Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry

Sensors

卷:22

Andrea Masiero¹  Arnadi Murtiyoso²  Pierre Grussenmeyer²  Eugenio Pellis²  Tania Landes² 

[1] Department of Civil and Environmental Engineering, University of Florence, 50121 Florence, Italy;

[2] Université de Strasbourg, INSA Strasbourg, CNRS, ICube Laboratory UMR 7357, 67084 Strasbourg, France;

关键词: photogrammetry;    semantic segmentation;    deep learning;    automation;    dense matching;    point cloud;   

DOI  :  10.3390/s22030966

来源: DOAJ

【 摘 要 】

Developments in the field of artificial intelligence have made great strides in the field of automatic semantic segmentation, both in the 2D (image) and 3D spaces. Within the context of 3D recording technology it has also seen application in several areas, most notably in creating semantically rich point clouds which is usually performed manually. In this paper, we propose the introduction of deep learning-based semantic image segmentation into the photogrammetric 3D reconstruction and classification workflow. The main objective is to be able to introduce semantic classification at the beginning of the classical photogrammetric workflow in order to automatically create classified dense point clouds by the end of the said workflow. In this regard, automatic image masking depending on pre-determined classes were performed using a previously trained neural network. The image masks were then employed during dense image matching in order to constraint the process into the respective classes, thus automatically creating semantically classified point clouds as the final output. Results show that the developed method is promising, with automation of the whole process feasible from input (images) to output (labelled point clouds). Quantitative assessment gave good results for specific classes e.g., building facades and windows, with IoU scores of 0.79 and 0.77 respectively.

【 授权许可】

Unknown