We present two improvement techniques for stereo matching algorithms using silicon retina sensors. We verify the results with ground truth data. In contrast to conventional monochrome/color cameras, silicon retina sensors deliver an asynchronous flow of events instead of common framed and discrete intensity or color images. While using this kind of sensor in a stereo setup to enable new fields of applications, it also introduces new challenges in terms of stereo image analysis. Using this type of sensor, stereo matching algorithms have to deal with sparse event data, thus, less information. This affects the quality of the achievable disparity results and renders improving the stereo matching algorithms a necessary task. For this reason, we introduce two techniques for increasing the accuracy of silicon retina stereo results, in the sense that the average distance error is reduced. The first method is an adapted belief propagation approach optimizing the initial matching cost volume, and the second is an innovative two-stage postfilter for smoothing and outlier rejection. The evaluation shows that the proposed techniques increase the accuracy of the stereo matching and constitute a useful extension for using silicon retina sensors for depth estimation.