  Existing techniques for 3D action recognition are sensitive to viewpoint
variations because they extract features from depth images which change
significantly with viewpoint. In contrast, we directly process the pointclouds
and propose a new technique for action recognition which is more robust to
noise, action speed and viewpoint variations. Our technique consists of a novel
descriptor and keypoint detection algorithm. The proposed descriptor is
extracted at a point by encoding the Histogram of Oriented Principal Components
(HOPC) within an adaptive spatio-temporal support volume around that point.
Based on this descriptor, we present a novel method to detect Spatio-Temporal
Key-Points (STKPs) in 3D pointcloud sequences. Experimental results show that
the proposed descriptor and STKP detector outperform state-of-the-art
algorithms on three benchmark human activity datasets. We also introduce a new
multiview public dataset and show the robustness of our proposed method to
viewpoint variations.
