模拟深度相机噪声，提升呼吸率估计的仿真真实度
Influence of Depth Camera Noise Models on Respiration Estimation
- 构建3D渲染仿真管道，重点测试不同噪声模型影响
- 低分辨率下噪声模型差异显著，高斯模型不再适用
- 适合做多相机呼吸监测算法训练与验证的研究者
深度相机是监测呼吸率等生命体征的一种有前景的模态。虽然已有多种方法可在受控环境下提取生命体征，但要实现更灵活的应用（如多相机场景），仍需通过仿真生成大量训练与测试数据。本文首次展示一个聚焦于不同噪声模型的3D渲染仿真流程，利用合成与真实呼吸信号作为基准，生成基于深度相机的呼吸信号。结果表明，在此场景中，大多数噪声可准确建模为高斯分布；但当图像分辨率过低时，不同噪声模型之间的差异变得明显。
原文摘要 · Abstract (English)
Depth cameras are an interesting modality for capturing vital signs such as respiratory rate. Plenty approaches exist to extract vital signs in a controlled setting, but in order to apply them more flexibly for example in multi-camera settings, a simulated environment is needed to generate enough data for training and testing of new algorithms. We show first results of a 3D-rendering simulation pipeline that focuses on different noise models in order to generate realistic, depth-camera based respiratory signals using both synthetic and real respiratory signals as a baseline. While most noise can be accurately modelled as Gaussian in this context, we can show that as soon as the available image resolution is too low, the differences between different noise models surface.
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