A Point Cloud Video Recognition Acceleration Framework Based on Tempo-Spatial Information

In point cloud video recognition (PVR) tasks, deep neural networks (DNNs) have been widely adopted to enhance accuracy. However, real-time processing is hindered due to the increasing volume of points and frames that require processes. Point clouds represent 3D-shaped discrete objects using a multitude of points. Consequently, these points often exhibit an uneven distribution in the view space, resulting in strong spatial similarity within each point cloud frame. Taking advantage of this observation, this article introduces PRADA, a P oint Cloud R ecognition A cceleration algorithm via D ynamic A pproximation. PRADA approximates and eliminates the similar local pairs’ computations and recovers their results by copying dissimilar local pairs’ features for speedup with negligible accuracy loss. Furthermore, considering the slow changes in point cloud frames that lead to the high temporal similarity among points across multiple frames, we design PointV, a Point Cloud V ideo Recognition Acceleration algorithm, to minimize unnecessary computations of similar points in the temporal domain. Moreover, we propose the PRADA and PointV architectures to accelerate the PRADA and PointV algorithms. These two architectures can be integrated to gain higher performance improvement. Our experiments on a wide variety of datasets show that PRADA averagely achieves about $7\times$ speedup over 1080TI GPU. In addition, the experimental results show that the PointV architecture and the integrated architecture can respectively achieve $11.7\times$ and $13.9\times$ performance improvement with acceptable accuracy compared to the 1080TI GPU.