Ninja Codes: Neurally Generated Fiducial Markers for Stealthy 6-DoF Tracking (wikitopia.city)

Researchers introduced "Ninja Codes," a neurally generated class of fiducial markers that subtly alter arbitrary images so they can double as stealthy 6‑DoF tracking targets. An encoder network makes visually modest modifications to existing textures or pictures; the modified images are printed on ordinary color printers and pasted onto surfaces. A jointly trained detection pipeline—built end-to-end and inspired by deep steganography—recovers marker identity and pose from RGB camera input, enabling reliable six‑degree‑of‑freedom localization on devices capable of running inference. Experiments show the approach works under common indoor lighting and across diverse environmental textures, making the markers inconspicuous compared with traditional conspicuous fiducials. For the AI/ML community this represents a novel fusion of learned steganography and geometric tracking: networks are optimized not just for concealment but for robust, real‑world pose decoding after printing and re‑imaging. Practical upsides include easier integration of AR, robotics, and motion interfaces where aesthetics matter; challenges and research directions include quantifying the robustness/visibility tradeoff, domain generalization to varied lighting and cameras, and security or misuse considerations (e.g., covert tags). Ninja Codes therefore open a new line of work on functional yet visually blended visual codes and pose estimation models.