We tackle the practical problem of
hand pose estimation from a single noisy depth image. A dedicated
three-step pipeline is proposed. We analyze the depth noises, and
suggest tips to minimize their negative impacts on the overall
performance. Our approach is able to work with Kinect-type noisy depth
images, and reliably produces pose estimations of motions efficiently.
• Estimate Hand Poses Efficiently from Single Depth Images (DHand))