自适应面具的快速稠密立体匹配技术研究

Research on Fast Dense Stereo Matching Technique Using Adaptive Mask

文中提出一种稠密点云快速立体匹配方法,该方法以传统相位相关算法为基础,通过对匹配点梯度估计的方法自适应叠加离散面具,增加近似同一深度区域的匹配权重,使重构精度与可信度得以提升.通过储存与重复利用二维傅里叶变换的中间结果大幅提高算法的计算效率.由于该算法符合SIMD模型规则,因此GPU的并行计算能力使得匹配过程基本达到了实时性要求.实验表明,该快速相位相关算法对短基线平行光轴被动立体视觉系统所采集的光滑不规则漫反射物体表面具有较好地快速重构能力,因此可在诸如三维人脸识别等领域得到广泛应用.

Based on the classical phase-only correlation algorithms, a fast stereo matching method is proposed for dense point cloud. The adaptive discrete mask is used to the matching weight of the similar dense fields by estimating the gradient of matching points, thus the reconstruction precision and reliability are improved. Moreover, the proposed method also improves computational efficiency via storing and reusing the intermediate data of 2D DFT. Since the proposed algorithm satisfies SIMD model, the GPU parallel computing makes the matching process basically reach real-time . The experimental results show that the proposed fast phase-only correlation algorithm performs well in the surface reconstruction of smooth irregular diffuse objects obtained from short-baseline parallel-axis binocular stereo device, therefore it can be widely used in 3 D face recognition and other fields.