Towards an Integrated Design Methodology for H-Bim

Pellis, E.; Masiero, Andrea; Tucci, Grazia; Betti, Michele; Grussenmeyer, Pierre

doi:10.4995/arqueologica9.2021.12158

Cited by 2 publications

(3 citation statements)

References 41 publications

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“…However, the use of deep learning on 3D point clouds still faces several significant challenges due to: (i) the unstructured and unordered nature of point clouds, which prevents the use of 2D network architectures, (ii) the large data size, which implies long computing time and (iii) the unavailability of large dedicated dataset for the networks training process. Studies exist which aim to remedy this problem [ 62 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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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: Literature Reviewmentioning

confidence: 99%

“…This does not exactly correspond to the input images in the experiments, which were close-range photogrammetry images. Methods to automatically create more suitable training data for close-range photogrammetry are also under investigation, with preliminary results described in [ 62 ].…”

Section: Conclusion and Future Investigationsmentioning

confidence: 99%

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

Murtiyoso

Pellis

Grussenmeyer

et al. 2022

Sensors

Self Cite

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“…A possible solution to this problem is the creation of synthetic training datasets, even though this procedure is less common in the DCH domain (Pierdicca et al, 2019). Pellis et al (2021) also proposed reprojection of 3D point cloud labelling into the 2D space of photogrammetric images in order to augment 2D semantic segmentation training data in a DCH context. Other recent approaches for the generation of new data include techniques based on generative models, in particular generative adversarial networks (GAN) (Goodfellow et al, 2016).…”

Section: Deep Learning For Point Cloud Segmentationmentioning

confidence: 99%

Automatic Training Data Generation in Deep Learning-Aided Semantic Segmentation of Heritage Buildings

Murtiyoso

Matrone²,

Martini³

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In the geomatics domain the use of deep learning, a subset of machine learning, is becoming more and more widespread. In this context, the 3D semantic segmentation of heritage point clouds presents an interesting and promising approach for modelling automation, in light of the heterogeneous nature of historical building styles and features. However, this heterogeneity also presents an obstacle in terms of generating the training data for use in deep learning, hitherto performed largely manually. The current generally low availability of labelled data also presents a motivation to aid the process of training data generation. In this paper, we propose the use of approaches based on geometric rules to automate to a certain degree this task. One object class will be discussed in this paper, namely the pillars class. Results show that the approach managed to extract pillars with satisfactory quality (98.5% of correctly detected pillars with the proposed algorithm). Tests were also performed to use the outputs in a deep learning segmentation setting, with a favourable outcome in terms of reducing the overall labelling time (−66.5%). Certain particularities were nevertheless observed, which also influence the result of the deep learning segmentation.

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Towards an Integrated Design Methodology for H-Bim

Cited by 2 publications

References 41 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

Automatic Training Data Generation in Deep Learning-Aided Semantic Segmentation of Heritage Buildings

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