Bundle Adjustment Accuracy Assessment of Unordered Aerial Dataset Collected Through Kite Platform

Abate, D.; Murtiyoso, Arnadi

doi:10.5194/isprs-archives-xlii-2-w17-1-2019

Cited by 2 publications

(1 citation statement)

References 16 publications

(13 reference statements)

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“…Photogrammetry as a discipline has a long history of use in the field of surveying. Starting with primarily small scale aerial use [ 2 , 3 ], the use of terrestrial images has also been effectively applied for many applications in larger scale architectural survey [ 4 ]. In the past few decades, photogrammetry has also seen significant developments in both its theoretical and technical aspects.…”

Section: Introductionmentioning

confidence: 99%

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

Murtiyoso

Pellis

Grussenmeyer

et al. 2022

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Murtiyoso

Pellis

Grussenmeyer

et al. 2022

Sensors

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Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures

2020

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The paper presents an efficient photogrammetric workflow to improve the 3D reconstruction of scenes surveyed by integrating terrestrial and Unmanned Aerial Vehicle (UAV) images. In the last years, the integration of this kind of images has shown clear advantages for the complete and detailed 3D representation of large and complex scenarios. Nevertheless, their photogrammetric integration often raises several issues in the image orientation and dense 3D reconstruction processes. Noisy and erroneous 3D reconstructions are the typical result of inaccurate orientation results. In this work, we propose an automatic filtering procedure which works at the sparse point cloud level and takes advantage of photogrammetric quality features. The filtering step removes low-quality 3D tie points before refining the image orientation in a new adjustment and generating the final dense point cloud. Our method generalizes to many datasets, as it employs statistical analyses of quality feature distributions to identify suitable filtering thresholds. Reported results show the effectiveness and reliability of the method verified using both internal and external quality checks, as well as visual qualitative comparisons. We made the filtering tool publicly available on GitHub.

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Bundle Adjustment Accuracy Assessment of Unordered Aerial Dataset Collected Through Kite Platform

Cited by 2 publications

References 16 publications

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

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

Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures

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