Watermark Retrieval from 3D Printed Objects via Convolutional Neural Networks
This addresses a specific problem in 3D printing and data retrieval, but it is incremental as it adapts existing CNN methods to a new application.
The paper tackles the problem of retrieving digital data embedded in 3D printed surfaces using a Convolutional Neural Network to locate surface textures, achieving accuracy suitable for practical applications through extensive testing under varied conditions.
We present a method for reading digital data embedded in planar 3D printed surfaces. The data are organised in binary arrays and embedded as surface textures in a way inspired by QR codes. At the core of the retrieval method lies a Convolutional Neural Network, outputting a confidence map of the location of the surface textures encoding value 1 bits. Subsequently, the bit array is retrieved through a series of simple image processing and statistical operations applied on the confidence map. Extensive experimentation with images captured from various camera views, under various illumination conditions and from objects printed with various material colours, shows that the proposed method generalizes well and achieves the level of accuracy required in practical applications.