Human stereo vision works by fusing a pair of perspective images with a purely horizontal parallax. Recent developments suggest that very few varieties of multiperspective stereo pairs exist. In this paper, we introduce a new stereo model, which we call epsilon stereo pairs, for fusing a broader class of multiperspective images. An epsilon stereo pair consists of two images with a slight vertical parallax. We show many multiperspective camera pairs that do not satisfy the stereo constraint can still form epsilon stereo pairs. We then introduce a new ray-space warping algorithm to minimize stereo inconsistencies in an epsilon pair using multiperspective collineations. This makes epsilon stereo model a promising tool for synthesizing close-to-stereo fusions from many non-stereo pairs.