3D hand pose estimation in point cloud using 3D convolutional neural network on egocentric datasets

In particular, we evaluated the entire FPHA dataset with five different sampling configurations. The results show that 3D distance error is increased many times compared to estimates on the hand datasets are not obstructed (the hand data obtained from surveillance cameras, are viewed from top view, front view, sides view) such as MSRA, NYU, ICVL datasets. The results are quantified, analyzed, shown on the point cloud data of CVAR dataset and projected on the color image of FPHA dataset.
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3D hand pose estimation in point cloud using 3D convolutional neural network on egocentric datasets