无需标定即可实时估计相机畸变参数，实现虚拟内容与真实视频视觉一致。
Blind Augmentation: Calibration-free Camera Distortion Model Estimation for Real-time Mixed-reality Consistency
- 利用去噪、去模糊和景深去除技术实现自校准，自动估计参数。
- 在无需标定的条件下，实时生成高保真虚拟内容融合效果。
- 适合需要快速部署的AR应用，尤其适用于游戏引擎等实时渲染场景。
真实摄像机画面受噪声、运动模糊（MB）和景深（DoF）影响。某些应用中这些被视为需消除的畸变，但在增强现实（AR）中，若直接移除会影响视觉一致性或违背美学设计。本文提出一种无需标定的实时方法，可自动估计噪声、运动模糊和景深参数，使虚拟内容与真实视频流在视觉上保持一致。现有方法通常依赖标定步骤，并需可微调的专用神经网络，效率较低。本方法通过现代计算机视觉技术（如去噪、去模糊、去景深）实现自我校准，从而自动适配任意黑箱式实时模拟方法（如游戏引擎），显著提升合成效率与质量。该方案无需额外标定，支持即插即用的高保真混合现实内容生成。
原文摘要 · Abstract (English)
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.
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