Efficient Stereo Matching by Local Linear Filtering and Improved Dynamic Programming

摘要: In this paper, we present a dense stereo correspondence algorithm combining local linear filtering and improved dynamic programming (DP) algorithm, which maintains good performance in both accuracy speed. Traditional DP, as all know having most advantage efficiency among global approaches, suffers from typical streaking artifacts. Recently, an enhanced DP-based can reduce streak effects well by employing vertical consistency constraint between the scanlines, but with ambiguous matching at object boundaries. To tackle problem, cost-filtering framework is deployed very fast edge preserving filter DP optimization, without additional burden of computational complexity. The evaluation using Middlebury benchmark datasets demonstrate that our method produces results comparable to those state-of-the-art algorithms much more efficient.