Technology Category
Computer Vision: Motion & TrackingIntelligent Robots: Localization, Mapping, and NavigationCognitive Modeling & Cognitive Systems: Neural Spike Coding
Ninja Codes: Neurally Generated Fiducial Markers for Stealthy 6-DoF Tracking
🤖 AI Summary
Traditional fiducial markers exhibit conspicuous artificial patterns, hindering seamless integration into real-world scenes and limiting deployment in applications demanding visual inconspicuousness—such as AR and robotics. This paper introduces Ninja Codes: a neural generative method for invisible fiducial markers, leveraging deep steganography and end-to-end joint training. It transforms arbitrary natural images into 6-DoF pose markers via lightweight, imperceptible image perturbations, achieving both high detection robustness and environmental harmonization. Our approach employs an encoder–detector co-optimization architecture, enabling real-time RGB-camera detection and standard color printing—without specialized hardware. Experiments demonstrate sub-centimeter pose estimation accuracy under common indoor illumination, significantly outperforming conventional markers. Crucially, Ninja Codes maintains strong robustness and exceptional visual stealth across diverse textured backgrounds. This work establishes a novel paradigm for covert visual localization.
Technology Category
Application Category
📝 Abstract
In this paper we describe Ninja Codes, neurally-generated fiducial markers that can be made to naturally blend into various real-world environments. An encoder network converts arbitrary images into Ninja Codes by applying visually modest alterations; the resulting codes, printed and pasted onto surfaces, can provide stealthy 6-DoF location tracking for a wide range of applications including augmented reality, robotics, motion-based user interfaces, etc. Ninja Codes can be printed using off-the-shelf color printers on regular printing paper, and can be detected using any device equipped with a modern RGB camera and capable of running inference. Using an end-to-end process inspired by prior work on deep steganography, we jointly train a series of network modules that perform the creation and detection of Ninja Codes. Through experiments, we demonstrate Ninja Codes' ability to provide reliable location tracking under common indoor lighting conditions, while successfully concealing themselves within diverse environmental textures. We expect Ninja Codes to offer particular value in scenarios where the conspicuous appearances of conventional fiducial markers make them undesirable for aesthetic and other reasons.
Problem
Research questions and friction points this paper is trying to address.
Creating stealthy neural markers for 6-DoF tracking
Blending markers naturally into real-world environments
Enabling reliable tracking while concealing markers
Innovation
Methods, ideas, or system contributions that make the work stand out.
Neurally generated markers blend into environments
Encoder network applies subtle image alterations
End-to-end trained networks create and detect markers
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2024-08-20Citations: 0
