Abstract

Buildings are a central feature of human culture and are increasingly being analyzed with computational methods. However, recent works on computational building understanding have largely focused on natural imagery of buildings, neglecting the fundamental element defining a building's structure -- its floorplan. Conversely, existing works on floorplan understanding are extremely limited in scope, often focusing on floorplans of a single semantic category and region (e.g. floorplans of apartments from a single country). In this work, we introduce WAFFLE, a novel multimodal floorplan understanding dataset of nearly 20K floorplan images and metadata curated from Internet data spanning diverse building types, locations, and data formats. By using a large language model and multimodal foundation models, we curate and extract semantic information from these images and their accompanying noisy metadata. We show that WAFFLE enables progress on new building understanding tasks, both discriminative and generative, which were not feasible using prior datasets. We will publicly release WAFFLE along with our code and trained models, providing the research community with a new foundation for learning the semantics of buildings.

Citation

@misc{ganon2024wafflemultimodalfloorplanunderstanding,
  title={WAFFLE: Multimodal Floorplan Understanding in the Wild}, 
  author={Keren Ganon and Morris Alper and Rachel Mikulinsky and Hadar Averbuch-Elor},
  year={2024},
  eprint={2412.00955},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2412.00955}, 
}