Distortion Driven Variational Multi-view Reconstruction

This paper revisits variational multi-view stereo and identifies two issues pertaining to matching and view merging: i) regions with low visibility and relatively high depth variation are only resolved by the sole regularizer contribution. This often induces wrong matches which tend to bleed into neigh boring regions, and more importantly distort nearby features. ii) small matching errors can lead to overlapping surface layers which cannot be easily addressed by standard outlier removal techniques. In both scenarios, we rely on the analysis of the distortion of spatial and planar maps in order to improve the quality of the reconstruction. At the matching level, an anisotropic diffusion driven by spatial grid distortion is proposed to steer grid lines away from those problematic regions. At the merging level, advantage is taken of Lambert´s cosine law to favor contributions from image areas where the cosine angle between the surface normal and the line of sight is maximal. Tests on standard benchmarks suggest a good blend between computational efficiency, ease of implementation, and reconstruction quality.