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In this paper, we present a novel iterative median filter based strategy to improve the quality of the depth maps provided by sensors like Microsoft Kinect. The quality of the depth map is improved in two aspects, by filling holes present in the maps and by addressing the random noise. The holes are filled by iteratively applying a median based filter which takes into account the RGB components as well. The color similarity is measured by finding the absolute difference of the neighbourhood pixels and the median value. The hole filled depth map is further improved by applying a bilateral filter and processing the detail layer separately using Non-Local Denoising. The denoised detail layer is combined with the base layer to obtain a sharp and accurate depth map. We show that the proposed approach is able to generate high quality depth maps which can be quite useful in improving the performance of various applications of Microsoft Kinect such as pose estimation, gesture recognition, skeletal and facial tracking, etc.

To maintain a reliable registration of the virtual world with the real world, augmented reality (AR) applications require highly accurate, low-latency tracking of the device. In this paper, we propose a novel method for performing this fast 6-DOF head pose tracking using a cluster of rolling shutter cameras. The key idea is that a rolling shutter camera works by capturing the rows of an image in rapid succession, essentially acting as a high-frequency 1D image sensor. By integrating multiple rolling shutter cameras on the AR device, our tracker is able to perform 6-DOF markerless tracking in a static indoor environment with minimal latency. Compared to state-of-the-art tracking systems, this tracking approach performs at significantly higher frequency, and it works in generalized environments. To demonstrate the feasibility of our system, we present thorough evaluations on synthetically generated data with tracking frequencies reaching 56.7 kHz. We further validate the method's accuracy on real-world images collected from a prototype of our tracking system against ground truth data using standard commodity GoPro cameras capturing at 120 Hz frame rate.