This paper presents a learning-based object segmentation proposal generation method for stereo images. Unlike existing methods which mostly rely on low-level appearance cue and handcrafted similarity functions to group segments, our method makes use of learned deep features and designed geometric features to represent a region, as well as a learned similarity network to guide the grouping process. Given an initial segmentation hierarchy, we sequentially merge adjacent regions in each level based on their affinity measured by the similarity network. This merging process generates new segmentation hierarchies, which are then used to produce a pool of regional proposals by taking region singletons, pairs, triplets and 4-tuples from them. In addition, we learn a ranking network that predicts the objectness score of each regional proposal and diversify the ranking based on Maximum Marginal Relevance measures. Experiments on the Cityscapes dataset show that our approach performs significantly better than the baseline and the current state-of-the-art.