提出一种基于特征面片的复杂型面轮廓三维视觉测量方法。该方法包括多模式局部特征检测与匹配、特征面片集初始化、膨胀、滤波等重构过程，其输入是已标定的立体图像，输出是覆盖表面的可视稠密面片集。该方法不需要目标和场景的任何初始边界信息，能够自动检测和抛弃外部点和缺失部分；不需要对邻域特征进行任何平滑处理，仅需通过局部单演特征一致约束、立体极线约束和表面可视性约束来高效地匹配、膨胀和滤波稀疏表面点集，并生成密集表面点集。采用多项式曲面提取算法重建三维几何网格模型，采用子采样法将彩色、纹理等特征融合进三维几何网格，重建高保真的三维实体形貌。

A feature patch-based three-dimensional vision measuring technique for complex surface and silhouette is proposed. There are several procedures for the proposed method including detecting and matching multi-modal local features, initializing, expanding and filtering patch sets. The algorithm outputs a dense set of rectangular patches covering the surfaces visible in the input calibrated images. The first step of the proposed algorithm is implemented as a matching, expanding, and filtering procedure. It starts from a sparse set of matched key points, and repeatedly expands these to nearby pixel correspondences using the monogenic feature congruency and the epipolar geometric constraint before using visibility constraints to filter away false matches. The keys to its performance are effective techniques for enforcing local photometric consistency and global visibility constraints. A simple but effective polygonal surface extraction algorithm is then used to turn the resulting patch model into a mesh appropriate for image-based modeling. According to the multi-modal monogenic features of a patch, the color and texture information is fused into the reconstructed mesh. Thus a three-dimensional high-fidelity solid model can be obtained finally.