interiqr: Unobtrusive Edible Tags using Food 3D Printing

Figure 1: interiqr is a method that utilizes the infill structure of the 3D printing process to embed information inside the food, which allows for hiding the tag from the human eye. We present an end-to-end pipeline that allows the users to embedding data through food 3D printing and decoding them through several applications.
Figure 2: System workflow: (a) The system takes the tag information, food 3D model, and infill information as input. Then, (b) the system generates a tag by customizing the 3D printing slicer and output G-code file to (c) the food 3D printer. At the same time, the system generates an encoder file for (d) the recognition process. The 3D printed food is recognized through image processing, and (e) the food information is extracted along with other specific applications.


We present interiqr, a method that utilizes the infill parameter in the 3D printing process to embed information inside the food that is difficult to recognize with the human eye. Our key idea is to utilize the air space or secondary materials to generate a specific pattern inside the food without changing the model geometry. As a result, our method exploits the patterns that appear as hidden edible tags to store the data and simultaneously adds them to a 3D printing pipeline. Our contribution also includes the framework that connects the user with a data-embedding interface through the food 3D printing process, and the decoding system allows the user to decode the information inside the 3D printed food through backlight illumination and a simple image processing technique. Finally, we evaluate the usability of our method under different settings and demonstrate our method through the example application scenarios.

Yamamoto Miyatake, Parinya Punpongsanon, Daisuke Iwai, and Kosuke Sato. interiqr: Unobtrusive Edible Tags using Food 3D Printing. In Proceedings of the ACM Symposium on User Interface Software and Technology (UIST) 2022, pp. 1-11. Bend, USA, October 2022. Acceptance Rate: 26.3%

宮武大和, プンポンサノン・パリンヤ, 岩井大輔, 佐藤宏介. 3Dプリント食品内部への情報埋め込み. 第84回情報処理学会全国大会, 情報処理学会, ページ 4:189-4:190, 2022年3月. In Japanese