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首页> 外文期刊>Photogrammetric Engineering & Remote Sensing: Journal of the American Society of Photogrammetry >Integrated Image Matching and Segmentation for 3D Surface Reconstruction in Urban Areas
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Integrated Image Matching and Segmentation for 3D Surface Reconstruction in Urban Areas

机译：城市地区3D表面重建的集成图像匹配与分割

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摘要

High-resolution imagery, which features the advantages of high-quality imaging, a short revisit time, and lower costs, is an attractive option for 3D reconstruction applications. Photogrammetric 3D reconstruction requires reliable and dense image matching. In urban areas, however, image matching is particularly difficult because of the complexity of urban textures and the severe occlusion problems caused by buildings. This paper presents an integrated image matching and segmentation approach (named SATM+) for 3D reconstruction in urban areas. SATM+ is based on our existing self-adaptive triangulation-constrained matching (SATM) framework and incorporates three novel aspects to address image matching challenges in urban areas: (1) image segmentation-based occlusion filtering, (2) segment-adaptive similarity measurement to reduce matching ambiguity, and (3) local and regional dense matching propagation to generate reliable and dense matches. We performed an experimental analysis of two sets of high-resolution urban images, and the 3D point clouds generated using the proposed SATM+ were compared with airborne light detection and ranging (lidar) data and the point clouds generated using the semi-global matching (SGM) method. The results indicate that SATM+ can generate 3D point clouds with a geometric accuracy comparable to that of lidar data but a much higher point density. SATM+ performs similarly to SGM in relatively flat areas, but is superior in built-up areas. The proposed approach is a promising option for image-based 3D surface reconstruction in urban areas.
机译：高分辨率图像，具有高质量成像的优势，短暂的重新访问时间和更低的成本，是3D重建应用的有吸引力的选择。摄影测量3D重建需要可靠和密集的图像匹配。然而，在城市地区，由于城市纹理的复杂性和建筑造成的严重闭塞问题，图像匹配特别困难。本文介绍了城市地区三维重建的集成图像匹配和分割方法（名为SATM +）。 SATM +基于我们现有的自适应三角测量 - 受限匹配（SATM）框架，并包含三个新颖方面，以解决城市地区的图像匹配挑战：（1）基于图像分割的遮挡滤波，（2）分段 - 自适应相似性测量减少匹配的歧义，（3）本地和区域密集匹配传播以产生可靠和密集的比赛。我们对两组高分辨率城市图像进行了实验分析，并将使用所提出的SATM +产生的3D点云与空气射击检测和测距（LIDAR）数据和使用半全局匹配产生的点云进行比较（SGM ） 方法。结果表明，SATM +可以产生与LIDAR数据相当的几何精度的3D点云，而是更高的点密度。 SATM +类似于相对平坦的区域的SGM类似地执行，但在内置区域方面优越。所提出的方法是城市地区基于图像的3D表面重建的有希望的选择。

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