Energy-Aware Depth Map Generation for 3D Portrait on Android Systems

The most important information in transforming a 2D image into a 3D image is the depth of each pixel in the image. However, a normal 2D image usually does not contain such information, which makes the transformation impossible. On the other hand, for certain types of pictures, such as personal portraits, it is possible to infer crude depth information from their known contexts and properties. Unfortunately, depth map generation is very involving and, if executed on a mobile phone, will consume a lot of energy. This is undesirable, particularly when the mobile device is running out of battery. The application must be aware of the energy status of the system, make appropriate tradeoffs, and then adapt accordingly. This paper presents such an energy-aware, 2D-to-3D image transformation tool for personal portraits on mobile phones. The tool will choose a suitable depth-map generation algorithm based on the remaining energy of the device. We will discuss how to make the tradeoffs and evaluate the idea on real machines.