High-performance computing model for 3D camera system

Computer vision and sensing systems have been widely applied to industry, and this includes robotics, an area requiring real-time applications. However, robust and high-accuracy systems are usually time-consuming, but high-performance devises can speed up the systems. Many applications use GPU (Graphic Processing Unit) for faster computation. The greatest advantages of the GPU system include high-memory bandwidth and flexibility and ease of programming. However, this method requires transferring data between host (computer) and device (graphic card). Moreover, it only supports data parallelism or overlap copy data and kernel execution. Multi-core CPU offers another approach for improving system performance. OpenMP, an API that supports multi-core CPU programming strongly, offers flexibility and simple programming using compiler directives items; in particular, the computation can be done without transferring data to the device and full task parallel execution. However, multi-core CPU is limited to several cores. Combining the advantages of GPU and multi-core CPU optimizes system performance. This paper proposes a novel high-performance computing model using combination architecture of GPU and multi-core CPU to obtain a two parallel layer system including data parallel and task parallel models. The result implements a highly accurate 3D reconstruction application using the improved HOC structured light coding algorithm. The experimental results of the proposed model reveal a speed 20 times faster than the original implementation in the single CPU.