Multiview stereo (MVS) is a crucial process in image-based automatic 3-D reconstruction and mapping applications. In a dense matching process, the matching cost is generally computed between image pairs, making the efficiency low due to the large number of stereo pairs. This paper presents a novel object-based MVS algorithm using structural similarity (SSIM) index matching cost in a coarse-to-fine workflow. As far as we know, this is the first time SSIM index is introduced to calculate the matching cost of MVS applications. In contrast to classical stereo methods, the proposed object-based structural similarity (OSSIM) method computes only a depth map for each image. Thus, the efficiency can be greatly improved when the overlap between images is large. To obtain an optimized depth map, the winner-take-all and semi-global matching strategies are implemented. Moreover, an object-based multiview consistency checking strategy is also proposed to eliminate wrong matches and perform pixelwise view selection. The proposed method was successfully applied on a close-range Fountain-P11 data set provided by EPFL and aerial data sets of Vaihingen and Zürich by the ISPRS. Experimental results demonstrate that the proposed method can deliver matches at high completeness and accuracy. For the Vaihingen data set, the correctness and completeness rate were 71.12% and 95.99% with an RMSE of 2.8 GSD. For the Foutain-P11 data set, the proposed method outperformed the other existing methods with the ratio of pixels less than 2 cm. Extensive comparison using Zürich data set shows that it can derive results comparable to the state-of-the-art software (PhotoScan, Pix4d, and Smart3D) in urban buildings areas.

Multi-view dense matching is a crucial process in automatic 3D reconstruction and mapping applications. In this paper, we present a robust and effective multi-view dense matching algorithm for high-resolution aerial images based on a graph network. The overlap ratio and intersection angle between image pairs are used to find candidate stereo pairs and build the graph network. A Coarse-to-Fine strategy based on an improved Semi-Global Matching algorithm is applied for disparity computation across stereo pairs. Based on the constructed graph, point clouds of base views are generated by triangulating all connected image nodes, followed by a fusion process with the average reprojection error as a priority measure. The proposed method was successfully applied in experiments on aerial image test dataset provided by the ISPRS of Vaihingen, Germany and an oblique nadir image block of Zürich, Switzerland, using three kinds of matching configurations. The proposed method was compared to other state-of-art methods, SURE and PhotoScan. The results demonstrate that the proposed method delivers matches at higher completeness, efficiency, and accuracy than the other methods tested; the RMS for average reprojection error reached the sub pixel level and the actual positioning deviation was better than 1.5 GSD.