A Multiview Stereo Algorithm Based on Image Segmentation Guided Generation of Planar Prior for Textureless Regions of Artificial Scenes

Abstract

Textureless building surfaces composed of homogenized pixels could lead to failure of photometric consistency. However, textureless regions widely present in artificial scenes usually exhibit strong planarity enabling depth estimation of textureless regions with planar priors. However, existing methods for generating planar priors suffer from over-segmentation of large planes with textureless regions, which indicates that planarity is not fully exploited. In this study, we propose a novel generation method of planar prior by combining mean-shift clustering and superpixel segmentation. The planarity is fully utilized given preferential generation of planar priors for large planes with textureless regions in artificial scenes. Finally, a probabilistic graphical model is used to adopt the planar priors and smoothing constraints into depth estimation process. The image gradient is used as a criterion of the degree of texture to adaptively adjust the weights of different constraints. Experimental results on the benchmark dataset ETH3D, UDD5, and SenseFly demonstrate that the proposed method can effectively recover the depth information of textureless regions in high-resolution images to obtain highly complete three-dimensional (3-D) models of artificial scenes.