Class FactoryStereoDisparity

java.lang.Object
boofcv.factory.disparity.FactoryStereoDisparity

public class FactoryStereoDisparity
extends Object

Creates high level interfaces for computing the disparity between two rectified stereo images. Algorithms which select the best disparity for each region independent of all the others are referred to as Winner Takes All (WTA) in the literature. Dense algorithms compute the disparity for the whole image while sparse algorithms do it in a per pixel basis as requested.

Typically disparity calculations with regions will produce less erratic results, but their precision will be decreased. This is especially evident along the border of objects. Computing a wider range of disparities can better results, but is very computationally expensive.

Dense vs Sparse. Here dense refers to computing the disparity across the whole image at once. Sparse refers to computing the disparity for a single pixel at a time as requested by the user,