Aiming at the shortage of SIFT algorithm in time comsumption and getting less matching points. On the one hand, the paper improves the original SIFT algorithm, it proposes regional growth algorithm based on SIFT, so you can get many matching points which are good for generating the disparity map; on the other hand, the paper uses the CPU and GPU heterogeneous platforms and analyses the CUDA programming model and memory model. This paper analyses the algorithm in detail, so the algorithm can be carried out on CUDA. Experimental results show that, compared with the original algorithm, the algorithm is about 10 times faster, and generate good disparity map.
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