Five Ai Limited

Oscar Rahnama

Tommaso Cavallari

Depth information is extracted from a stereoscopic image pait by an image processing system. For each pixel of a target image of the stereoscopic image pair, a final disparity cost vector is computed having cost components corresponding to different disparities. The final disparity cost vector is stored in association with that pixel. That pixel is assigned the disparity corresponding to the lowest cost component of the final disparity cost vector, wherein the extracted depth information comprises the disparities assigned to the pixels of the target image. For at least a subset of the pixels of the target image, the final disparity cost vector is computed for each of those pixels by computing, with respect to the reference image, a set of matching costs for that pixel and the different disparities, and combining the matching costs with the one or more final disparity cost vectors stored in association with one or more of the adjacent pixels of the target image.

Stereo depth estimation

In one aspect, a method of extracting depth information from a stereoscopic pair of images comprises: in a first depth extraction phase, determining a set of support points in 3D space for at least a first image of the stereoscopic image pair; using the set of support points to determine a 3D surface prior; and in a second depth extraction phase, computing, for the first image, a second-stage disparity map, by optimizing a second cost function defined so as to penalize deviation in the second-stage disparity map from the 3D surface prior. Disparity smoothness may be encouraged in at least one of the first and second depth extraction phases using a cost function that penalizes deviations in disparity between nearby pixels.

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Oxford, United Kingdom

Oscar Rahnama