A non-local adaptive hypothesis propagation for multi-view stereo

Abstract

Hypothesis propagation is a central component of PatchMatch-based multi-view stereo and significantly impacts the reconstruction performance. However, current propagation methods rely on photometric consistency to guide hypothesis propagation within a local area. When the centroid is located in a low-textured area with reflective or refractive properties, high chromatic aberration may cause the multi-view matching to fall into a local optimum that fails to provide reliable hypotheses, leading to reconstruction errors. To address this problem, we propose a non-local adaptive hypothesis propagation scheme. First, we evenly distribute sampling points in eight directions on the checkerboard to quickly determine reliable initial hypotheses. Then, starting from the initial hypotheses generated in the eight directions of the checkerboard, the hypotheses are adaptively propagated to non-checkerboard areas based on matching cost, reducing interference from unreliable photometric consistency and improving reconstruction performance in challenging areas. The test results on large-scale benchmarks show that the proposed scheme has significant advantages in reconstructing challenging areas. It can significantly improve the completeness of point clouds from current state-of-the-art methods and outperform existing propagation schemes.