Adaptive stereo similarity fusion using confidence measures

• We propose similarity fusion strategy based on stereo confidences.
• We propose a consensus strategy to exploit spatial correlation between pixels.
• Our fusion increases the accuracy of global and local stereo algorithms.
• We out-perform other fusion strategies.

In most stereo-matching algorithms, stereo similarity measures are used to determine which image patches in a left–right image pair correspond to each other. Different similarity measures may behave very differently on different kinds of image structures, for instance, some may be more robust to noise whilst others are more susceptible to small texture variations. As a result, it may be beneficial to use different similarity measures in different image regions. We present an adaptive stereo similarity measure that achieves this via a weighted combination of measures, in which the weights depend on the local image structure. Specifically, the weights are defined as a function of a confidence measure on the stereo similarities: similarity measures with a higher confidence at a particular image location are given higher weight. We evaluate the performance of our adaptive stereo similarity measure in both local and global stereo algorithms on standard benchmarks such as the Middlebury and KITTI data sets. The results of our experiments demonstrate the potential merits of our adaptive stereo similarity measure.