Sparse view stereo matching

This paper addresses the sparse view matching problem where the camera parameters lie within ranges depending on the sensor used. An approach, based on homographic transformation, is proposed. The operation is split into two phases. The first phase deals with matches on the ground surface, which is considered planar. The second phase detects matches on arbitrary planes. This is done by detecting junctions of different shapes and reconstructing planes inferred by them. Two versions of that approach are suggested based on the sum of absolute differences and the variance normalized correlation techniques. The first technique is computationally inexpensive while the later is more robust to changes in lighting condition between views. Experiments show that our approach outperforms non-homographic correlation.