
The 30th International Conference, MMM 2024, took place in Amsterdam, The Netherlands, January 29 – February 2, 2024, in which our ASHVIN partner CERTH / ITI presented a conference paper entitled “A framework for 3D modelling of construction sites using aerial imagery and semantic NeRFs”. This peer-reviewed publication was authored by Panagiotis Vrachnos, Marios Krestenitis, Ilias Koulalis, Konstantinos Ioannidis and Stefanos Vrochidis, all researchers at CERTH/ITI. This paper is related to the ASHVIN Demonstration Site #7 - Bridges in highway network in Spain. Abstract The rapid evolution of drone technology has revolutionized data acquisition in the construction industry, offering a cost-effective and efficient method to monitor and map engineering structures. However, a significant challenge remains in transforming the drone-collected data into semantically meaningful 3D models. 3D reconstruction techniques usually lead to raw point clouds that are typically unstructured and lack the semantic and geometric information of objects needed for civil engineering tools. Our solution applies semantic segmentation algorithms to the data produced by NeRF (Neural Radiance Fields), effectively transforming drone-captured 3D volumetric representations into semantically rich 3D models. This approach offers a cost-effective and automated way to digitalize physical objects of construction sites into semantically annotated digital counterparts facilitating the development of digital twins or XR applications in the construction sector. Note THIS VERSION IS THE SUBMITTED ONE (not the final version published by the MMM2024 editor).
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
