Image-based three-dimensional (3D) reconstruction is a process of extracting 3D information from an object or entire scene while using low-cost vision sensors. A structure-from-motion coupled with multi-view stereo (SFM-MVS) pipeline is a widely used technique that allows 3D reconstruction from a collection of unordered images. The SFM-MVS pipeline typically comprises different processing steps, including feature extraction and feature matching, which provide the basis for automatic 3D reconstruction. However, surfaces with poor visual texture (repetitive, monotone, etc.) challenge the feature extraction and matching stage and affect the quality of reconstruction. The projection of image patterns while using a video projector during the image acquisition process is a well-known technique that has been shown to be successful for such surfaces. In this study, we evaluate the performance of different feature extraction methods on texture-less surfaces with the application of synthetically generated noise patterns (images). Seven state-of-the-art feature extraction methods (HARRIS, Shi-Tomasi, MSER, SIFT, SURF, KAZE, and BRISK) are evaluated on problematic surfaces in two experimental phases. In the first phase, the 3D reconstruction of real and virtual planar surfaces evaluates image patterns while using all feature extraction methods, where the patterns with uniform histograms have the most suitable morphological features. The best performing pattern from Phase One is used in Phase Two experiments in order to recreate a polygonal model of a 3D printed object using all of the feature extraction methods. The KAZE algorithm achieved the lowest standard deviation and mean distance values of 0.0635 mm and −0.00921 mm, respectively.

中文翻译：

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基于合成噪声模式的特征提取方法在基于图像的少纹理对象建模中的应用

基于图像的三维（3D）重建是在使用低成本视觉传感器的同时从对象或整个场景提取3D信息的过程。运动结合多视图立体声（SFM-MVS）管线是一种广泛使用的技术，它允许从无序图像集合中进行3D重建。SFM-MVS流水线通常包括不同的处理步骤，包括特征提取和特征匹配，这为自动3D重建提供了基础。但是，视觉纹理较差（重复，单调等）的表面会挑战特征提取和匹配阶段，并影响重建质量。在图像获取过程中使用视频投影仪时图像图案的投影是一种众所周知的技术，已被证明对这种表面是成功的。在这项研究中，我们通过合成生成的噪声模式（图像）来评估不同特征提取方法在无纹理表面上的性能。在两个实验阶段中，对有问题的表面评估了七种最新的特征提取方法（HARRIS，Shi-Tomasi，MSER，SIFT，SURF，KAZE和BRISK）。在第一阶段，对真实和虚拟平面的3D重建将使用所有特征提取方法来评估图像图案，其中具有均匀直方图的图案具有最合适的形态特征。第一阶段中性能最好的图案用于第二阶段实验中，以便使用所有特征提取方法重新创建3D打印对象的多边形模型。