Reconstruction of Dense Point Cloud Using Space-patch Model from Sequence Images

Reconstruction method of dense point cloud using space-patch model(SPM) from sequence images is proposed. Considering gray consistency constraints and space geometric constraints of SPM，dense SPM are generated to approximating surface by selecting，expanding and filtering of seed points. This method is beyond the constraints of rigorous conditions of traditional dense matching of epipolar images.Experimental results show the validity of the algorithm and moreover. Kind of compartitive experiments are applied to analyzing that how iterations of expansion and filter influencing the quality of point cloud. The results show that massive 3D point cloud can be reconstructed by using repeatedly iterations of expansion and filter from high resolution images.