What Can We Learn from Depth Camera Sensor Noise?

Although camera and sensor noise are often disregarded, assumed negligible or dealt with in the context of denoising, in this paper we show that significant information can actually be deduced from camera noise about the captured scene and the objects within it. Specifically, we deal with depth cameras and their noise patterns. We show that from sensor noise alone, the object's depth and location in the scene can be deduced. Sensor noise can indicate the source camera type, and within a camera type the specific device used to acquire the images. Furthermore, we show that noise distribution on surfaces provides information about the light direction within the scene as well as allows to distinguish between real and masked faces. Finally, we show that the size of depth shadows (missing depth data) is a function of the object's distance from the background, its distance from the camera and the object's size. Hence, can be used to authenticate objects location in the scene. This paper provides tools and insights into what can be learned from depth camera sensor noise.

Automated summary

This paper investigates the noise of cameras and sensors, demonstrating that valuable information about scenes and objects can be obtained from the noise. Specifically, it shows that depth and location of objects can be deduced from sensor noise alone. The paper also reveals that noise distribution on surfaces can provide information about light direction and distinguish between real and masked faces. Furthermore, it shows that the size of depth shadows can authenticate objects location in the scene.