论文标题
使用普通智能手机的即时反射转换成像
On-the-go Reflectance Transformation Imaging with Ordinary Smartphones
论文作者
论文摘要
反射转换成像（RTI）是一种流行技术，可以通过在不同的光条件下捕获对象来恢复每像素反射率信息。以后可以用来揭示表面细节并交互重新确认主题。但是，这样的过程通常需要专用的硬件设置来从多个位置恢复光方向，从而使该过程在实验室外执行时乏味。 我们提出了一种新颖的RTI方法，可以通过使用两台普通智能手机录制视频来执行。一种设备的闪光灯光线用于照亮主题，而另一个设备捕获了反射率。由于LED靠近相机镜头，我们可以通过自由移动照明设备的同时观察受试者周围的基金标记来推断数千个图像的光方向。为了处理这种数量的数据，我们提出了一个神经重新确定模型，该模型通过主成分分析（PCA）从极紧凑的反射分布数据中重建对象外观，以构建对象外观。实验表明，所提出的技术可以通过由此产生的RTI模型轻松执行，该模型可以超越涉及专用硬件设置的最先进方法。
Reflectance Transformation Imaging (RTI) is a popular technique that allows the recovery of per-pixel reflectance information by capturing an object under different light conditions. This can be later used to reveal surface details and interactively relight the subject. Such process, however, typically requires dedicated hardware setups to recover the light direction from multiple locations, making the process tedious when performed outside the lab. We propose a novel RTI method that can be carried out by recording videos with two ordinary smartphones. The flash led-light of one device is used to illuminate the subject while the other captures the reflectance. Since the led is mounted close to the camera lenses, we can infer the light direction for thousands of images by freely moving the illuminating device while observing a fiducial marker surrounding the subject. To deal with such amount of data, we propose a neural relighting model that reconstructs object appearance for arbitrary light directions from extremely compact reflectance distribution data compressed via Principal Components Analysis (PCA). Experiments shows that the proposed technique can be easily performed on the field with a resulting RTI model that can outperform state-of-the-art approaches involving dedicated hardware setups.
