A method for solving binocular and multi-view stereo matching problems is presented in this paper. A weak consistency constraint is proposed, which expresses the visibility constraint in the image space. It can be proved that the weak consistency constraint holds for scenes that can be represented by a set of 3D points. As well, also proposed is a new reliability measure for dynamic programming techniques, which evaluates the reliability of a given match. A novel reliability-based dynamic programming algorithm is derived accordingly, which can selectively assign disparity values to pixels when the reliabilities of the corresponding matches exceed a given threshold. Consistency constraints and the new reliability-based dynamic programming algorithm can be combined in an iterative approach. The experimental results show that the iterative approach can produce dense (60~90%) and reliable (total error rate of 0.1~1.1%) matching for binocular stereo datasets. It can also generate promising disparity maps for trinocular and multi-view stereo datasets.