Watermark Retrieval from 3D Printed Objects via Convolutional Neural Networks

Xin Zhang, Qian Wang, Toby Breckon, Ioannis Ivrissimtzis

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.

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