Point-cloud refinement via exact matching

In many multi-view stereo (MVS) algorithms, a point-cloud evolution is performed, based on the matching process. For most of them, an assumption is usually employed for the matching, which indicates that the matching windows have the same shape. This assumption lays a great limit to the quality of the reconstructed result. To improve the point-cloud obtained from other algorithms, and break the limit laid by the regular matching, we propose our refinement method using exact matching. The exact matching enables more accurate matching windows for the points, by taking the normal vector into consideration. By maximizing the exact matching result, the point´s coordinate and normal vectors are optimized, and we can thus make the original point-cloud much better.