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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >SEMANTIC PHOTOGRAMMETRY – BOOSTING IMAGE-BASED 3D RECONSTRUCTION WITH SEMANTIC LABELING
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SEMANTIC PHOTOGRAMMETRY – BOOSTING IMAGE-BASED 3D RECONSTRUCTION WITH SEMANTIC LABELING

机译：语义照相术–使用语义标签引导基于图像的3D重建

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摘要

Automatic semantic segmentation of images is becoming a very prominent research field with many promising and reliable solutions already available. Labelled images as input for the photogrammetric pipeline have enormous potential to improve the 3D reconstruction results. To support this argument, in this work we discuss the contribution of image semantic labelling towards image-based 3D reconstruction in photogrammetry. We experiment semantic information in various steps starting from feature matching to dense 3D reconstruction. Labelling in 2D is considered as an easier task in terms of data availability and algorithm maturity. However, since semantic labelling of all the images involved in the reconstruction may be a costly, laborious and time consuming task, we propose to use a deep learning architecture to automatically generate semantically segmented images. To this end, we have trained a Convolutional Neural Network (CNN) on historic building fa?ade images that will be further enriched in the future. The first results of this study are promising, with an improved performance on the quality of the 3D reconstruction and the possibility to transfer the labelling results from 2D to 3D.
机译：图像的自动语义分割已成为一个非常突出的研究领域，已经有许多有希望的和可靠的解决方案。带标签的图像作为摄影测量管线的输入，具有改善3D重建结果的巨大潜力。为了支持这一论点，在这项工作中，我们讨论了图像语义标记对摄影测量中基于图像的3D重建的贡献。从特征匹配到密集的3D重建，我们从各个步骤对语义信息进行了实验。就数据可用性和算法成熟度而言，在2D中进行标记被认为是一项更轻松的任务。但是，由于对重建中涉及的所有图像进行语义标记可能是一项昂贵，费力且耗时的任务，因此我们建议使用深度学习架构来自动生成语义分割的图像。为此，我们已经对卷积神经网络（CNN）进行了历史建筑外观图像的培训，该图像将在未来得到进一步丰富。这项研究的最初结果令人鼓舞，其3D重建质量的性能得到了改善，并且有可能将标记结果从2D转移到3D。

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