Abstract
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.
Abstract (translated)
由于没有可靠的图像像素对应关系,MVS中的无纹理区域重建一直是一个挑战性的问题。在本文中,我们提出了一种无纹理aware Segmentation And Correlative refinement guided Multi-View Stereo(TSAR-MVS),一种新方法,通过滤波、精化以及分割,有效地解决了无纹理区域在3D重建中面临的挑战。首先,我们实现了联合假设滤波,该技术将信心估计与差异连续性检测相结合,以消除不正确的深度估计。其次,为了均匀分布具有信心深度的像素,我们引入了一种迭代相关 refinement策略,利用RANSAC生成超级像素,然后使用中值滤波以扩展准确确定的像素的影响。最后,我们提出了一种无纹理aware segmentation方法,利用边缘检测和线检测准确识别使用3D平面 fitting 大的无纹理区域。对大量数据集的实验表明,我们的方法 significantly outperforms 大多数非学习方法,并表现出对无纹理区域的稳健性,同时保留 fine details。
URL
https://arxiv.org/abs/2308.09990
