ArchCAD-400K: A Large-Scale CAD drawings Dataset and New Baseline for Panoptic Symbol Spotting
Authors: Ruifeng Luo
Zhengjie Liu
Tianxiao Cheng
Jie Wang
Tongjie Wang
Fei Cheng
Fu Chai
Yanpeng Li
Xingguang Wei
Haomin Wang
Shenglong Ye
Wenhai Wang
Yanting Zhang
Yu Qiao
Hongjie Zhang
Xianzhong Zhao
Venue: NeurIPS 2025 poster ()
Keywords: CAD
panoptic symbol spotting
dataset
Primary area: applications

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.

ArchCAD-400k: A Large-Scale CAD drawings Dataset and New Baseline for Panoptic Symbol Spotting
Ruifeng Luo 2,1" Zhengjie iiul.s,iaanxia Cheng Jie Wang?, Tongjie Wang?, Xingguang Wei3.6, Haomin Wang YanPeng Li?, Fu Chai l, Fei Cheng Shenglong Ye Wenhai
Wang", Yanting Zhang", Yu Qiao", Hongjie Zhang Xianzhong Zhao?.41
TTnggi University, 2 *Areplus East China Architectural Design & Research Institute Co., Ltd., Shanghai AI Laboratory, 'Shanghai Qi Zhi Institute "Shanghai Innovation Institute, University of Science and Technology of China, SShanghai Jiao Tong University, *Donghua University
bttps://githut com/ http//AcciA--LBBACCccc
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 draw- ings to automatically generate high-quality annotations, thus significantly reducing manual labeling efforts. Utilizing this engine, construct ArchCAD-400k, large-scale CAD dataset consisting of 413,062 chunks from 5538 standardized drawings, making it over 26 times larger than the largest existing CAD dataset. ArchCAD-400k boasts an extended drawing diversity and broader categories, of- fering line-grained annotations. Furthermore, we present for panoptic symbol spotting, termed Dual-Pathway Symbol Spotter (DPSS). It incorporates an adaptive fusion module to enhance primitive features with com- plementary image features, achieving state-of-the-art performance and enhanced robustness. Extensive experiments validate the effectiveness of DPSS, demonstrat- ing the value of ArchCAD-4006 and its potential to drive innovation in architectural design and construction.
baseline model
1 Introduction
For a long time, CAD drawings have served as the universal language of architectural design, enabling seamless communication among designers, engineers, and construction personnel through standardized graphical primitives such as arcs, circles, and polylines. Accurate perception of these primitives in 2D CAD drawings is essential for various downstream applications, including automated drawing review and 3D building information modeling (BIM) [I] 2 4] as this ability enables the optimization of CAD-based workflows while enhancing efficiency and precision in architectural design and construction processes[5] []
Building upon earlier research in symbol spotting 7 g 9 10, pioneering work formally defined the
task of panoptic symbol spotting [[]] for floor plan CAD drawings and established the benchmark
13 [4 15 [6]. However, the manual annotation of line- grained labels is highly time-consuming and labor-intensive process, severely limiting the dataset's scale and diversity. This bottleneck restricts the ability to train models that can generalize across diverse
dataset FloorPlanCAD [I]
"Equl contribution;
Corresponding Authors: wwu.zhanghonggieee.. wwweingggi@@mmmmooooooooooo gmail.com; x.zhao ww.haa@tongii.edu..cmm
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(a) Raw CAD Drawings
(b) Panoptic labeled Drawings
Figure I: An example of annotated drawings in ArchCAD-400K Each geometric primitive is assigned both semantic and instance labels, with distinct semantic categories represented by different colors and instances visualized through convex hull masks. The chunks extracted from complete drawings.
building types, varying spatial scales, complex layouts, and wide range architectural component categories, thereby hindering their applicability real-world scenarios.
To address these limitations, we propose an efficient annotation pipeline for line-grained labels. reducing annotation cost from 1,000 person-hours for 16K data to 800 hours for 413K. The core idea leverage the structured organization of floor plan drawings, including layers and blocks. to enable scalable and cost-effective large-scale annotation. Layers support bulk labeling via semantic grouping (e.g., doors, windows), while blocks allow instance reuse for repeated elements. As shown Figure the layer-block structure, inherently designed to enforce drawing standards and ensure consistency in architectural documentation. forms the foundational framework for our automated annotation pipeline. To ensure high data quality, we use completed drawings from top design institutions and fully vectorized annotation workflow, with expert review of automated outputs.
Based on the efficient and accurate data engine, we construct ArchCAD-400k, large-scale floor plan CAD drawing dataset for the panoptic sym- bol spotting task. The dataset contains 5,538 complete drawings meticulously selected from 11,917 industry-standee
drawings, with a total of 413,062 annotated chunks, surpassing Floor- PlanCAD scale by a factor of 26. While Floor- PlanCAD primarily focuses on residential build- ings, ArchCAD-400K covers a diverse range of building types, with residential structures ac- counting for only 14% and large-scale public and commercial facilities forming the majority.
(b)
(a)
Figure 2: Layer-Block Structure: (a) Layers organize
CAD drawings into functional categories. (b) Blocks define reusable elements for efficiency.
The average area of drawings in ArchCAD-4000 spans 11,000 m², significantly larger than Floor- PlanCAD's m2 average, with 51.9% ranging from 1,000 to 10,000 m² and 4.4% exceeding 100,000 m² Furthermore, ArchCAD-400 introduces a comprehensive semantic categorization, including 27 categories such structural components (e.g., columns, beams), non-structural elements (e.g., doors, windows), and drawing notations (e.g., axis lines, labels), with 14 categories each containing over million primitives. This extensive scale, diversity, and detailed annotation make ArchCAD-400k a robust resource for advancing Al models in construction industry. An example from ArchCAD-400k is illustrated in Figure] [
We further propose a novel framework for panoptic symbol spotting. named Dual-Pathwa Symbol Spotter (DPSS), which incorporates an adaptive fusion module to effectively enhance primitive features with complementary image features. This design enables the model to achieve superior performance and enhanced robustness compared to existing methods while demonstrating strong scalability and generalization capabilities on larger-scale datasets. In summary, our work presents the following contributions:
(1) We develop a highly efficient annotation pipeline specifically designed for floor plan CAD drawings, which generates high-quality annotations with improved efficiency compared to image-

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based manual annotation, sigmificantly reducing the annotation cost from 1,000 person person-hours hours for 16K data to 800 person-hours for 413K data.
(2) We introduce ArchCAD-4000, a large-scale floor plan CAD drawing dataset that surpasses the current largest FloorPlanCAD dataset by an order of magnitude in size and exhibits greater diversity in terms of building types, spatial scales, and architectural component categories.
(3) We propose DPSS, novel framework for panoptic symbol spotting that achieves state-of-the-art performance on FloorPlanCAD and ArchCAD-400K, surpassing the second-best method by 3% and 10%, respectively, with exceptional accuracy, robustness, and scalability.
2 Related Works
2.1 Floor Plan Datasets
Several datasets have been developed for floor plan analysis. SESYD 9 comprises 1,000 synthetic vectorized documents with ground truth annotations. FPLAN-POLY 10 contains 42 floor plans
derived from images [17 for spatial relationship analysis. Cubicasa provides 5,000 floor plan images annotated with over 80 object categories, focusing on residential layouts. RFP [18] includes 2,000 annotated floor plans with detailed room-level information for Asian residential buildings. FloorPlanCAD [TT offers 16,103 vector-graphie floor plans in ".svg" format, annotated across 35 object categories. LS LSSCAD I6 introduces test set of 50 full-size CAD drawings with an average area of 1, 1,000 square meters. However, these datasets are limited in scale and rely on labor-intensive annotation, hindering large-scale training.
2.2 Large-scale Vision Datasets
Large-scale vision datasets have significantly advanced deep learning models for complex visual understanding tasks 19 datasets such as ImageNet 19 (14M images, 21K categories) and COCO (20] (300K images with object detection, segmentation, and captioning labels) have been instrumental in developing state- of-the-art models for object recognition, classification, and scene understanding. These datasets provide rich annotations that enhance model generalization, improving performance across general and domain-specinn tasks. Nevertheless, large-scale vector-based datasets and efficient annotation processes for floor plan understanding remain scarce.
20
21
22
23
24
25 26 (2]] including floor plan recognition. Image-based
2.3 Panoptic Symbol Spotting
The panoptic symbol spotting task, initially proposed in 11. involves the simultaneous detection and classification of architectural symbols (e.g., doors, windows, stairs) in floor plan CAD drawings.
While traditional methods 7 focus countable instances (e.g., windows, tables), Fan et al. (II] extended this to uncountable objects (e.g., walls, railings), inspired by (28). They introduced PanCADNet, which integrates Faster R-CNN (29] for countable instances and Graph Convolutional Networks 330 for uncountable elements. Subsequently, Fan et al. [12] proposed CADTransformer, utilizing HRNetV2-Waa [31] and Vision Transformers [32] for primitive tokenization and embedding aggregation. Zheng et al. [1]] adopted graph-based representations with Graph Attention Networks for semantic and instance-level predictions. Liu al. [4] introduced SymPoint, exploring point set representations, later enhanced by SymPointV2 13 through layer feature encoding and position- guided training. In contrast, CADSpotting [16] densely samples points along primitives and employs Sliding Window Aggregation for efficient panoptic segmentation of large-scale CAD drawings. While these methods perform well on FloorPlanCAD, they struggle on our more diverse, complex, and large-scale dataset, highlighting the need for robust and scalable solutions.
3 Annotation pipeline of ArchCAD-400k
We carefully gathered over 11917 complete CAD drawings from the industry, covering a wide range of building types. From the initial drawings, 5.538 drawings were validated as strictly conforming to the layer-block organizational standards. Through automated annotation combined with expert refinement, we generated approximately 413,062 chunks with line- grained annotations, each sized
3

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Stage::
Semantic stago
Cle Ole
Stage2:
Instance ansotation stage
Bloks estracted
proposols (Aate)
progesal
Figure 3: Overall pipeline of the annotation process. Primitives are labeled by automated methods followed by
manual modification.
at 14mx14m aligning with the design of FloorPlanCAD [1]. The overall annotation pipeline of ArchCAD-400k is shown in Figure and detailed below.
3.1 Annotation Format
The ArchCAD-400k employs structured annotation format tailored for the panoptic symbol spotting task, as defined in [T] Each graphical primitive (c.g., line, arc, circle) in the drawing is characterized by a dual-identifier pair, (Ik, 2k), where l denotes its semantic category and 24 represents the instance identifier. Primitives sharing the same 2g value are considered to be part of the same instance.
3.2 Layer-Block Standardization Screening
Manual annotation of floor plans is inefficient due to crowded layouts and inconsistent symbols. Standardized drawings layers for bulk annotation (c.g., door layer) and blocks for identifying repeated instances, clarifying topological and semantic relationships for automation.
However, the feasibility of automated annotation depends on the standardization level of the drawings. Non-standare drawings can have ambiguous layer names and mixed primitives, leading to semantic confusion. Notably, professional design institutes typically maintain internal layering standards, which can normalized and processed to establish machine-inte inttrrrrante structures. Thus, we restrict our data to completed drawings from leading design institutions and apply layer naming validation algorithm against a reference table, discarding drawings with over 5% deviation. This dual quality control ensures standardization of the input data, providing solid foundation for future automated annotation based on block standardization. The reference table is provided in Appendix A.
3.3 Automated Annotation with Expert Refinement
The automated annotation process based layer-block standardization greatly reduces manual workload and enables large-scale annotation. However, its accuracy limited, especially with non-standard drawings that escape standardization. Semantic ambiguities and mixed primitives within layers often cause errors, necessitating human correction. To improve accuracy, we engaged 10 experienced architectural drafters with over 3 years of experience to refine annotations using a vector-basee interface. Unlike typical image-based annotation methods such as bounding boxes or polygons, direct vector editing avoids raster-to-vector conversion enrors and resolves issues like overlapping instances.
We adopted a two-stage annotation system as shown in Figure In the semantic stage, layers are categorized by names and content, with their semantics displayed in for expert review and correction. In the instance stage, key categories are isolated and instance masks are proposed from block information, then manually refined. We also developed tools to automatically check layer-block compliance, flagging enrors for quick human conrection. Using this system, annotated 5.538

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Table I: Comparison of ArchCAD-4000 existing CAD drawing datasets.
Scale
Data Format
Annotation
Elements
Dataset
Source
Size
Toaal Area
Raster
Vector
Type
Method
Architoctura
Structural
Notation
CELAN-POLY
x 10 m²
Human
SESYD 9 FlouePlanCAD
Synthetic
1000
<5 x 106 m²
Instance
Human
x
x
Industry
16K
1.6 106
Panoptic
Human
x
x
ArchCAD-4008
Industry
10'
/
Panoptic
drawings in 800 hours, which is over 10 times more efficient than the FloorPlanCAD dataset while maintaining high accuracy and reliability.
Ethical and Copyright Considerations. The dataset used this work is derived from architectural or design drawings, which may contain potentially sensitive or proprietary information. To ensure compliance with ethical and copyright standards, we apply strict data anonymization before training or release, including removal of identifiable text and irreversible obfuscation of metadata, so that the original content of any individual project cannot be reconstructed. All data is used solely for research under academic fair use, and no raw data that may infringe copyright or IP rights is released. These measures ensure compliance with ethical standards and protection of PII.
Exploring ArchCAD-400k
We compare our ArchCAD-400k with existing datasets, as summarized in Table] Publicly available datasets, such as SESYD (. FPLAN-POLY [10], and FloorPlanCAD [11], exhibit limitations in terms of source, scale, data format, annotation type and method, as well as the range clements included in floor plans. These constraints affect their applicability to real-world architectural analysis.
4.1 Extended Building Diversity
Previous floor plan datasets predominantly originated from residential buildings, with primary emphasis on indoor layouts. This limitation constrained their applicability in analyzing a wider variety of project types. In contrast, our dataset curated from a more diverse array buildings, as illustrated in Figure 4al with residential structures comprising only 14% of the total. A substantial portion consists large-scale public and commercial buildings, including office complexes, industrial parks, and other expansive facilities. This diverse composition provides a richer and more representative architectural dataset, enabling applications across a wider variety of building scenarios.
Offec Sedding
12%
17.1%
Uin Res Aieii
Traceraaioo
Calural Edcasin
1966- tha
size. (9tt))
loite Sitao
20000- 1ogten
Vida
Caceuu Caroli
Betdiigg (o))
(a)
(b)
Figure 4: (a) Different project types of drawings; (b) Area distribution of drawings; (c) Number of annotated primitives 27 semantic classes, where bar colors match each class's visualization.
5

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4.2 Larger Data and Spatial Scale
ArchCAD-400k stands out for its unprecedented scale, comprising 5,538 complete drawings and 413,062 chunks, surpassing FloorPlanCAD'ss 15,663 chunks by over 26 times. This substantial scale significantly enhances the robustness and generalization capabilities of models for the spotting task.
In addition to the data scale, ArchCAD-400k features much larger drawings compared to existing datasets, which are generally restricted to small areas under 1,000 m². The average drawing in dataset spans 11 11,000m² closely reflecting real-world architectural dimensions. As shown in Figure 4b 51.9% of the drawings cover areas between 1,000 and 10,000.m², while 4.4% exceed 100,000 m representing wide spectrum of building areas and configurations.
4.3 Diverse Element Types
Traditional floor plan datasets typically focus on common non-structural elements, such as furniture, equipment, and decorative features. While such categorizations are sufficient for residential interior layouts, they often fail to capture critical components when applied to more diverse building types, particularly in complex architectural or industrial settings.
To address this limitation, our ArchCAD-400k adopts a comprehensive semantic categorization that extends beyond traditional architectural elements. Specifically, introduce annotations for structural components (e.g., columns, beams, and holes) and drawing notations (e.g., axis lines, labels, and markers) in addition non-structural elements. Structural components fundamental to reconstructing the overall building structure they define the load- bad--nering framework and spatial organization of the architectural design. In contrast, drawing notations are ubiquitous in real-world drawings and play a critical role in ensuring accurate interpretation and practical implementation. This tripartite categorization, encompassing non-structural, structural, and notation elements, enables a broader range of applications, including indoor navigation, architectural design, and structural analysis.
ArchCAD-400k provides detailed annotations for 27 categories, with the distribution of primitives across these categories visually represented in Figure 4c Notably, 14 of these categories each encompass more than million primitives, underscoring the extensive scale of our dataset. Addi- tionally, our dataset includes 7 countable instance categories. While this count fewer than that of FloorPlanCAD, our dataset surpasses it in terms of diversity and complexity within each category, presenting more formidable challenge. For instance, we consolidate various fine-grained furniture types from FloorPlanCAl such as chair, table, and bed, into a single unified "furniture" category. Visual examples of the categories can be found in Appendix A.
Method
To enhance panoptic symbol spotting on large-scale datasets, we propose Dual-Pathway Symbol Spotter (DPSS), robust framework free from prior knowledge like color or layer cues, As shown in Figure DPSS employs a dual-path feature extractor: an image branch encodes rendered graphics for global context, while point cloud branch captures geometric details by treating primitives points. An adaptive fusion module aligns these multimodal features, and a transformer decoder performs panoptic segmentation for precise symbol spotting in complex diagrams.
5.1 Two-stream Primitive Encoding Module
The extraction of high-quality features for vector graphic panoramic segmentation is crucial as it directly affects segmentation performance. Unlike raster images, vector graphics consist of primitives, yet research on their modeling remains limited, highlighting the need for better feature extraction methods. CADTransformer [12] rasterizes vector graphics and samples features at the primitive centers, while SymPoint [14] encodes primitives as points using geometric attributes and point cloud encoder (33. Image-based approaches capture semantics well but struggle with fine-grained tasks, whereas point-based methods handle instances effectively but are less suited for sparse structures like walls. To address this, we combine visual and geometric encoding. Our visual encoder, based on HRNetV2 [34], extracts feature maps from rendered images. Mcanwhile, Point Transformer-

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Primitive
Point Transformer backbone
Transformer Decoder Layer
000000
Prinatve Gumas
Leamable queries
Panoplic spotiin result
Raster image
CNN backbone
Prestid koulii
(a) DPSS Framework
(b) Adaptive fusion
Figure 5: The overall architecture of the proposed Dual-Pathway Symbol Spotter (DPSS). CAD drawings are encoded through both point backbone and image backbone. Features from dual pathways are aligned and fused in adaptive fusion module, then pass through a transformer decoder to generate mask predictions and classifications at primitive level.
inspired point cloud encoder generates geometric features for each primitive, ensuring a more comprehensive representation.
5.2 Adaptive Fusion Module
We use an image sampler to extract semantic features for each primitive. Given the primitive center (Zie Me) and the feature map F H#*WXC the sampler produces point-wise image features V. Since sampling points may fall between pixels, bilinear interpolation is applied:
V = (C(N)))))
where Vp is the extracted feature, K. is the interpolation function, and FN(y') denotes neighboring pixels. The combination of primitive and image features is challenging due to size variations, noise, and overlapping. To address this, we introduce geometry-guided fusion layer. First, graphic features X, and image features Vp transformed via MIP, then concatenated and pass through activation function a. A weight matrix w is computed as:
w = a (Wi tanhhnW22X + W&V,)),
where W1, W2, W3 are learnable parameters. Finally, the weighted features yield the representation:
Up = concat(X,, VV))
5.3 Decoder and Loss Function
The decoder is designed based the architectures of DETR (51 and Fask2Former 36
achieving
panoptic symbol spotting by predicting primitive-level masks along with their categories. The loss function composed of a standard cross-entropy loss (Lets) for class predictions, a binary cross- entropy loss (Lace), and Dice loss (Laice) (37] for mask predictions. The overall loss is formulated as weighted sum the three losses Adt Jidts + >bee + Adiea where Adls Abces and Adice denote the weight for each loss term respectively.
6 Experiments
We split our ArchCAD-400k dataset into training, validation, and test sets using 7:1:2 ratio, ensuring that each drawing and its corresponding annotations appear in only one split. This results in 289,144 annotated samples for training, 41,306 for validation, and 82,612 for testing. Following the definition of panoptic symbol spotting, we evaluate the model performance using Panoptic Quality (PQ), Segmentation Quality (SQ), and Recognition Quality (RQ). The formulation of these metrics can be found in [T]. evaluate model performance on ArchCADD-400k, we compare existing methods and proposed method DPSS, with detailed results presented below.

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Table 2: Panoptic symbol spotting results FloorplanCAD
dataset
Additional prior inputs
Total
Thing
Stuff'
Method
PQ
RQ
PQ
SQ
RQ
PQ
5Q
RQ
SymPointV
90.1
96.3
93.6
90.8
96.6
94.0
80.8
90.9
88.9
CADSpotting
88.9
93.0
89.7
93.2
80.6
89.7
89.8
DPSS
89.5
96.2
93.1
90.4
96.6
93.5
79.7
91.1
87.5
CADTransformer
w/o
68.9
88.3
73.3
78.5
94.0
83.5
58.6
81.9
71.5
GAT-CADNeI [5
w/o
73.7
91.4
80.7
SymPoinl [4]
w/o
83.3
91.4
91.1
84.1
94.7
88.8
48.2
69.5
69.4
SymPointV2113
w/o
83.2
91.3
91.1
85.8
92.5
92.7
49.3
20.3
70.1
DPSS
w/o
86.2
93.0
92.6
88.0
94.1
93.5
64.7
83.0
77.9
6.1 Implement Details
We adopt HRNetW48 [31] pretrained COCO-Stuff (38 the backbone for the visual branch. The point cloud branch uses the PointTrransformerV2 39 encoder pretrained on ScanNet 40
The model is trained 8 NVIDIA A800 GPUs and the optimizer is AdamW. On FloorplanCAD [1] dataset, we set batch size of per GPU and with a learning rate of 2 x 10-4 and a weight decay of 0.1. The model is trained for 50 epochs. On ArchCAD-400k, train the model for 10 epochs with learning rate of 2 x 10-4 and the same weight decay. Other baseline methods ArchCAD-400k are trained for 10 epochs under their default configurations. All models were confirmed to have converged before evaluation to ensure fair performance comparison.
set batch size of 4 per GPU and
Quantitative Evaluation
Quantitative comparison FloorPlanCAD. We evaluate multiple methods on the FloorPlanCAD dataset (Tables [ 2nd 3). under two scenarios: with and without prior information (such as layers and color). This distinction reflects how CAD drawings encode cecaatics-primitiie in the same layer or with similar colors often share categories.
SymPointV2 improves SymPointV1 by adding layer encoding, while CADSpotting samples primitives based on color and position. As shown in Table DPSS performs comparably to these methods when priors are present. Without them, however, SymPointV2 suffers notable drop, whereas DPSS stays robust-achieving 3% higher overall PQ and 15% higher "Stuff" PQ than SymPointV2, demonstrating better generalization. In Table DPSS also clearly outperforms others in Semantic and Instance Spotting without priors, including some classical semantic segmentation and instance detection algorithms.
While our method benefits from prior information, we prioritize performance without it, as this better reflects real-world scenarios where such metadata is often unavailable.
Table 3: Semantic and instance spotting results on FloorPlanCAD
Additional prior inputs
Semuntic spotling
spotting
Method
F1
wFL
AP50
AP75
mAP
SymPointV2 15 CADSpotting
89.5
88.3
71.3
60.7
60.1
93.5
93.9
72.2
69.0
DPSS
93.1
93.2
74.8
71.0
70.8
DeepLabv3+R101
68.8
71.4
DINO 44
64.9
54.9
47.5
CAD'Trastormer
w/o
82.2
80.1
SymPoint [1
w/o
86.8
85.5
66.3
55.7
52.8
SymPointV2 13
w/o 87.0
86.3
66.4 4
57.7
57.5
DPSS
w/o 92.0
93.2
67.0
61.8
61.5
Quantitative comparison on ArchCAD-400k. We evaluate various methods on our ArchCAD. 400k dataset (Table comparing DPSS with CADTransformer,, SymPoint, and SymPointV2, all without using layer or color priors, to better reflect real-world conditions. Compared to FloorPlanCAD, 4rchCAD-400kk more challenging, leading to overall lower performance. For instance, SymPointV2 drops from 83.2% PQ on FloorPlanCAD to 60.5% on ArchCAD-400L -a decrease of over 22%.

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Table 4: Panoptic, semantic, and instance symbol spotting results ArchCAD-400K
Total
Thing
Stuff
Semantic Spotting
Instance Spotting
Method
PQ
5Q
RQ
PQ
SQ
RQ
PQ
SQ
RQ
F1
wFl
AP50
AP7S
mAP
CADTransoomen
12
60.0
89.7
66.9
52.5
83.6
62.7
70.1
96.7
72.5
84.1
81.4
SymPoinl 14
47.5
86.1
55.3
51.4
91.9
55.9
39.9
73.9
54.0
76.8
62.0
36.1
30.9
30.8
SymPointV
60.5
88.0
68.8
62.4
91.7
68.1
52.8
73.7
71.7
69,8
69.3
44.9
40.2
39.7
DPSS
70.6
90.2
78.2
65.6
92.4
70.9
77.6
87.8
88.4
87.8
84.1
45.6
41.1
40.7
DPSS shows clear advantages in scalability and robustness, In semantic spotting, achieves an Fl of 87.8%, outperforming CADTransformer (84.1%), SymPoint (76.8%), and SymPointV2 (69.8%). In instance spotting, it reaches 40.7% mAP, surpassing SymPointV2 (39.78 (9.7% and SymPoint (30.8%). For panoptic symbol spotting, DPSS attains total PQ of 70.6%, significantly ahead of SymPointV2 (60.5%) and SymPoint (47.6%). Notably, Stuff PQ reaches 77.6%, 24.8% higher than SymPointV2.
In summary. DPSS consistently outperforms baselines across all tasks, even without prior information. Its strong performance the more complex ArchCAD-400K highlights its scalability, robustness, and practical applicability to real-world CAD scenarios.
6.3 Ablations
Ablation studies on DPSS. To demonstrate the effectiveness of the proposed DPSS, we conducted ablation studies focusing on different encoding strategies. Specifically, we evaluated the impact of using only the primitive encoder, only the image encoder, and the combination of both, shown in Table 5 (Lines 1-3). The integration of both the image encoder and the primitive encoder yields 1.3% improvement in PQ compared using the primitive encoder alone, and 2.1% gain over using only the image encoder. These results highlight the complementary nature of the two encoding branches. Furthermore, we investigated the role of the adaptive fusion module by replacing it with simple concatenation strategy for combining image and primitive features, as shown in Table (Lines 3-4). The results show that incorporating our adaptive fusion leads significant 3.2% increase PQ, underscoring its effectiveness in enhancing feature integration.
Table 5: Ablation experiments on FloorPlanCAD
Primitive Encoder
Image Encoder
Adaptive Fusion
PQ
RQ
SQ
81.7
90.1
90.6
80,9
89.4
90.6
83.0
90.1
92.1
86.2
93.0
92.6
Generalization ability of ArchCAD-400k. To validate the generalization ability of ArchCAD-400kk We implement DPSS on FloorPlanCAD([T] dataset with dual-pathway encoder pretrained on ArchCAD-400k. Results are illustrated in Figure 6a The pre-trained model achieves faster and more stable convergence, surpassing the performance of non-pre-trained models at 50 epochs within 30 epochs. suggests that ArchCAD-400k covers FloorPIanCAD and furthermore has more diverse patterns and properties for models generalize across datasets.
Scaling performance of ArchCAD-400k. We conducted experiments to validate the scaling law in the context of panoptic symbol spotting. Using subset of our ArchCAD -00kk and adopting DPSS as the baseline network, we evaluated the training performance across different data scales, ranging from 10K samples to the full 400K samples. As demonstrated in Figure (bb and 6c within 10K to 400K data range, doubling the dataset size consistently reduces the loss by 2.33 and improves the PQ metric by 6.40% in average. The results indicate that, for panoptic symbol spotting tasks, large-scale datasets significantly enhance model performance.
6.4 Qualitative Results
The qualitative results on our ArchCAD-400k are illustrated in Figure In the presented cases, DPSS demonstrates superior performance than SymPointV2 [K]. In architectural diagrams (row 1), characterized by diversity of component types and numerous interfering lines, DPSS is capable

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(a)
(b)
(c)
Figure 6: (a) Comparison of model performance: pretrained vs. non-pre-trained on ArchCAD-400k; (b) Loss
convergenee across different datasct sizes; (c) Panoptic quality across different dataset sizes.
(a) Ground truth
DPSS
(c) SymPointV2 (d) Ground truth
(e) DPSS
(f) SymPointV2
Figure 7: Qualitative comparison between DPSS and SymPointV2 13 on FlaorPlanCAD((inn)) and ArchCAD-
400k (Line2).
of filtering out such disturbances to achieve precise spotting results. structural drawings (row 2), where the shapes of components exhibit high similarity, DPSS effectively integrates contextual information to discern semantic differences between analogous components.
Conclusion
In this work, address the challenges in panoptic symbol spotting for architectural CAD drawings by introducing an efficient annotation pipeline, a large-scale dataset (ArchCAD-400k) and a novel framework (DPSS). Our pipeline reduces annotation costs, enabling the creation of ArchCAD-400K, which surpasses existing datasets in scale and diversity. With 413 413,062 annotated chunks from 5,538 drawings, ArchCAD-400k covers diverse building types and spatial scales, advancing Al models in architectural design. DPSS, equipped with an adaptive fusion module to effectively enhance primitive features with complementary image features, achieves state-of-the performance on FloorPlanCAD and ArchCAD demonstrating superior accuracy, robustness, and scalability. We hope that our ArchCAD-400k will catalyze further progress this domain.
Acknowledgements
This work was supported by the Shanghai Qi Zhi Institute (SQZ202309), the National Natural Science Foundation of China (No. 62206046), and the Shanghai Committee Science and Technology (No. 22YF1461500),
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A More Information About the ArchCAD-400k
A.1 Detailed description of categorization
In Figure g we present some visual examples of content from each category. Engineering symbols from different categories can share great similarity. For example, simple rectangles can represent columns, holes, foundation, or furniture. Similarly, pairs of parallel lines might denote a pipeline, beam, wall. At the same time, some instances can drawn in diverse forms, As illustrated in Figure [8] there are at least six different ways to represent door, and the appearance of stairs can vary greatly different scenarios.
Handell
Curtii wall
Pipe
Wixdow
Elevator
ostum
Windon cunann
Cocartte bozn
Sain
Stoc
Robar
lincing
Embelmene
Plouraae
Axds
Stecl colorn
Parkinu spot
Synbok
Figure 8: Visual examples of content each category.
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A.2 Additional information about the annotation pipeline
Our annotation pipeline adopts regex matching to map layer names semantic classes. The layer names in standardized drawings have hierarchical format like In T we list part of the standardized naming table for doors, walls, and stairs. Categories can be matched through the keyword in the table.
Table 6: Examples of correspondence between layer names and semanties
Semantic label
Standard layer specifications
Content description
A-DOOR A-DOOR-FRAME
The surface structure of the door. The internal steel frame supporting the door. The line indicating the position of the door beam. The layout or design of the fireproof roller shutter.
Door
A-DOOR-HEAL
A-DOOR-ROLL
A-WALL-BLOK
Masonry block wall structural stability. Concrete wall with high strength and fire resistance. Lightweight partition with stud framing and drywall.
A-WAL-COONC
A-WALL-STUD
A-WALL-PRHIS
Partial height wall for spatial division.
A-WALL-SCRN
Metal wall for electromagnetic shielding.
A-WALII-FINI
Final surface treatment or cladding of wall.
A-WALL-INSU
Insulation layer thermal acoustic performance.
Wall
A-WALL-TPIN
Partial- Phrthal-height wall for restroom privacy.
A-WALL-EXPL
Reinforced wall to withstand blast pressures.
S-WALL-LINE
The outline or framework of a wall in structural drawings.
S-WALL-HATCH
Represents the filling material pattern of wall in structural drawings
A-STRS-TREA
Stepping surface and vertical connector in stair construction. Mechanical moving stairs for vertical transportation between floors.
A-STRS-ESE ESSCL
Stairs
A-STRS-HRAL
Safety rails along stair edges for fall prevention. Outline representing stair geometry structural drawings.
S-STRS-LINE
22

---

A.3 Additional examples of ArchCAD-400k
example of well-labeled large architectural drawings exists the main text. We further show two labeled drawings in our dataset in Figure 9 types of components.
Each of them covers an area over 8000 m², with various
(a) Raw CAD Drawings
(b) Panoptic labeled Drawings
Figure 9: Additional example of the annotated drawings.
23

---

B More Evaluations
B.1 Analysis on Computational Efficiency
To evaluate the computational efficiency of the proposed method, we conducted additional experi- ments measuring inference latency across representative approaches. Our model operates on images with resolution of 700x700, which introduces only moderate computational overhead. Compared with SymPoint- V2 [3], the proposed method increases inference latency by approximately 30%, while achieving 3.0% and 10.1% % higher panoptic quality on ArchCAD-4000 and FloorPlanCAD, respectively. These results indicate that the proposed approach attains favorable balance between computational efficiency and segmentation accuracy.
Table 7: Comparison with existing methods on FloorPlanCAD and ArchCAD-400K.
Method
PQ (FloorPlanCAD)
PQ (ArchCAD-4000K
Average Latency
CADTransformer
68.9
60.0
159ms
SymPoint 44
83.3
47.6
666ms
SymPoint-V V2 13
83.2
60.5
95ms
DPSS (ours)
86.2
70.6
12
24

---

B.2 Additional Quantitative Evaluations
We present the detailed experimental results of various methods on the ArchCAD-400k, including the panoptic quality (PQ) for each category as well as the mean IoU (Intersection over Union) for each category. The IoU for each category is obtained by calculating the intersection over union between the predicted panoptic segmentation masks and the ground truth masks. Different methods excel handling different types of objects, DPSS achieves higher PQ and loU metrics. For the current spotting results, there is still significant room for improvement.
Table 8: Quantitative results for panoptic symbol spotting of each category on ArchCAD-400K
DPSS
CADTransfommer
SymPuint
14
SymPoinIV2 [33
class
IoU
PQ
IoU
PQ
IoU
PQ
IoU
PQ
symbol
84.4
89.8
62.4
70.7
76.4
80.9
74.2
79.3
axis
90.8
85.4
23.2
32,9
58.3
56.2
71.8
83.7
door
64.4
75.9
53.2
64.9
63.9
75.6
82.6
79.1
floor slab
40.8
85.6
14.4
80.3
20.9
82.8
47.7
69.4
elevator
45.6
77.6
54.6
73.3
28.0
stairs
60.4
65.4
35.3
45.2
41.3
55.4
43.7
78.0
furniture
72.5
88.7
66.5
73.3
63.2
83.9
43.4
54.0
hole
61.1
70.2
32.1
40.0
59.6
53.8
50.2
60.7
window
47.8
57.2
52.4
55,0
64.7
68.4
57.8
70.8
curtain wall
65.9
83.7
48.8
61.4
50.3
72.6
33.9
39.1
wall
73.4
75.6
52.7
57.7
70.7
concrete column
65.8
83.6
59.5
66.8
64.4
71.6
63.9
69.8
steel column
48.6
80.3
50.7
71.8
52.9
82.8
51.1
79.2
concrete beam
81.8
81.7
43.8
45.0
61.5
62.1
48.3
77.9
steel beam
79.1
73.0
19.7
34,9
47.5
52.2
71.5
77.4
parking spot
66.8
73.2
57.9
76.9
46.0
79.3
72.1
69.0
roof
3.5
28.7
50.1
55.6
base
85.0
90.3
65.8
75.2
71.2
83.1
16.3
21.3
bracing
19.6
33.8
7.8
20.0
20.2
23.2
67.6
81.1
rehar
72.7
93.1
39.4
69.4
56.5
81.1
53.5
75.9
equipment
37.2
45.8
1.0
7.0
30.8
31.8
28.4
29.1
handrail
73.6
65.9
31.2
31.0
40.2
42.7
70.3
85.8
pipe
44.4
48.3
32,9
34.4
51.7
41.7
52.5
window curtain
50.4
80.3
58.6
72.1
49.2
67.7
46.3
47.5
construction joint
0.0
5.3
0.0
0.3
2.5
4.8
4.1
3.7
embedment
0.0
0.0
0.0
0.0
0.0
0.0
1.2
2.8
hydrant
70.7
71.7
68.6
64.5
74.1
69.4
49.5
34.0
overall
70.6
67.04
47.6
49.1
60.5
59.1
60.0
60.8
C Limitations and Future Work
Although the proposed DPSS demonstrates strong performance on the panoptic symbol spotting task, some limitations remain. Notably, the current approach cannot process entire vector drawing in single pass, which leads to high computational overhead. With the availability the ArchCAD-400K dataset, complex and comprehensive research questions related to panoptic symbol spotting and the analysis of engineering line drawings can be explored. For example, future directions include the pre-trained models tailored for CAD vector graphics, and efficient inference strategies to handle large scale real-world drawings.
25


Reviews:
[{"review_id": "fxGkHzcnjW", "reviewer": "Reviewer_WEoK", "summary": "The 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.", "questions": "I 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.", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "I have carefully read and understood the comments regarding my raised concerns, and have skimmed through the other reviews and discussions. I acknowledge that it was unfortunate that I could not answer the authors's concerns more promptly and engage in discussions. \n\nI would like to raise my final rating to 5-accept, as overall my concerns were pretty much addressed.  Yet, below I'd like to outline my final concerns and recommendations over each of the questions. Perhaps the authors would  consider this worthy of further refinement for the final version of the paper. \n**Q1, Q2:** I understand the idea, but as a reader I would like to have a clear idea what these \"top-tier architectural design institutions\" were, and which guidelines they follow for production of engineering drawings, organizing them into layers, etc, if such information is available. It would be useful to provide context wrt the data background and the type of practices involves in the original development of the drawings, otherwise the dataset risks _not representing global architectural diversity_  as outlined in the ethics review. \n\n**Q3—Q5:** I think the answers are adequate, and as a reader, I would expect clear definitions in the paper. \n\n**Q6—Q9:** I appreciate the authors making an effort to provide more experimental evidence wrt the performance of their models  and the statistics of the dataset. I thank the authors and hope this this information could be added to the final version of the paper.", "rebuttal": "**Q1: About the acquisition details of the dataset.**\n\n**A1:** Thank you for pointing this out. The raw CAD data was collected from engineering drawings provided by top-tier architectural design institutions. This ensures both the professional quality and diversity of the dataset, making it representative of real-world CAD design practices.\n\nThe data was made available to us under a collaborative research agreement and no direct monetary cost was involved in acquiring the dataset.\n\n**Q2: About the concept of layer and the method used for layer extraction.**\n\n**A2:** In CAD drawings, a layer refers to a categorical division of graphical elements defined by engineers. The purpose of layer differentiation is to facilitate clearer organization of drawing content and enable selective rendering of specific components, thereby improving clarity and supporting more effective collaboration among team members.\n\nIn our pipeline, we extract layer information by programmatically parsing the CAD files, leveraging metadata embedded in the DWG/DXF formats. These layers are then used to isolate structural components for downstream analysis.\n\n**Q3: About the definition and clarification of the term \"standardized\" drawings.**\n\n**A3:** We appreciate the reviewer's careful reading. The term \"standardized\" drawings refers to CAD drawings that follow a consistent layering convention, where each type of building component is assigned to a specific layer based on an internally defined standard. This standard ensures semantic consistency across drawings and facilitates automated parsing. A brief explanation of this standard is provided in Appendix A.2. We will refine this statement in the revised version.\n\n**Q4: About the reliability of annotations, including inter-annotator agreement and gold standard validation.**\n\n**A4:** We employed several strategies to ensure the reliability and accuracy of the annotations during the annotation process:\n\n(1) Annotation Guidelines: A comprehensive annotation manual was developed, detailing the definition and visual examples of each category. Annotators were trained using this guide to maintain consistency. A portion of the manual is included in Appendix A.1.\n\n(2) Quality Control: All annotations underwent a secondary verification step. Given the complexity of CAD drawings, we implemented a hierarchical review process, where a senior annotator re-checked each annotated file. This procedure helped to eliminate labeling inconsistencies and reduce human errors.\n\n(3) Customized Annotation Pipeline: As mentioned in paper 3.3, before manual labeling, we apply a pipeline that extracts inherent information such as layers and blocks to generate annotation suggestions. This avoids labeling from scratch, improves efficiency, and reduces human errors, especially in densely labeled CAD drawings.\n\n**Q5: About the semantic categories.**\n\n**A5:** The semantic categories used in our project were independently defined based on domain requirements. Our primary criterion was whether the categorized elements are essential for reconstructing a Building Information Model (BIM). While some categories align with prior work such as FloorPlanCAD, our classification scheme was tailored to industrial CAD drawings and encompasses additional component types specific to structural and architectural design.\n\n**Q6: About the train/val/test split of the dataset.**\n\n**A6:** Thank you for raising this important point. We split the dataset at the chunk level, rather than the drawing level, to form the train/val/test sets. This decision was made based on two key considerations:\n\n(1) Low risk of data leakage:  Chunks assigned to different splits do not spatially overlap, which theoretically prevents any form of data leakage between training and testing. Although some chunks may come from the same large drawing, their sparse details result in insignificant correlation between chunks, keeping the risk of data leakage low.\n\n(2) Improved balance across splits:  Variation in both class distribution and drawing area can cause imbalance when splitting at the drawing level. Chunk-level splitting helps ensure better balance in instance counts and spatial coverage across splits.\n\nAs a result, class balance is easier to achieve and more controllable under the chunk-level splitting strategy.\n\n**Q7: About the relationship between symbol complexity and model performance.**\n\n**A7:** Thank you for raising this insightful point. We agree that understanding how symbol complexity and image resolution affect model performance is an interesting and helpful for understanding the problem.\n\nTo investigate the impact of symbol complexity, we analyzed the number of primitives within each symbol as a proxy for complexity. Specifically, we partitioned the test symbols into eight subsets based on the percentile of primitive count and then computed evaluation metrics on each subset.\n\n| Symbol Complexity |  PQ  |  SQ  |  RQ  |\n| :------------------------: | :--: | :--: | :--: |\n|           \\[1,4)           | 82.5 | 94.4 | 87.5 |\n|           \\[4,5)           | 90.9 | 95.8 | 94.9 |\n|           \\[5,6)           | 87.5 | 93.5 | 93.7 |\n|           \\[6,9)           | 88.7 | 93.5 | 94.9 |\n|           \\[9,13)          | 88.9 | 94.0 | 94.5 |\n|          \\[13,21)          | 86.1 | 94.2 | 91.4 |\n|          \\[21,46)          | 86.9 | 93.2 | 93.3 |\n|           \\[46,)           | 77.1 | 85.3 | 90.4 |\n\nFrom the results, we can find that there exists a certain correlation between symbolic complexity and model performance. In brief, both extremely simple and extremely complex symbols represent performance bottlenecks for the model.\n\nFor symbols with low complexity within the range [1, 4), the model exhibits relatively low recognition quality (RQ), likely due to the lack of distinctive structural features. Within the moderate complexity range [4, 46), the model demonstrates relatively stable performance. However, in the highest complexity split [46, ), the performance degradation is primarily attributed to a decline in segmentation quality (SQ), as the high structural complexity leads to intricate and ambiguous symbol boundaries in the symbolic space.\n\n**Q8: About the relationship between image resolution and model performance.**\n\n**A8:** Thank you for raising this insightful point. We conducted a set of experiments to evaluate the influence of raster image resolution on model performance. Specifically, we varied the resolution used for rasterizing the input vector graphics while keeping all other components of the pipeline unchanged. The results are summarized as follows.\n\n| Raster Size |  PQ  |  SQ  |  RQ  |\n| :------------------: | :--: | :--: | :--: |\n|        384×384       | 69.6 | 79.0 | 88.1 |\n|        700×700       | 86.2 | 93.0 | 92.6 |\n|       1024×1024      | 85.8 | 92.3 | 92.9 |\n\nThe results suggest that increasing the resolution is beneficial to model performance when starting from a relatively low baseline. However, beyond a certain threshold further increasing the resolution yields diminishing returns and may lead to degradation in efficiency.\n\nThis phenomenon can be attributed to the inherent characteristics of CAD drawings: since they consist primarily of discrete, line-based primitives, the amount of spatial detail is inherently limited. Once the resolution is sufficient to capture and distinguish these primitives, further increases do not yield additional useful information.\n\n**Q9: The evaluation metrics are defined in pixel space or symbolic space?**\n\n**A9:** Thank you for raising this question. The evaluation metrics for panoptic symbol spotting are defined in the symbolic space. Their formal definitions can be found in [1], and they are commonly adopted as standard practice in prior work in the panoptic symbol spotting task.\nMore specifically, the key distinction between evaluation in pixel space and symbolic space lies in the way IoU is computed. For a symbol $s$ represented as a set of primitives $s= \\lbrace e_1,e_2,… \\rbrace $, the IoU between a predicted symbol $s_p$ and a ground truth symbol $s_g$ is calculated as:\n\n$$\n\\mathrm{IoU}(s_p, s_g) = \\frac{\\sum_{e_i \\in s_p \\cap s_g} \\log{(1+L(e_i))}}{\\sum_{e_i \\in s_p \\cup s_g} \\log{(1+L(e_i))}}\n$$\n\nwhere $L(e_i)$ denotes the length of the primitive $e_i$.\n\nThis formulation ensures that the metric is sensitive to geometric correctness at the symbol level, while also addressing the issue of symbol size imbalance. The use of a logarithmic scaling function $\\log{(1+L(e_i))}$ prevents large symbols from disproportionately dominating the metric.\n\n**References**\n\n[1] Zhiwen Fan, Lingjie Zhu, Honghua Li, Xiaohao Chen, Siyu Zhu, and Ping Tan. Floorplancad: A large-scale cad drawing dataset for panoptic symbol spotting. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10128–10137, 2021."}, {"review_id": "Xe5r0nQ3ov", "reviewer": "Reviewer_FDkf", "summary": "This 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.", "questions": "Figure 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.", "limitations": "I 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).", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "I maintain my score of accept. In the rebuttal, the reviewers addressed my primary concerns about dataset bias and reproducibility of their labeling methodology, as well as promising to add more qualitative results to give context to the quantitative. If these and the clarification on the dataset size comparison to FloorPlanCAD are included in their final revision, I am happy to accept this paper.", "rebuttal": "**Q1: About the reduction in categorization granularity compared to previous datasets, such as merging multiple categories into \"furniture\".**\n\n**A1:** Thank you for the observation. We intentionally chose to reduce the categorization granularity for certain object types. For example, we grouped subtypes such as tables, chairs, and cabinets into a single furniture category. Our rationale is twofold:\n\n(1) Limited Utility: While structural elements such as walls, columns, and curtain walls are essential for architectural modeling and reconstruction, fine-grained distinctions among furniture types offer relatively limited benefit for these purposes. The impact of individual furniture subtypes on the global structure is minimal, making detailed categorization a lower priority.\n\n(2) Annotation Efficiency: Preliminary analysis revealed that the furniture class could be subdivided into over 20 distinct subtypes. However, annotating such fine-grained labels across large-scale datasets would introduce substantial overhead. Given the limited structural value of these distinctions, we opted for a more efficient, coarser labeling strategy.\n\nOverall, this reflects a conscious trade-off between annotation cost and semantic value, aligned with the objectives of our reconstruction task.\n\n**Q2: About the relationship between dataset size, the number of distinct drawings, and potential diversity bias due to the inclusion of larger buildings.**\n\n**A2:** We appreciate this important point. Our dataset contains a larger number of distinct drawings than FloorPlanCAD. FloorPlanCAD has 1,332 distinct CAD drawings. In contrast, our dataset includes 5,538 distinct drawings.\n\nBoth datasets are organized in a chunk-wise fashion, but the increased number of large buildings in our dataset reflects an expansion in both spatial coverage and complexity, rather than a reduction in diversity. We acknowledge the concern that larger buildings could skew the distribution; however, we take steps to mitigate this, as described in our response to Q3.\n\n**Q3: About the basis of distribution in Figure 4(a), and the potential bias introduced by chunk-wise representation.**\n\n**A3:** Thank you for raising this concern. Figure 4(a) indeed shows the distribution by source drawings, not by chunks. We recognize that the correlation between drawing type and spatial size could result in an overrepresentation of certain sources at the chunk level.\n\nTo address this, we conducted an additional analysis of the dataset with a chunk-wise breakdown, presented in the as follows.\n\n| Category | By Drawings | By Chunk |\n|:---------------|:------:|:------:|\n| Office Building  | 12% | 14% |\n| Industrial Park | 20% | 21% |\n| Transportation | 8% | 17% |\n| Hotel | 8% | 12% |\n| Commercial Complex | 8% | 13% |\n| Cultural Education | 12% | 6% |\n| Residential | 18% | 12% |\n| Others | 14% | 5% |\n\nThis analysis revealed that while there are some differences in proportions between drawing-wise and chunk-wise distributions, the semantic category distribution and spatial diversity remain well-balanced at the chunk level.\n\n**Q4: About the generation of initial annotation proposals.**\n\n**A4:** We appreciate the interest in our annotation workflow. The initial labeling proposals were generated entirely through CAD layer-based heuristics, without any machine learning or predictive components. \n\nSpecifically, we built a layer-to-category mapping table by consolidating naming conventions across various CAD standards and companies. This mapping enabled automatic pre-labeling of most graphical elements. Annotators then reviewed and refined these proposals based on context and visual correctness.\n\nA partial snapshot of this mapping table is provided in Appendix A.2. This rule-based pipeline ensured high initial labeling quality and interpretability, which was subsequently verified through multi-stage quality control.\n\n**Q5: About the qualitative results.**\n\n**A5:** We appreciate the reviewer's suggestion. In the revised version, we will include additional qualitative visualizations to better illustrate the strengths and limitations of our method. These visual examples will be carefully selected to correspond with the quantitative metrics, thereby providing more intuitive insights into what the reported numerical values reflect in practice.\n\n**Q6: About the definition and usage of the terms \"Thing\" and \"Stuff\" in evaluation metrics.**\n\n**A6:** Thank you for highlighting this point. The terms “Thing” and “Stuff” come from definition of panoptic symbol spotting [1].\n\n\"Things\" refer to countable, instance-level objects (e.g., columns, doors).\n\n\"Stuff\" refers to amorphous symbols of similar objects or material with semantic meaning (e.g., wall).\n\nEvaluating the two metrics separately allows for a more precise assessment of the model's performance on semantic and instance segmentation tasks, which is a common practice on this problem [1–3].\n\n**Q7: About the handling of intellectual property in the dataset.**\n\n**A7:** We acknowledge your concern and agree that raw floorplans could constitute IP, even if scrubbed of metadata and labels. During the data processing period, we applied several measures for IP protection:\n\n(1) Spatial Chunking: Floorplans are divided into localized chunks, containing only partial spatial context.\n\n(2) Cross-Source Shuffling: Chunks are decoupled from their original drawing context and mixed across different sources.\n\n(3) Text Anonymization: Any textual elements that could reveal project background (like project names, client identifiers, etc.) are removed.\n\nDue to this process, it is extremely difficult to reconstruct the original full floorplan, even with access to all chunks. Some of the shuffled chunks are shown in supplementary materials. We believe this approach significantly reduces the risk of re-identification or IP leakage.\n\n**References**\n\n[1] Zhiwen Fan, Lingjie Zhu, Honghua Li, Xiaohao Chen, Siyu Zhu, and Ping Tan. Floorplancad: A large-scale cad drawing dataset for panoptic symbol spotting. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10128–10137, 2021.\n\n[2] Zhiwen Fan, Tianlong Chen, Peihao Wang, and Zhangyang Wang. Cadtransformer: Panoptic symbol spotting transformer for cad drawings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10986–10996, 2022.\n\n[3] Wenlong Liu, Tianyu Yang, Yuhan Wang, Qizhi Yu, and Lei Zhang. Symbol as points: Panoptic symbol spotting via point-based representation. arXiv preprint arXiv:2401.10556, 2024."}, {"review_id": "hsN10O54kd", "reviewer": "Reviewer_NcKG", "summary": "This 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.", "questions": "One 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?", "limitations": "The 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.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### 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.", "was_revised": true, "final_justification": "I have carefully read the authors' response and feel that they have addressed most of my concerns. The only remaining issue is that, although adding an image feature extraction module significantly improves performance, the novelty is somewhat limited. However, considering that the authors will release the proposed dataset, which is valuable to the community, I will maintain my original rating.", "rebuttal": "**Q1: About computational speed of the proposed method.**\n\n**A1:** We appreciate the reviewer's suggestion regarding the comparison of computational speed with other SOTA methods. \n\nOur model operates on images with a resolution of 700×700 pixels, which introduces some additional computational cost, but the increase is modest due to the relatively small size. We selected this resolution empirically—higher resolutions did not yield further performance gains, while lower resolutions led to noticeable performance degradation. \n\nWe also found that incorporating image features significantly improves model performance. These features enhance the robustness to noisy primitives and contribute positively to overall panoptic quality, making the moderate computational overhead worthwhile.\n\nWe measured the average inference latency of several representative methods on an NVIDIA A800 GPU, as summarized in the table below. Compared to SymPoint-V2[3], our method increases latency by only 30%, while achieving a 3.0% and 10.1% improvement in panoptic quality on ArchCAD-400K and FloorPlanCAD respectively.\n\n| Method | PQ (FloorPlanCAD) | PQ (ArchCAD-400K) | Average Latency |\n|:------:|:--------:|:--------:|:------:|\n| CADTransformer[1] | 68.9 | 60.0 | 159ms |\n| SymPoint[2] | 83.3 | 47.6 | 66ms |\n| SymPoint-V2[3] | 83.2 | 60.5 | 95ms |\n| DPSS | 86.2 | 70.6 | 121ms |\n\n**Q2: About the impact of loss function weighting.**\n\n**A2:** We conduct some experiments to determine a reasonable configuration of loss function. Specifically, we experimented with different combinations of loss weights $\\lambda_{cls}$ : $\\lambda_{bce}$ : $\\lambda_{dice}$, and empirically identified a reasonable range for balancing these terms. The setting around 1:2:2 yielded stable performance and was adopted as the default in our implementation.\n\n| $\\lambda_{cls}$ | $\\lambda_{bce}$ | $\\lambda_{dice}$ | PQ | RQ | SQ |\n|:------:|:------:|:------:|:------:|:------:|:------:|\n| 1 | 0.5 | 0.5 | 82.4 | 89.5 | 92.1 | \n| 1 | 1 | 1 | 84.7 | 92.7 | 91.4 |\n| 1 | 2 | 2 | 86.2 | 93.0 | 92.6 |\n\n**Q3: About the release of the dataset.**\n\n**A3:** We fully intend to release all major components to the community. \n\nMore specifically:\n\n(1) The dataset will be publicly released on HuggingFace before October 2025. We are currently undergoing final legal and technical checks to ensure proper anonymization and licensing.\n\n(2) In addition to the data, we will also release a toolkit designed for parsing, cleaning, and managing both our dataset and other CAD drawings from diverse sources. We believe this will significantly facilitate downstream research and application.\n\n(3) The code for the proposed DPSS framework will also be made available on GitHub along with the dataset toolkit.\n\n**References**\n\n[1] Zhiwen Fan, Tianlong Chen, Peihao Wang, and Zhangyang Wang. Cadtransformer: Panoptic symbol spotting transformer for cad drawings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10986–10996, 2022.\n\n[2] Wenlong Liu, Tianyu Yang, Yuhan Wang, Qizhi Yu, and Lei Zhang. Symbol as points: Panoptic symbol spotting via point-based representation. arXiv preprint arXiv:2401.10556, 2024.\n\n[3] Wenlong Liu, Tianyu Yang, Qizhi Yu, and Lei Zhang. Sympoint revolutionized: Boosting panoptic symbol spotting with layer feature enhancement. arXiv preprint arXiv:2407.01928, 2024."}, {"review_id": "bv3KbSdsMB", "reviewer": "Reviewer_utxp", "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.", "questions": "Will the authors release the code, model, and dataset?", "limitations": "N/A", "rating": 5, "confidence": 1, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "[Strengths]\n* This paper is easy written.\n* It is an important research problem that is worth exploration.\n* The experimental results are good compared with the existing methods.\n\n[Weaknesses]\n* It is suggested to add the section to introduce the training time, the use of training resource, and inference time.\n* 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?\n* 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.\n* I did not see much weaknesses in this paper and I am not an expert in this domain.", "was_revised": true, "final_justification": "Thanks for addressing my concerns. So my rating is modified accordingly.", "rebuttal": "**Q1: About the release of the dataset and code.**\n\n**A1:** We fully intend to release all major components to the community. \n\nMore specifically:\n\n(1) The dataset will be publicly released on HuggingFace before October 2025. We are currently undergoing final legal and technical checks to ensure proper anonymization and licensing.\n\n(2) In addition to the data, we will also release a toolkit designed for parsing, cleaning, and managing both our dataset and other CAD drawings from diverse sources. We believe this will significantly facilitate downstream research and application.\n\n(3) The code for the proposed DPSS framework will also be made available on GitHub along with the dataset toolkit.\n\n**Q2: About the reporting of training time, computational resources used, and inference time.**\n\n**A2:** Thank you for the valuable suggestion. We agree that reporting computational efficiency is important for a comprehensive understanding of the proposed method.\n\nWe have included a summary of the training time and inference speed in the revised version as follows. Specifically, all experiments were conducted using 8 NVIDIA A800 GPUs. For the ArchCAD-400K dataset, we employed a batch size of 4 per GPU and trained the model for 10 epochs. For the FloorPlanCAD dataset, we used a batch size of 2 per GPU with 50 training epochs. Further details of the training and inference setup can be found in Section 6.1 of the main text.\n\n| Method | Training Time on ArchCAD-400K | Training Time on FloorPlanCAD | Average Inference Time |\n|:-----:|:------:|:------:|:------:|\n| CADTransformer[1] | 24h | 7h | 159ms |\n| SymPoint[2] | 40h | 18h |  66ms |\n| SymPoint-V2[3] | 48h | 13h | 95ms |\n| DPSS | 54h | 16h | 121ms |\n\n**Q3: About the framework’s performance on noisy or real-world CAD environments beyond curated datasets.**\n\n**A3:** We appreciate the reviewer's concern regarding data realism and robustness.\n\nFirst, regarding layer information, during the design of the panoptic symbol spotting task, we explicitly accounted for the possibility of incomplete or inconsistent layer annotations. Accordingly, our algorithmic evaluations were conducted under both with-layer and no-layer settings, as reflected in the \"Additional prior inputs\" column of Tables 2 and 3 in the main paper.\n\nSecond, with respect to noise and imperfections in the drawings, our ArchCAD-400K dataset is collected directly from real-world architectural design archives, and thus naturally reflects the challenges and noise present in practical CAD environments.\n\nIn summary, we have made a deliberate effort to ensure that both our dataset and experimental setup reflect realistic conditions encountered in actual CAD workflows.\n\n**Q4: About reporting the standard deviations and multiple experimental runs.**\n\n**A4:** We acknowledge this limitation. Due to the considerable computational cost associated with training (as noted in Q2), it is difficult to perform multiple full runs for each setting. We plan to include repeated experiments for key configurations in the revised version to further validate the stability of our results.\n\nNevertheless, to partially mitigate this, we used consistent data splits and evaluation protocols across all methods to ensure fair comparison. Besides, the performance gaps between our method and baselines are obvious across both datasets (Table 2, 3, 4), which we believe provides sufficient support for the reliability of our claims.\n\n**References**\n\n[1] Zhiwen Fan, Tianlong Chen, Peihao Wang, and Zhangyang Wang. Cadtransformer: Panoptic symbol spotting transformer for cad drawings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10986–10996, 2022.\n\n[2] Wenlong Liu, Tianyu Yang, Yuhan Wang, Qizhi Yu, and Lei Zhang. Symbol as points: Panoptic symbol spotting via point-based representation. arXiv preprint arXiv:2401.10556, 2024.\n\n[3] Wenlong Liu, Tianyu Yang, Qizhi Yu, and Lei Zhang. Sympoint revolutionized: Boosting panoptic symbol spotting with layer feature enhancement. arXiv preprint arXiv:2407.01928, 2024."}]

Author rebuttal:

Meta-review: The manuscript addresses the task of panoptic symbol detection, as for example needed for technical or architectural drawings. To address this task, the paper introduces ArchCAD-400K, a large-scale dataset of architectural CAD files from the internet, which can be used to train foundation models. Therefore, the paper provides an efficient data collection and annotation pipeline. The dataset has symbol annotations across a variety of semantic classes and instances. The paper further provides a model pre-trained on this dataset and evaluated on panoptic symbol spotting.
While seeing the merits of the dataset and model, the reviewers were initially critical about the paper, mostly because of limited description of the dataset details, collection process and evaluation. However, these concerns could be resolved during the discussion phase, leaving the paper with positive scores after the rebuttal and discussion.
Decision: Accept (poster)