3D CV Descriptor on Parallel Heterogeneous Platforms

Abstract

Embedded three-dimensional (3D) Computer Vision (CV) is considered a technology enabler for future consumer applications, attracting a wide interest in academia and industry. However, 3D CV processing is a computation-intensive task. Its high computational cost is directly related to the processing of 3D point clouds, with the 3D descriptor computation representing one of the main bottlenecks. Understanding the main computational challenges of 3D CV applications, as well as the key characteristics, enabling features, and limitations of current computing platforms, is clearly strategic to identify the directions of evolution for future embedded processing systems targeting 3D CV. In this work, an innovative and complex 3D descriptor (called SHOT) has been ported on a high-end and an embedded computing platform. The high-end system is composed by a high-performance Intel CPU coupled with a Nvidia GPU. The embedded platform is, instead, composed by an ARM-based processor, coupled with the STHORM accelerator. STHORM is a many-core low-power accelerator developed by ST Microelectronics, featuring up to 64 computational units. The SHOT descriptor has been parallelized using the OpenCL programming model for both platforms. Finally, we have performed an in-depth performance comparison and analysis between general-purpose processors and accelerators in both high-end and embedded domains, discussing and highlighting the main differences in the Hardware/Software (HW/SW) design methodologies and approaches between high-end and embedded systems targeting 3D CV applications.