul Garcia-Martin, Patrick William Haertel, Jamison John O'Keefe et al.UIST 2023 · 22 citations
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