Research on CUDA-based image parallel dense matching

By analyzing the computer burden of image dense matching and the characteristic of Graphic Processor Unit(GPU) parallel computing mode, we designed a new solution of image parallel dense matching based on CUDA. We adopted the coarse-to-fine strategy, firstly implemented the pixel level normalized cross-correlation(NCC) matching method on CUDA; and then improved the matching precision by parallel affine least square matching(ALSM) under the GPU architecture. The proposed method implemented the dense matching in a full parallel mode, and took the advantage of the multi-threads supported by GPU, and finally obtained an obvious improvement in computational efficiency. The experiment results indicated that: the overall time consuming for the proposed dense matching method on GPU can achieve up to 25 times speedup over the version on CPU, which makes real-time 3D reconstruction become possible.