Dense Multi-view 3D-reconstruction Without Dense Correspondences

TL;DR

This paper presents a variational multi-view shape-from-shading method that fuses information across images and color channels using an ADMM algorithm, enabling dense 3D reconstructions without dense correspondences.

Contribution

It introduces a novel variational framework coupled with an ADMM algorithm for multi-view shape-from-shading that avoids dense correspondence computation.

Findings

Achieves highly accurate dense 3D reconstructions from multiple images.

Works effectively on both simulated and real imagery.

Produces detailed geometry even in textureless regions.

Abstract

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.