Real-time accurate stereo with bitwise fast voting on CUDA
This paper proposes a real-time design for accurate stereo matching on Compute Unified Device Architecture (CUDA). We adopt a leading local algorithm for its high data parallelism. A GPU-oriented bitwise fast voting method is proposed to effectively improve the matching accuracy, which is enormously faster than the histogram-based approach. The whole algorithm is parallelized on CUDA at a fine granularity, efficiently exploiting the computing resources of GPUs. On-chip shared memory is utilized to alleviate the latency of memory accesses. Compared to the CPU counterpart, our design attains a speedup factor of 52. With high matching accuracy, the proposed design is still among the fastest stereo methods on GPUs. The advantages of speed and accuracy advocate our design for practical applications such as robotics systems and multiview teleconferencing.