Dense Multi-view 3D-reconstruction Without Dense Correspondences
This addresses the problem of detailed 3D reconstruction in textureless areas for computer vision applications, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackles dense 3D reconstruction from multiple images without requiring dense correspondences by coupling shape-from-shading solutions across views and color channels using a variational formulation. The method achieves highly accurate dense reconstructions in experiments on simulated and real imagery, particularly in areas with smooth brightness variation and lacking texture.
We introduce a variational method for multi-view shape-from-shading under natural illumination. The key idea is to couple PDE-based solutions for single-image based shape-from-shading problems across multiple images and multiple color channels by means of a variational formulation. Rather than alternatingly solving the individual SFS problems and optimizing the consistency across images and channels which is known to lead to suboptimal results, we propose an efficient solution of the coupled problem by means of an ADMM algorithm. In numerous experiments on both simulated and real imagery, we demonstrate that the proposed fusion of multiple-view reconstruction and shape-from-shading provides highly accurate dense reconstructions without the need to compute dense correspondences. With the proposed variational integration across multiple views shape-from-shading techniques become applicable to challenging real-world reconstruction problems, giving rise to highly detailed geometry even in areas of smooth brightness variation and lacking texture.