Hybrid stereo matching by dynamic programming with enhanced cost entry for real-time depth generation

In this paper, a hybrid stereo matching method combining cross-based adaptive window aggregation and basic dynamic programming (DP) is proposed. A well-known drawback of DP is the streaking artifact, which greatly influences the subjective effect. Rather than introducing variable tree structures, we tackle this problem by providing an enhanced cost entry for DP optimization. Inter-scanlines are related closely by cost aggregating within a surface adaptive window, in which way the artifact is greatly reduced. Another contribution of our paper is that a variable starting point scheme is presented to force DP optimization to start from the accurate point, which prevents error cost accumulating at the beginning. Evaluation results show that our method outperforms most DP based algorithms. More importantly, the proposed hybrid method can be used in realtime systems. CUDA implementation of the proposed method can generate a disparity map in 15.17ms for a typical stereo pair with resolution of 384 × 288 and 16 disparity levels.