A novel dense matching algorithm based on disparity growing and trifocal tensor is proposed because of the problem of dense matching. SIFT algorithm is used for the feature extraction of three-view. Then, the coarse matching is obtained by using the method of Euclidean distance. For removing the part incorrect matching of coarse matching set, gradient principal direction angle difference histogram of key point is used. Trifocal tensor and initial value of disparity which is used for directing dense matching is calculated by three-view matching. Root points which are selected from two-view matching points are used for disparity growing. Three-view dense matching is obtained by using point correspondence of trifocal tensor the same time. Then, initial value of disparity is used for improving the precision of the extraction. The experimental results indicate that the method get the accurate and dense matching in widely baseline case. Dense disparity map can be obtained.