Multiview Stereo via Noise Suppression PatchMatch

In this article, we reveal and rectify two underlying factors that hinder the performance of previous PatchMatch-based multiview stereo (MVS) methods in texture-less regions. First, previous methods approximate the desire pixel-wise similarity measurement with the patch-wise counterpart, which tends to be misled by ambiguous matches in texture-less areas. Therefore, we propose a novel similarity measurement that takes into account the relation between neighboring pixels and the central one. Second, the projection formula adopted by previous methods mistakes invisible points behind the optical center as visible, which leads to wrong results in view selection and depth maps. We not only rectify the formula, but also propose a geometric restriction that eliminates wrong results by shrinking the solution space of the hypothetical normal vector. These two improved aspects are necessary components of the traditional MVS pipeline and can therefore be easily applied to other PatchMatch-based MVS methods. Experiment results show that the proposed approach can effectively suppress the noise in depth maps while preserving fine details. In terms of ${F} _{\textbf {1}}$ -score, it achieves state-of-the-art performance on ETH3D high-resolution MVS benchmark.