TSAR-MVS: Textureless-aware segmentation and correlative refinement guided multi-view stereo

文献类型： 外文期刊

第一作者： Yuan, Zhenlong

作者： Yuan, Zhenlong;Cao, Jiakai;Wang, Zhaoqi;Li, Zhaoxin;Li, Zhaoxin

作者机构：

关键词： Multi-view stereo; 3D reconstruction; Filtering; Superpixel; Segmentation

期刊名称：PATTERN RECOGNITION （ 影响因子：8.0； 五年影响因子：8.4 ）

ISSN： 0031-3203

年卷期： 2024 年 154 卷

页码：

收录情况： SCI

摘要： 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 the 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 an iterative correlation refinement strategy that leverages RANSAC to generate 3D planes based on superpixels, succeeded by a weighted 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 for further depth completion. Experiments on ETH3D, Tanks & Temples and Strecha datasets demonstrate the superior performance and strong generalization capability of our proposed method.

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