A computationally efficient stereo matching algorithm is introduced providing high precision dense disparity maps via local aggregation approach. The proposed algorithm exploits a novel paradigm, namely separable successive weighted summation (SWS) among horizontal and vertical directions with constant operational complexity, providing effective connected 2D support regions based on local color similarities. The intensity adaptive aggregation enables crisp disparity maps which preserve object boundaries and depth discontinuities. The same procedure is also utilized to diffuse information through overlapped pixels during occlusion handling. According to the experimental results on Middlebury online stereo benchmark, the proposed method is one of the most effective local stereo algorithm providing high quality disparity models by unifying constant time filtering and weighted aggregation. Hence, the proposed algorithm provides a competitive alternative, with its efficient GPU and FPGA implementations, for various local methods in terms of achieving precise disparity maps from stereo video within fast execution time.