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REVIEW 4 major objections 5 minor 52 references

WAFFLE: Multimodal Floorplan Understanding in the Wild

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read WAFFLE is a nearly 20K-image floorplan dataset from Wikimedia Commons with LLM-extracted metadata, and the paper argues it enables building-type retrieval, open-vocabulary segmentation, and type-conditioned floorplan generation that prior…

desk verdict A genuinely new diverse floorplan dataset, but the retrieval evaluation likely overstates layout understanding by not masking text in the images. read the letter →

arxiv 2412.00955 v2 pith:6W3ZRDUH submitted 2024-12-01 cs.CV

Keren Ganon , Morris Alper , Rachel Mikulinsky , Hadar Averbuch-Elor This is my paper · ORCID
classification cs.CV
keywords floorplanunderstandingin-the-wilddatasetmultimodallearningbuildingtyperetrievalopen-vocabularysegmentationtext-to-imagegenerationpseudo-labelingarchitecturalfeatures
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces WAFFLE, a dataset of 18,556 floorplan images gathered from openly available Internet media, paired with structured metadata extracted by a large language model: building name, building type, country, and architectural features grounded to image regions. The central claim is that this automatically curated, noisy data supports building-understanding tasks that earlier floorplan datasets, which mostly cover apartments from a single country, cannot support. On this data, a fine-tuned contrastive vision-language model learns to retrieve a building's type from its floorplan, an open-vocabulary segmentation model learns to localize architectural terms, and a text-to-image diffusion model learns to generate floorplan images for specific building types. A sympathetic reader would take the paper as showing that in-the-wild schematic imagery plus LLM-generated labels can serve as a foundation for learning the semantics of buildings.

What carries the argument

The machine behind the dataset is a fully automatic curation-and-labeling pipeline. A transformer object detector (DETR) fine-tuned on 200 annotated images locates layout components (floorplan, legend, compass, scale); the contrastive image-text model CLIP filters scraped images to floorplans; OCR is run on each image; and prompts to the large language model Llama-2 turn the noisy metadata and OCR text into structured pseudo-ground truth, including building name, type, country, and architectural features. Grounding is achieved by matching legend keys and feature names to OCR detections inside the detected floorplan and legend regions, giving the textual labels spatial coordinates. This pipeline is what converts unlabeled schematic images into paired image-text data usable for retrieval, segmentation, and generation.

What would settle it

Take a random sample of WAFFLE test images, have independent annotators label building type and grounded features without seeing the machine-generated labels, then evaluate the fine-tuned retrieval and segmentation models against those human labels; if performance collapses toward chance while the models still match the machine labels, the reported understanding is an artifact of label bias rather than building semantics.

Watch

Extended reading notes

Core claim

WAFFLE contains nearly 20K floorplan images spanning more than 100 countries, over 1K building types, and over 11K distinct grounded architectural features, with a train/test split of 18,259 and 297 images made by country to prevent building leakage. The paper's claim is that these data enable new discriminative and generative tasks: fine-tuning CLIP on paired images and building-type labels raises retrieval Recall@1 from 1.5% to 11.8%; fine-tuning CLIPSeg on grounded features raises open-vocabulary segmentation Average Precision from 0.157 to 0.226; and fine-tuning Stable Diffusion with prompts like "a floor plan of a <building type>" improves generation realism (FID from 194.8 to 145.3) and prompt alignment, with users preferring the fine-tuned generations 70.42% of the time. The same images also serve as a challenging benchmark: a supervised residential-floorplan segmentation model trained on CubiCasa5K attains only 0.488 IoU on walls in WAFFLE, suggesting that prior narrow datasets do not transfer to diverse real-world floorplans. The paper reports manual inspection of 100 random samples with 85-96% accuracy for the language-model-extracted labels.

Load-bearing premise

The load-bearing premise is that the building type, country, and architectural-feature labels produced by Llama-2 are accurate enough to serve both as training signal and as test ground truth; they are validated only by manual inspection of 100 random samples (85-96% accuracy), and the test-set labels themselves are not re-verified.

Editorial extensions

If this is right

  • A model fine-tuned on WAFFLE can identify the building type of a never-seen floorplan, including non-residential types such as castles, temples, and hospitals.
  • Open-vocabulary segmentation trained on WAFFLE can localize architectural terms such as nave, choir, and court inside floorplans, including terms absent from fixed residential label sets.
  • Text-conditioned generation on WAFFLE produces floorplan images whose structure reflects the prompt type, such as towers for castles and many small rooms for hospitals.
  • WAFFLE exposes the limited transfer of existing supervised residential models: their segmentation performance drops sharply on diverse, in-the-wild floorplans.
  • Structure-conditioned generation works for diverse building types even when the structural constraint is unusual for that type, because the text condition and layout condition are fused from separate sources.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: because the test split is chosen by country, part of the retrieval and generation gains could come from country-specific visual style rather than building semantics; an audit that swaps or mixes countries would separate these factors.
  • Inference: the same LLM-curation pipeline could plausibly transfer to other schematic document families such as maps, engineering drawings, or historic blueprints, though the paper does not test this.
  • Inference: the reported label accuracy rests on 100 manual samples; verifying a larger random sample and the full test set would strengthen the case that the pseudo-labels, not metadata bias, drive the results.
  • Inference: the dataset's acknowledged skew toward historic and religious buildings implies that models trained on it may underperform on modern commercial and industrial building types.
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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces WAFFLE, a dataset of 18,556 floorplan images scraped from Wikimedia Commons, with LLM-extracted pseudo-labels (building name, type, country, grounded architectural features) and OCR/detection metadata. The authors evaluate the dataset as a benchmark and as training data for four tasks: fine-tuned CLIP retrieval of building type (Section 4.1), open-vocabulary segmentation with CLIPSeg (Section 4.2), a semantic segmentation benchmark for existing models (Section 4.3), and text- and structure-conditioned floorplan generation with Stable Diffusion and ControlNet (Sections 4.4 and 4.5), including a user study. The central claim is that WAFFLE makes feasible new discriminative and generative building-understanding tasks and that fine-tuning on it improves performance relative to strong baselines.

Significance. If the central claims hold, WAFFLE would be a valuable community resource: it is substantially larger and more diverse in building type and country than existing floorplan datasets, it is built by an automatic pipeline, and it is intended for public release with code and models. The paper also takes useful precautions in the weakly supervised segmentation setup, notably deriving labels from Wikipedia text rather than from image pixels and inpainting OCR regions before training. The overall significance therefore hinges on whether the reported gains reflect visual understanding of floorplan layout rather than text leakage or unverified pseudo-labels; the experiments as reported do not yet establish that.

major comments (4)
  1. [Section 4.1] Table 2's building-type retrieval experiment feeds raw floorplan images to CLIP without masking or inpainting OCR text, even though Section 4.2 explicitly inpaints OCR regions "to prevent leakage from the written text in the images." In-the-wild floorplans frequently contain titles, labels, and legends, and the dataset pipeline collects OCR detections precisely because this text is informative. The improvement from R@1=1.5% to 11.8% could therefore be driven by reading words such as "cathedral" or "castle" rather than by understanding floorplan layout, leaving the central claim of visual building-type understanding unsupported. The authors should repeat the evaluation on OCR-inpainted or masked images, or on a text-free subset such as the bottom images in Figure 1, to control for this confound.
  2. [Section 3.3] The pseudo-label validation is limited to 100 random samples from the whole dataset, with reported accuracies of 89% for building names, 85% for building types, and 96% for countries. The manual inspection of the test set only removes images that do not contain a valid floorplan and does not verify the building-type labels that are used as ground truth in the retrieval and generation evaluations (Tables 2, 5, 6). Since building-type pseudo-labels are both the training target and the evaluation target, systematic errors or biases in those labels could inflate the apparent improvements of CLIP_FT and SD_FT. The authors should report pseudo-label accuracy on the test set, with a per-type breakdown, or re-verify the test-set labels before drawing conclusions.
  3. [Tables 2, 3, 5] The reported comparisons lack confidence intervals, significance tests, or multiple-seed variance, which is necessary to support the paper's "significantly outperforms" language. This is especially important for Table 3, which uses a 95-image manually selected evaluation set and an empirically chosen mIoU threshold of 0.25, and for Table 5, where the CLIP similarity differences (e.g., 24.9 vs 25.6) are small and FID is known to be unstable on small datasets. The user study in Section 4.4 reports a 70.42% preference without a confidence interval or participant-level variance. Adding standard errors, paired tests, or seed-level results would substantially strengthen the central claims.
  4. [Table 3] The comparison against CC5K in the open-vocabulary segmentation experiment is not apples-to-apples: the footnote states that CC5K was evaluated only over a subset of residential buildings, while CLIPSeg and the fine-tuned model are evaluated on the full manually annotated 95-image set. The claim in Section 4.2 that the method "outperform[s] the strongly-supervised CC5K" is therefore not directly supported by the table as presented. The authors should evaluate all methods on the same image set or clearly report subset sizes and separate results for the common subset.
minor comments (5)
  1. [Section 4.5] The phrase "we leverage our data in an unsupervised manner" is imprecise for the structure-conditioned experiment, because the ControlNet for internal structure is trained on CubiCasa5K annotations; only the boundary-conditioned variant uses automatically extracted edges from WAFFLE. The caption of Figure 7 also states that the condition is from CubiCasa5K. The text should clarify what is trained on WAFFLE versus external data.
  2. [Table 6] Fine-tuning actually hurts the Theatre category on FID (238.4 vs 189.5) and KMMD (0.17 vs 0.08) relative to the pretrained model; the claim that fine-tuning improves "the vast majority of building types" should be replaced with a precise statement of the number and identities of categories that improve.
  3. [Appendix C.2] The semantic-similarity thresholds (0.7 positive, 0.4 negative) and the mIoU binarization threshold (0.25) are set empirically; reporting a short sensitivity analysis would help establish that the segmentation improvement does not depend on these choices.
  4. [Section 4.4] The kernel used for KMMD is not specified; reproducible reporting of kernel maximum mean discrepancy requires the kernel choice and bandwidth.
  5. [General] Minor typographical issues: "W AF FLE" and "W AFFLE" appear in the abstract, and "manualy inpect" appears in Appendix B.3.2.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: WAFFLE is a dataset-and-benchmark paper; its tasks are evaluated against external or manually validated annotations.

full rationale

This paper's central claim is that the WAFFLE dataset enables new building-understanding tasks, not that a particular quantity is derived from first principles. I examined every load-bearing step for reduction to its own inputs. (1) Building-type retrieval (Sec. 4.1) trains CLIP on pseudo-labels extracted from Wikipedia text and evaluates retrieval on held-out test images with a country-based split; the labels are not fitted from image pixels, so this is not a fitted input called a prediction. (2) Open-vocabulary segmentation (Sec. 4.2) uses OCR bounding boxes as weak supervision with OCR regions inpainted during training and is evaluated on 95 manually annotated images; this is standard weakly supervised learning, not self-definitional. (3) The semantic-segmentation benchmark (Sec. 4.3) uses manually annotated ground truth. (4) Generation experiments compare pretrained versus fine-tuned Stable Diffusion using FID, KMMD, CLIP similarity, and a user study; none of these metrics are constructed from the training labels. Self-citations appear only as related-work or metric references and are not load-bearing. The validity risks (pseudo-label noise, text leakage in retrieval) are measurement concerns, not circularity. No circular step can be identified.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The free parameters are curation and evaluation thresholds chosen by hand or on the data, not fitted physical constants. The axioms are domain assumptions about the reliability of internet metadata, OCR, and LLM extraction. No new physical or conceptual entities are introduced; the GAFs are an annotation scheme rather than an invented entity.

free parameters (6)
  • CLIP positive-score sum threshold = 0.5
    Used in both seed generation and dataset extension (Appendix B.3.2) to decide whether an image is a floorplan; chosen empirically and directly controls dataset composition.
  • CLIP top-5 floor-plan prompt test = all top five prompts contain 'floor plan'
    Seed generation criterion in Appendix B.3.2; empirically selected to create a highly accurate seed of 3,402 images.
  • ViT classifier filter threshold = filter out 10% of data
    Dataset extension threshold in Appendix B.3.2; chosen empirically to balance precision and recall in the final dataset.
  • Semantic similarity thresholds for segmentation targets = positive > 0.7, negative < 0.4
    Used in Appendix C.2 to assign OCR bounding boxes as positive or negative targets for GAF text queries; thresholds are hand-picked.
  • mIoU binarization threshold = 0.25
    Set empirically for evaluation in Section 4.2; directly affects the reported mIoU numbers.
  • ControlNet condition scale and CFG scale = condition 0.5, CFG 15.0
    Inference settings for structure-conditioned generation (Appendix C.3); chosen to balance prompt adherence and condition fidelity.
assumptions (5)
  • domain assumption LLM-extracted pseudo-labels (building type, country, grounded architectural features) are accurate enough to serve as training and evaluation ground truth.
    Validated on only 100 samples with 85-96% accuracy (Section 3.3); the entire downstream evaluation depends on this premise.
  • domain assumption OCR detections inside detected floorplan and legend boxes correspond to semantically meaningful architectural features.
    Used to ground GAFs and create segmentation supervision (Sections 3.2 and C.2); false matches would corrupt the labels.
  • domain assumption Wikipedia and Wikimedia Commons metadata and text are a reliable source of building semantics.
    The dataset is scraped from these sources (Section 3.1); if the metadata is wrong, the pseudo-labels are wrong.
  • domain assumption Inpainting OCR text with Stable Diffusion removes leakage while preserving floorplan structure.
    Needed for the segmentation experiment to prevent the model from reading text; the paper acknowledges inpainting artifacts (Section 4.2, Figure 14).
  • domain assumption The test/train country split prevents building-level leakage.
    Stated in Section 3.3; images of the same building could still appear under different files or categories, which is not explicitly checked.

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Cite this review

Pith. "Pith review of WAFFLE: Multimodal Floorplan Understanding in the Wild." pith.science (2026). https://pith.science/paper/6W3ZRDUH

@misc{pith2026241200955,
  author       = {Pith},
  title        = {Pith review of: WAFFLE: Multimodal Floorplan Understanding in the Wild},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6W3ZRDUH}},
  note         = {Machine review of arXiv:2412.00955}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2412.00955 by the authors.

Figure 1
Figure 1. What can we understand from looking at these im [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Samples from WAFFLE. Above, we show images paired with their structured data, including the building name and type, country of origin, and their grounded architectural features. We also visualize the detected layout components (floorplan, leg￾end, compass, and scale, as relevant) overlaid on top of the images [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. We automatically extract legends and architectural fea [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (24 more)
Figure 4
Figure 4. Figure 4: Comparison of open-vocabulary segmentation probabil [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Examples for generated floorplans for various building types, using the prompt “A floor plan of a [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Boundary-conditioned generation. The first column shows images in [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Structure-conditioned generation. For each image pair, the first image displays a building layout condition, taken from the [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: The prompts used for gathering CLIP scores. ( [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: An example of an image which contains legend text, [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: Distribution of common building types extracted automatically (log scale), illustrating the rich semantics captured in [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: Distribution of the grounded architectural features (log scale), among almost 3K grounded images, 25K instances grounded, [PITH_FULL_IMAGE:figures/full_fig_p013_11.png]
Figure 12
Figure 12. Figure 12: Number of samples per country (log scale) in [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]
Figure 14
Figure 14. Figure 14: An example of in-painting to preprocess data for the [PITH_FULL_IMAGE:figures/full_fig_p014_14.png]
Figure 15
Figure 15. Figure 15: A sample question from our user study on text￾conditioned floorplan generation. model’s UNet weights. Images and conditions are resized using the method described above. We train the model for 20K iterations with a batch size of 4, a learning rate of 10−5 and Adam opt…
Figure 16
Figure 16. Figure 16: Benchmark for semantic segmentation (over the [PITH_FULL_IMAGE:figures/full_fig_p016_16.png]
Figure 17
Figure 17. Figure 17: Additional results on the semantic segmentation benchmark. Images are annotated according to [PITH_FULL_IMAGE:figures/full_fig_p017_17.png]
Figure 18
Figure 18. Figure 18: Wall segmentation results, comparing the CubiCasa5K (CC5K) [18] baseline segmentation model that uses a ResNet backbone to a modified architecture that uses a Diffusion model, as described in Section C.5. The Diffusion-based model yields re￾fined wall predictions, as …
Figure 19
Figure 19. Figure 19: In each column: segmentation results on samples of our test set before (center) and after (right) fine-tuning on our data. [PITH_FULL_IMAGE:figures/full_fig_p018_19.png]
Figure 20
Figure 20. Figure 20: Additional comparisons of our segmentation probability map results on residential buildings with the strongly-supervised [PITH_FULL_IMAGE:figures/full_fig_p019_20.png]
Figure 21
Figure 21. Figure 21: Additional generated floorplans, showing diverse building types (provided on the left). The left-most column shows samples [PITH_FULL_IMAGE:figures/full_fig_p020_21.png]
Figure 22
Figure 22. Figure 22: Additional results for boundary-conditioned generation, showing a variety of shapes (shown on the left) and building types [PITH_FULL_IMAGE:figures/full_fig_p021_22.png]
Figure 23
Figure 23. Figure 23: Additional results for structure-conditioned generation, showing the effect of changing condition scale (CS) and CFG scales [PITH_FULL_IMAGE:figures/full_fig_p022_23.png]
Figure 24
Figure 24. Figure 24: Examples of images from our dataset with their building types (shown on the left) [PITH_FULL_IMAGE:figures/full_fig_p023_24.png]
Figure 25
Figure 25. Figure 25: The prompts used for LLM-based extraction of pGTs. Each [PITH_FULL_IMAGE:figures/full_fig_p024_25.png]
Figure 26
Figure 26. Figure 26: The prompts used for extracting the legend contents. The two left prompts are used for extracting data from Wikipedia, and the [PITH_FULL_IMAGE:figures/full_fig_p025_26.png]
Figure 27
Figure 27. Figure 27: The prompt used for legend simplification, serving to [PITH_FULL_IMAGE:figures/full_fig_p026_27.png]
Figure 28
Figure 28. Figure 28: The prompts used for extracting legends and architectural features from OCR detections. The two left prompts are used for [PITH_FULL_IMAGE:figures/full_fig_p027_28.png]

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.