Title: ArchCAD-400K: A Large-Scale CAD drawings Dataset and New Baseline for Panoptic Symbol Spotting
Venue: NeurIPS 2025
Accepted: yes
Keywords: CAD

panoptic symbol spotting

dataset
Abstract: Recognizing symbols in architectural CAD drawings is critical for various advanced engineering applications. In this paper, we propose a novel CAD data annotation engine that leverages intrinsic attributes from systematically archived CAD drawings to automatically generate high-quality annotations, thus significantly reducing manual labeling efforts. Utilizing this engine, we construct ArchCAD-400K, a large-scale CAD dataset consisting of 413,062 chunks from 5538 highly standardized drawings, making it over 26 times larger than the largest existing CAD dataset. ArchCAD-400K boasts an extended drawing diversity and broader categories, offering line-grained annotations. Furthermore, we present a new baseline model for panoptic symbol spotting, termed Dual-Pathway Symbol Spotter (DPSS). It incorporates an adaptive fusion module to enhance primitive features with complementary image features, achieving state-of-the-art performance and enhanced robustness. Extensive experiments validate the effectiveness of DPSS, demonstrating the value of ArchCAD-400K and its potential to drive innovation in architectural design and construction.


Peer reviews:
comments: SUMMARY:
The authors introduce ArchCAD-400K, a large-scale CAD dataset with 413K annotated chunks from over 5,500 standardized drawings, created using a novel auto-annotation engine. They also propose DPSS, a new baseline for panoptic symbol spotting that fuses primitive and image features. DPSS achieves state-of-the-art performance, highlighting the dataset’s value for advancing CAD symbol recognition.

STRENGTHS AND WEAKNESSES:
[Strengths]
* This paper is easy written.
* It is an important research problem that is worth exploration.
* The experimental results are good compared with the existing methods.

[Weaknesses]
* It is suggested to add the section to introduce the training time, the use of training resource, and inference time.
* The proposed framework is evaluated only on well-structured and curated datasets. There is no assessment of how the model performs under more challenging or noisy CAD environments, such as incomplete layers, overlapping primitives, or corrupted metadata, conditions common in practical settings. Could the authors explain more on it?
* The experimental results are reported without standard deviations, confidence intervals, or multiple runs. This omission makes it difficult to assess the reliability and statistical robustness of the claimed improvements.
* I did not see much weaknesses in this paper and I am not an expert in this domain.

QUESTIONS:
Will the authors release the code, model, and dataset?

LIMITATIONS:
N/A
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a large-scale floor plan CAD drawing dataset called ArchCAD-400k, which contains 413K CAD chunks from 5,538 full drawings. In addition, the authors introduce a Dual-Pathway Symbol Spotter for CAD spotting, which simultaneously leverages rendered CAD image features (extracted by HRNetV2) and symbol primitive features (extracted by SymPoint) to perform symbol spotting in CAD drawings. Experimental results demonstrate that the proposed dataset improves model performance.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n1. The paper introduces a large-scale floor plan CAD drawing dataset, ArchCAD-400k, and demonstrates an efficient annotation pipeline.  \n2. It proposes a Dual-Pathway Symbol Spotter method for CAD symbol spotting, achieving state-of-the-art results.\n### Weaknesses\n1. Combining the advantages of image-based methods (CADTransformer) and point-based methods (SymPoint) and extracting features through a dual-path approach is reasonable, but the novelty is moderate and this design increases computational cost.  \nIt would be better if the authors could compare the computational speed of their proposed method with other SOTA methods such as CADTransformer and SymPoint. For high-resolution images, feature extraction is expected to be quite time-consuming.\n2. The design of the decoder is the same as SymPoint/SymPointv2, so proper citation should be added. The loss function involves multiple weights for balancing; it would also be important to include ablation studies showing the impact of these weights on the results and convergence.\n\nQUESTIONS:\nOne major contribution of this paper is providing a large-scale CAD dataset. Will this dataset be released in the future? If so, what is the release timeline?\n\nLIMITATIONS:\nThe authors did not discuss limitations in the main text. In my opinion, the biggest limitation of the proposed method is that the image-based feature extraction becomes time-consuming when processing high-resolution CAD drawings, especially compared to point-based methods such as SymPoint or SymPointv2.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a dataset and learning algorithm for panoptic symbol spotting: finding and categorizing symbols in 2D CAD drawings. The dataset is much larger in terms of total floorplan area than previous datasets. It was created semi-automatically by automatically proposing segmentations and labeling to expert reviewers for refinement (this process is also a contribution). The proposed symbol spotting architecture uses parallel CNN and Point Transformer backbones to jointly encode raster and primitive input that are fused before being decoded by a transformer decoder into the final panoptic spotting results. The proposed spotting algorithm outperforms previous methods over both the contributed dataset and the previously largest dataset.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The dataset collected is very large _and_ expertly curated, and because the curation strategy is documented, it should be possible for anyone to expand that dataset using the same methodology.\n- Is SOTA in the more flexible no-priors case, so should generalize better than existing methods to less clean data.\n\n\nWeaknesses:\n- The dataset proposed in this work reduces the granularity of categorization in some areas compared to previous datasets (e.g. collapsing many categories down into \"furniture\"\n- This dataset's claims to be the largest by an order of magnitude is true on the measure of \"chunks\", which is directly related to the total floorplan area covered, it is actually built off of fewer overall distinct drawings than FloorPlanCAD. This apparently comes from the inclusion of larger public and commercial buildings in the dataset, which may bias the dataset, and ultimately provide less diversity than is implied by the dataset's size\n- It is unclear how the initial proposal annotations given to experts for refinement are generated\n- Few qualitative results shown, and without connection to quantitative results to aid in qualitative understanding of what the quantitative values mean.\n\nQUESTIONS:\nFigure 4(a): Is this a breakdown by chunks or by source drawings? If it is by drawings, what does the breakdown look like by chunks? I am concerned about bias because the larger industrial buildings presumably make up a far larger percentage of chunks than smaller residential plans due to having larger floorplan areas.\n\nPlease explain how the initial proposals in the expert labeling process were generated. Is it done entirely from authored layer annotations, or are there other steps (for examples data-driven predictive methods) involved?\n\nWhat are the \"Thing\" and \"stuff\" metrics? These are never defined in the exposition.\n\nLIMITATIONS:\nI am unclear if the claim about no IP being shared on line 157 is true (e.g. do the floorplan's themselves constitute IP even with metadata and identifiable labels scrubbed?) The only note about how the raw data was collected is on 113: \"We carefully gathered ... complete CAD drawings from the industry,\" so it is difficult to evaluate the claims of 152-158 (and just looking at the example data from the supplemental, it seems likely that the individual chunks could be reassembled to reconstruct full floorplans).", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe work targets panoptic symbol spotting, a task useful in the context of engineering applications using real vector design data such as technical or architectural drawings. To address this task, the authors provide a dataset for training panoptic symbol spotting models that aims to close the gap w.r.t. the limits in data size in existing data collections for training deep learning models. The authors design and execute a new efficient data  collection and annotation pipeline to produce a 400K drawing-chunk dataset with symbol annotations across a variety of semantic classes and instances. The dataset is then employed to pre-train a high-performing panoptic symbol spotting model that shows favourable performance compared to a number of baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The authors deliver the largest dataset for panoptic symbol spotting. Technically, the dataset could be subsequently used for other tasks such as vectorization of architectural (technical) drawings, due to its large size, real nature, and availability of vector ground-truth.\n2. Several results around collection and benchmarking of the dataset are valuable. Specifically, the authors were able to collect the dataset quite efficiently, which has contributed to its large size. Moreover, the authors have demonstrated that using the dataset for pre-training for the panoptic symbol spotting task result in improved performance, even with \"vanilla\" models. \n\nWeaknesses: \n1. Insufficient description of the data selection and annotation procedure. Specifically,\n— Where were the source (raw) data obtained from? Were these data paid for, and if yes, what are the financial costs associated with acquiring the data? \n— In the main text, multiple references to \"standardized drawings\" were made, however, this term was not clarified and no reference was given to any definition from the literature. \n— What do the authors identify as a layer? How specifically were layers selected/separated from the background drawing? \n— What would be the implications of time savings on the quality of the final annotations? More generally, how trustworthy are the data annotations? I think the authors did not provide too much information regarding the inter-annotator agreement, or performance on any kind of a golden set, so it remains to be seen if the dataset has the claimed high quality. \n— Is the taxonomy of semantic classes borrowed from FloorPlanCAD work or is it defined separately for this project? \n\n2. Insufficient description of the design of the experimental evaluation:\n— During the split of the dataset into the train/val/test parts, these three parts are ensured to contain drawings entirely, so as to prevent data leak from train to test, etc. However, this potentially limits the balancing quality across these sub-datasets, particularly in terms of instance/class numbers available for training, validation, or testing. Were the splits class-balanced? \n— Because the CAD drawings offer symbols with varying geometric complexity, it might be interesting to study the impact of symbol complexity (e.g. number of primitives per symbol) on the ultimate performance. The same could be said about the rasterisation resolution of the images (e.g. how many pixels are sampled across a common symbol). Are there any results w.r.t. this?\n— Are performance measures defined in the pixel space, or in the symbol space? E.g. a large (complex) symbol might significantly alter number as it is sampled with many pixels, yet it can only be a single number.\n\nQUESTIONS:\nI would greatly appreciate the answers to the questions formulated in the \"Weaknesses\" part of the review. More specifically, \n1. More details on the design choices regarding data collection and annotation, and the characteristics of the collected data need to be discussed. I do believe that the data is fair quality and useful, yet I'd like to paper to make a more compelling point about its qualities/properties. \n2. A similar point could be made regarding the experimental evaluation, and in particular about data sampling, balancing, and overall preparation for this.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}