  In this paper, we introduce the problem of estimating the real world depth of
elements in a scene captured by two cameras with different field of views,
where the first field of view (FOV) is a Wide FOV (WFOV) captured by a wide
angle lens, and the second FOV is contained in the first FOV and is captured by
a tele zoom lens. We refer to the problem of estimating the inverse depth for
the union of FOVs, while leveraging the stereo information in the overlapping
FOV, as Tele-Wide Stereo Matching (TW-SM). We propose different deep learning
solutions to the TW-SM problem. Since the disparity is proportional to the
inverse depth, we train stereo matching disparity estimation (SMDE) networks to
estimate the disparity for the union WFOV. We further propose an end-to-end
deep multitask tele-wide stereo matching neural network (MT-TW-SMNet), which
simultaneously learns the SMDE task for the overlapped Tele FOV and the single
image inverse depth estimation (SIDE) task for the WFOV. Moreover, we design
multiple methods for the fusion of the SMDE and SIDE networks. We evaluate the
performance of TW-SM on the popular KITTI and SceneFlow stereo datasets, and
demonstrate its practicality by synthesizing the Bokeh effect on the WFOV from
a tele-wide stereo image pair.
