Combining Stereo and Monocular Information to Compute Dense Depth Maps that Preserve Depth Discontinuities

In this paper, we show how simple and parallel techniques can be efficiently combined to compute dense depth maps and preserve depth discontinuities in complex real world scenes. Our algorithm relies on correlation followed by interpolation. During the correlation phase the two images play a symmetric role and we use a validity criterion for the matches that eliminates gross errors: at places where the images cannot be correlated reliably, due to lack of texture or occlusions for example, the algori thm does not produce wrong matches but a very sparse disparity map as opposed to a dense one when the correlation is successful. To generate dense depth map, the information is then propagated across the featureless areas but not across discontinuities by an interpolation scheme that takes image grey levels into account to preserve image features. We show that our algorithm performs very well on difficult images such as faces and cluttered ground level scenes. Because all the techniques described here are parallel and very regular they could be implemented in hardware and lead to extremely fast stereo systems.