We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to the synthetic models. The proposed framework uses a “pure” point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components.
CITATION STYLE
Coscia, P., Palmieri, F. A. N., Castaldo, F., & Cavallo, A. (2016). 3-d hand pose estimation from kinect’s point cloud using appearance matching. In Smart Innovation, Systems and Technologies (Vol. 54, pp. 37–45). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-33747-0_4
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