Large oblique stereo images are of great interest because they have a large coverage and a high reconstruction precision. However, severe distortions are more likely to occur in this type of image. This can make conventional algorithms inaccurate, computationally expensive, and invalid. We present a new robust quasidense image matching algorithm for large oblique images. Our algorithm can be divided into two steps. First, we find a sufficient number of highly accurate seed matches that are uniformly distributed by integrating complementary affine invariant feature matching with least-squares matching. Second, we consider quasidense matches covering overlapping areas of images. We use match propagation beginning with the best seed, iteratively applying the local perspective invariant neighborhood transform (PINT) with the normalized cross-correlation metric. The local PINT is dynamically updated using the current new match set, and the erroneous matches are eliminated according to their geometric consistency. We conducted experiments on simulated and real large oblique images to demonstrate that the proposed algorithm is effective and can robustly find quasidense matches. Comparisons with the existing methods demonstrated that it is superior in terms of accuracy and efficiency.