Abstract: Disparity range estimation is very important in stereo matching. An appropriate disparity range can increase the precision and speed of stereo matching. This paper proposes a fast disparity range estimation method based on matching-cost search and image subdivision. It evenly divides input images into several sub-blocks, and searches using matching cost to find out which sub-block is having the maximum/minimum disparity. After that, the maximum/minimum sub-block is recursively divided into smaller sub-blocks, until all current sub-blocks have the same disparity. To deal with image blocks with week textures, detecting their disparity reliabilities is applied via matching-cost diagram. Experimental results show that our method can achieve 27.7% reduction rate of search space while preserving 93.7% hit rate on average. Compared to traditional methods, it can get a more accurate disparity range. Moreover, the gained disparity range reduces the running time of stereo matching by 20%-45% while decreasing the average false-match rate.