Disparity estimation for fisheye images with an application to intermediate view synthesis

TL;DR

This paper introduces a novel disparity estimation method tailored for fisheye images that leverages fisheye projection knowledge to improve intermediate view synthesis quality.

Contribution

The proposed approach adapts disparity estimation to fisheye images by transforming coordinate grids, enhancing accuracy over traditional block matching methods.

Findings

More accurate disparity maps for fisheye images.

Improved intermediate view synthesis results.

Retains full fisheye field of view.

Abstract

To obtain depth information from a stereo camera setup, a common way is to conduct disparity estimation between the two views; the disparity map thus generated may then also be used to synthesize arbitrary intermediate views. A straightforward approach to disparity estimation is block matching, which performs well with perspective data. When dealing with non-perspective imagery such as obtained from ultra wide-angle fisheye cameras, however, block matching meets its limits. In this paper, an adapted disparity estimation approach for fisheye images is introduced. The proposed method exploits knowledge about the fisheye projection function to transform the fisheye coordinate grid to a corresponding perspective mesh. Offsets between views can thus be determined more accurately, resulting in more reliable disparity maps. By re-projecting the perspective mesh to the fisheye domain, the original fisheye field of view is retained. The benefit of the proposed method is demonstrated in the context of intermediate view synthesis, for which both objectively evaluated as well as visually convincing results are provided.