Blind Augmentation: Calibration-free Camera Distortion Model Estimation for Real-time Mixed-reality Consistency
This addresses the need for efficient and calibration-free consistency in augmented reality applications, representing an incremental improvement over existing methods.
The paper tackles the problem of making virtual content visually consistent with real camera footage in augmented reality by modeling noise, motion blur, and depth of field without requiring camera calibration. It proposes a method that estimates these parameters instantly, enabling the use of off-the-shelf real-time simulation methods for high-fidelity augmentation.
Real camera footage is subject to noise, motion blur (MB) and depth of field (DoF). In some applications these might be considered distortions to be removed, but in others it is important to model them because it would be ineffective, or interfere with an aesthetic choice, to simply remove them. In augmented reality applications where virtual content is composed into a live video feed, we can model noise, MB and DoF to make the virtual content visually consistent with the video. Existing methods for this typically suffer two main limitations. First, they require a camera calibration step to relate a known calibration target to the specific cameras response. Second, existing work require methods that can be (differentiably) tuned to the calibration, such as slow and specialized neural networks. We propose a method which estimates parameters for noise, MB and DoF instantly, which allows using off-the-shelf real-time simulation methods from e.g., a game engine in compositing augmented content. Our main idea is to unlock both features by showing how to use modern computer vision methods that can remove noise, MB and DoF from the video stream, essentially providing self-calibration. This allows to auto-tune any black-box real-time noise+MB+DoF method to deliver fast and high-fidelity augmentation consistency.