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DELMO: multi-view feature fusion framework for geometric, topological and semantic error detection in BIM/IFC quantity take-off (QTO). Classification over rendered IFC geometry, IFC classes, 100k entities. Pretrained models, dataset, and IfcExplode tools.

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DELMO

DOI

Information regarding training, dataset and trained models will be shared on this repository regarding the DELMO paper. Currently we are preparing the code for release with IFCnet datasets so results can be validated. All the main data etc is provided in the paper this is supposed to supplimentary.

We already provide models here

It also include training history data as well Hugging Face Model and Train Log

We also provide access to the dataset at the following URL. However, this version differs slightly from the dataset described in the paper, as it reflects subsequent development. ITaLab-BIM-ss

@dataset{shah2026multimodal_zenodo,
  author    = {Shah, Syed Haseeb and Park, JinA and Park, Sohyun and Kim, Inhan},
  title     = {A Multimodal IFC Dataset for BIM Entity Classification for Quantity Take-off},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.23370703},
  url       = {https://doi.org/10.5281/zenodo.23370703}
}

@dataset{shah2026multimodal_mendeley,
  author    = {Shah, Syed Haseeb and Park, JinA and Park, Sohyun and Kim, Inhan},
  title     = {A Multimodal IFC Dataset for BIM Entity Classification for Quantity Take-off},
  year      = {2026},
  publisher = {Mendeley Data},
  version   = {1},
  doi       = {10.17632/gym25k77n6.1},
  url       = {https://doi.org/10.17632/gym25k77n6.1}
}

About

DELMO: multi-view feature fusion framework for geometric, topological and semantic error detection in BIM/IFC quantity take-off (QTO). Classification over rendered IFC geometry, IFC classes, 100k entities. Pretrained models, dataset, and IfcExplode tools.

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