  The reconstruction of textureless areas has long been a challenging problem
in MVS due to lack of reliable pixel correspondences between images. In this
paper, we propose the Textureless-aware Segmentation And Correlative Refinement
guided Multi-View Stereo, a novel method that effectively tackles challenges
posed by textureless areas in 3D reconstruction through filtering, refinement
and segmentation. First, we implement joint hypothesis filtering, a technique
that merges a confidence estimator with a disparity discontinuity detector to
eliminate incorrect depth estimations. Second, to spread the pixels with
confident depth, we introduce a iterative correlation refinement strategy that
leverages RANSAC to generate superpixels, succeeded by a median filter for
broadening the influence of accurately determined pixels.Finally, we present a
textureless-aware segmentation method that leverages edge detection and line
detection for accurately identify large textureless regions to be fitted using
3D planes. Experiments on extensive datasets demonstrate that our method
significantly outperforms most non-learning methods and exhibits robustness to
textureless areas while preserving fine details.
