Fast recursive adaptive weight stereo matching

Stereo matching based on traditional adaptive weight is computational intensive. The basic idea of adaptive weight is that bigger weight should be given to those pixels with less color difference and shorter distance. A novel weight was defined to recursively implement cost aggregation. The weight between neighbor pixels was redefined as the product of distance attenuation factor and color difference function, while the weight between other pixels was redefined as the product of weights between neighbor pixels. Using the proposed weight, cost aggregation was recursively implemented with only 4 multiplications and 8 additions per pixel per disparity. A new fast dense stereo matching was designed based on recursive adaptive weight. Evaluation on the Middlebury’s benchmark proved that the proposed method is faster and more accurate than traditional adaptive weight method.