CAJ | 학술논문

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。
문중제출일충주밀점운쾌속입체필배방법，해방법이전통상위상관산법위기출，통과대필배점제도고계적방법자괄응첩가리산면구，증가근사동일심도구역적필배권중，사중구정도여가신도득이제승。통과저존여중복이용이유부리협변환적중간결과대폭제고산법적계산효솔。유우해산법부합SIMD모형규칙，인차GPU적병행계산능력사득필배과정기본체도료실시성요구。실험표명，해쾌속상위상관산법대단기선평행광축피동입체시각계통소채집적광활불규칙만반사물체표면구유교호지쾌속중구능력，인차가재제여삼유인검식별등영역득도엄범응용。
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 3D face recognition and other fields.