UltraDepth

Abstract

Time-of-flight (ToF) depth cameras have been increasingly adopted in various real-world applications, e.g., used with RGB cameras for advanced computer vision tasks like 3-D mapping or deployed alone in privacy-sensitive applications such as sleep monitoring. In this paper, we propose UltraDepth, the first system that can expose high-resolution texture from depth maps captured by off-the-shelf ToF cameras, simply by introducing a distorting IR source. The exposed texture information can significantly augment depth-based applications. Moreover, such a capability can be used to launch privacy attacks, which poses a major concern due to the prominence of ToF cameras. To design UltraDepth, we present an in-depth analysis on the impact of the distorting IR light on the distance measurement. We further show that, the reflection properties (reflectivity and incidence angle) of the objects will be encoded in the distorted depth map and hence can be leveraged to reveal texture of objects in UltraDepth. We then propose two practical implementations of UltraDepth, i.e., reflection-based and external IR-based implementations. Our extensive real-world experiments show that, the depth maps output by UltraDepth achieve 89.06%, 99.33%, 81.25% mean accuracy in object detection, face recognition and character recognition, respectively, which offers over 10x improvement over the ordinary depth maps and even approaches the performance of RGB and IR images in a number of scenarios. The findings of this work provide key insights for new research on depth-related computer vision and security of depth sensing devices.