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 (TSAR-MVS), 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.

在这篇论文中，我们提出了一种名为TSAR-MVS的新方法，通过过滤、细化和分割来有效解决3D重建中纹理缺失区域的挑战。该方法首先采用联合假设过滤技术来消除深度估计错误，然后通过迭代相关性细化和纹理感知分割方法来提高准确性和在纹理缺失区域保留细节的能力。实验证明，与大多数非学习方法相比，我们的方法在各种数据集上都显著优于其他方法，并且能够在纹理缺失区域中保持鲁棒性。

TSAR-MVS：纹理感知分割与相关细化引导的多视角立体重建