Year: 2024
Type: conference-paper
Abstract: In this paper we describe Ninja Codes, neurally-generated fiducial markers that blend naturally into real-world environments.Taking inspiration from recent advances in deep steganography, we train a neural network that transforms arbitrary images into functional fiducial markers (Ninja Codes) with minimal visual changes.Ninja Codes can be pasted onto various surfaces, to provide location tracking capability for applications such as augmented reality, robotics, etc. Ninja Codes can be printed using common color printers on regular paper, and can be detected by any hardware equipped with a standard RGB camera and capable of running inference.
Authors: Yusuke Imoto, Shunya Kato, Yuichiro Takeuchi
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4404519689