In this work we present an appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. We have considered both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the dierent types of interactions are classied. The hand-object case is clearly the most challenging task having to deal with multiple tracks. The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one. The pose estimation is obtained by applying a modied version of the ICP (Iterative Closest Point) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data. We use a "pure" point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components. For this reason, the proposed method can be also applied to data obtained from other types of depth sensor, or RGB-D camera.
['Pasquale Coscia', 'Francesco Palmieri', 'Francesco Castaldo', 'Alberto Cavallo']
3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching