Multi-view depth map reconstruction is a popular approach to generate 3D information with good flexibility and scalability. However, texture-weak regions and repeated textures challenge these methods in urban scenes. To address this need, this paper proposes an image-triangle based multi-view 3D reconstruction (IMGTR) method. Starting from constructing a density-adaptive image triangulation for each image, the main procedure is to determine the corresponding object plane for each image triangle under an objective function consisting of the image similarity measure, the smoothness constraint and the continuity constraint on the edges between adjacent triangles. Qualitative and quantitative experiments show that the proposed method can reconstruct complex urban structures more accurate and achieve higher fidelity in planar urban structures than some recently released multi-view stereo algorithms. Specifically, for the Vaihingen dataset IMGTR achieves an average improvement of 4.27% compared to PMVD, 50.94% to SURE and 54.76% to COLMAP in position accuracy.

多视图深度图重建是一种流行的生成 3D 信息的方法，具有良好的灵活性和可扩展性。然而，纹理薄弱区域和重复纹理在城市场景中挑战了这些方法。为了满足这一需求，本文提出了一种基于图像三角形的多视图 3D 重建 (IMGTR) 方法。从为每幅图像构建一个密度自适应的图像三角剖分开始，主要过程是在由图像相似性度量、平滑度约束和相邻边缘的连续性约束组成的目标函数下确定每个图像三角形对应的目标平面。三角形。定性和定量实验表明，与最近发布的一些多视图立体算法相比，所提出的方法可以更准确地重建复杂的城市结构，并在平面城市结构中实现更高的保真度。具体来说，对于 Vaihingen 数据集，IMGTR 与 PMVD 相比平均提高了 4.27%，在定位精度方面达到了 SURE 的 50.94% 和 COLMAP 的 54.76%。