Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings
Authors: Xingguang Wei
Haomin Wang
Shenglong Ye
Ruifeng Luo
Yanting Zhang
Lixin Gu
Jifeng Dai
Yu Qiao
Wenhai Wang
Hongjie Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Panoptic Symbol Spotting
Representation Learning
CAD Drawings
Primary area: applications

Abstract: We study the task of panoptic symbol spotting, which involves identifying both individual instances of countable \textit{things} and the semantic regions of uncountable \textit{stuff} in computer-aided design (CAD) drawings composed of vector graphical primitives.
Existing methods typically rely on image rasterization, graph construction, or point-based representation, but these approaches often suffer from high computational costs, limited generality, and loss of geometric structural information. In this paper, we propose \textit{VecFormer}, a novel method that addresses these challenges through \textit{line-based representation} of primitives. This design preserves the geometric continuity of the original primitive, enabling more accurate shape representation while maintaining a computation-friendly structure, making it well-suited for vector graphic understanding tasks. To further enhance prediction reliability, we introduce a \textit{Branch Fusion Refinement} module that effectively integrates instance and semantic predictions, resolving their inconsistencies for more coherent panoptic outputs. Extensive experiments demonstrate that our method establishes a new state-of-the-art, achieving 91.1 PQ, with Stuff-PQ improved by 9.6 and 21.2 points over the second-best results under settings with and without prior information, respectively—highlighting the strong potential of line-based representation as a foundation for vector graphic understanding.

Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings
Xingguang Weil.2* Haomin Wang
Shenglong Ye²
Ruifeng Luo 9,8 Yanting Zhang' Lixin Gu Jifeng Dai
Yu Qiao² Wenhai Wang2.7 Hongjie Zhang"!
University of Science and Technology of China SShnngaii AI Laboratory
Shanghai Jiao Tong University "Tongji University *Donghua University Tsinghua University "The Chinese University of Hong Kong Areplus East China Architectural Design & Research Institute Co., Ltd.
The code is available at http:/hhhhbbccceeeeeev//o////orr
Abstract
We study the task of panoptic symbol spotting, which involves identifying both individual instances of countable things and the semantic regions of uncountable stuff in computer-aided design (CAD) drawings composed of vector graphical primitives. Existing methods typically rely image rasterization, graph struction, or point-base based representation, but these approaches often suffer from high computational costs, limited generality, and loss of geometric structural in- formation. In this paper, we propose VecFormer, a novel method that addresses these challenges through line-based representation of primitives. This design pre- serves the geometric continuity of the original primitive, enabling more accurate shape representation while maintaining a computation-frient well-suited for vector graphic understanding tasks. To further enhance prediction reliability, we introduce Branch Fusion Refinement module that effectively inte- grates instance and semantic predictions, resolving their inconsistencies for more coherent panoptic outputs. Extensive experiments demonstrate that our method establishes new state-of-the achieving 91.1 PQ, with Stuff-PQ improved by 9.6 and 21.2 points over the second- best results under settings with and without prior information, representation as foundation for vector graphic understanding.
structure, making it
the strong potential of line-based
Introduction
Panoptic symbol spotting refers to the task of detecting and classifying all symbols within a CAD drawing, including both countable object instances (e.g.. windows, doors, furniture) and uncountable stuff regions (e.g., walls, railings) [ 3.
This capability is crucial CAD-based applications. serving as foundation for automated design review and for generating 3D Building Information Models (BIM). However, spotting each symbol, which typically comprises a group of graphical primitives, remains highly challenging due to factors such as occlusion, clutter, appearance variations, and severe class imbalance across different symbol categories.
Earlier approaches to this problem either rasterize CAD drawings and apply image-based detection segmentation methods [ directly construct graph representations of CAD drawings and leverage GNN-based techniques 5 6 7 However, both paradigms incur substantial computational costs, particularly when applied large-scale CAD drawings. To better handle primitive-level
"Equal Contribution
1 Corresponding author: nju.zhanghongjieegmmllll gomal....mmmmmmmmmmmmcmmm
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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data, recent methods treat CAD drawings as sets of points corresponding to graphical primitives and leverage point cloud analysis for symbol spotting. For example, SymPoint represents each primitive as a point with handcrafted features, encoding attributes such as primitive type and length. However, this manually defined representation is restricted to four predefined primitive types (line, arc, circle, and ellipse) and struggles to accommodate the more complex and diverse shapes frequently encountered in real-world CAD drawings. In contrast, the recent CADSpotting [9] forgoes explicit primitive types by densely sampling points along each primitive and representing each point using only its coordinate and color. Although this design eliminates reliance on primitive types, it lacks geometric structure and primitive-level awareness, which may hinder the model's ability to delineate symbol boundaries, resolve overlapping symbols, and capture structural configurations essential for accurate symbol spotting.
In this work, we propose VecFormer, a Transformer-based [0) model built on line-based represen- tation that serves as an expressive and type-agnostic formulation for vector graphical primitives. It employs line sampling to generate a sequence of line segments along each primitive, with each line represented by its intrinsic geometric attributes and associated primitive-level statistics, forming different primitive
compact and informative feature set. Figure Iillustrates a visual comparison representations. SymPoint [ encodes each primitive as a single point, which is too coarse to capture the fine- geeeggaaied structures, especially for long primitives commonly found in stuff regions, leading to degraded performance. To ensure a fair comparison, sampling-bass -basdd methods. As shown in Figure I] unlike CADSpotting [ which suffers from blurred symbol boundaries, our line-based VecFormer yields results with clearer structure and better align- ment to ground-truth, demonstrating higher geometric and structural fidelity. This more compact yet expressive representation is also better suited for Transformer- based architecture, which is sensitive to input sequence length. Further discussion on sequence length across different representations is detailed in Appendix
adopt the same sampling density across
Additionally, inspired by OneFormer3D III, we adopt a dual-branch Transformer decoder to guide the representation learning of vector graphical primitives, leveraging its strong multi-tasking capability to jointly model instance- and semantic-level information. To produce a more coherent panoptic output, we further propose a lightweight, training-fir frreing-free post-processing module, termed Branch Fusion Refinement (BFR), which combines predictions from the instance and semantic branches through manidenccebbsse fusion. This refinement enhances label consistency, mitigates mask fragmentation, and improves the overall coherence of panoptic symbol predictions.
To summarize, our main contributions are:
(1) We introduce VecFormer, novel approach that leverages type-agnostic and expressive line- based representation of vector graphcal primitives, instead of traditional point-based methods, leading to more accurate and efficient panoptic symbol spotting.
(2) We propose a Branch Fusion Refinement (BFR) module that effectively integrates instance and semantic predictions via mafidencce-bssed fusion, resolving their inconsistencies for more coherent panoptic outputs. yielding a performance gain of approximately 2 points in panoptic quality (PQ) on the FloorPlanCAD [ dataset.
(3) We conduct extensive experiments on the FloorPlanCAD [ dataset, where our VecFormer achieves PQ of 1.,, setting new state-of-the-art in the panoptic symbol spotting task. Notably, it improves Stuff-PQ by 9.6 and 21.2 points over the second-best results under settings with and without prior information, respectively, underscoring its superior performance and robustness in real-world CAD applications.
2 Related Work
2.1 Panoptic Image Segmentation
Panoptic segmentation 12 aims unify semantic [4] 15| 16
and instance segmentation [18
19 20 21] by assigning each pixel both a class label and an instance ID, effectively covering both things (countable objects) and stuff (amorphous regions). Early approaches predominantly relied on
CNN-based architectures 22 23 24 25), which, while effective, often required separate branches
for different segmentation tasks. Recent advancements have seen shift towards Transformer-baser models, which offer unified architectures for various segmentation tasks. Notably, Fask2Former [26]

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(a) Ground Truth
(b) SymPoint 8
(c) CADSpotting 9
(d) VecFormer (Ours)
Figure 1: Visualization of primitive representations. Compared to the blurry visual representations of point-based methods (b, c), our line-based approach (d) more closely reflects the ground truth drawing (a). As the data in vector format, please feel free zoom in observe finer differences. Additional comparisons are provided in Appendix A and Appendix B
unifies panoptic, instance, and semantic segmentation using masked attention. SegFormer [17 improves efficiency with hierarchical encoders and lightweight decoders. OneFormer (20 further introduces task-conditioned training jointly handle multiple segmentation tasks. Despite these successes in raster image domains, pixel-centric segmentation models face challenges when applied vector graphics tasks, such Panoptic Symbol Spotting in CAD drawings. Their reliance on dense pixel grids overlooks the inherent structure of vector primitives, making difficult to capture precise geometric relationships, maintain topological consistency, and resolve overlapping symbols. These limitations hinder performance in structured, symbol-rich vector environments.
2.2 Panoptic Symbol Spotting
The panoptic symbol spotting task, first introduced in []]. aims to simultaneously detect and classify architectural symbols (e.g., doors, windows, walls) in floor plan computer-aided design (CAD) drawings. While earlier approaches [2] primarily addressed instances of countable things (e.g., windows, doors, tables), Fan et al. [. inspired by [12), extended the task to include semantic regions of uncountable stuff (e.g., wall, railing). To support this task, they introduced the FloorPlanCAD benchmark and proposed PanCADNet as a baseline, which combines Faster R- R-CNN (2 for de- tecting countable things with Graph Convolutional Networks (28 for segmenting uncountable sfuff. Subsequently, Fan et al. [4] proposed CADTransformer,, utilizing HRNetV2-W49 (29] and Vision Transformers (30 for primitive tokenization and embedding aggregation. Zheng al.
6 adopted graph-based representations with Graph Attention Networks [31] for instance- and semantic-level pre- dictions. Liu er al. (8] introduced SymPoint, exploring point-based representations with handcrafted features, later enhanced by SymPoint- -2 (32) through layer feature encoding and resition-guides training. Recently, CADSpotting 9 densely samples points along primitives generate dense point data for feature extraction and employs Sliding Window Aggregation for efficient panoptic segmentation of large-scale CAD drawings. Although point- based representations are widely adopted in existing state-of-the- art methods 88 32 99, they exhibit notable limitations in complex and densely annotated CAD drawings, including redundant sampling, loss of geometric continuity, and reduced ability to distinguish adjacent or overlapping symbols, shown in Figure
Method
In this section, we first describe how heterogeneous vector graphic primitives are converted into a unified line-based representation. We then present the panoptic symbol spotting framework built upon this representation. Finally, we introduce our post-processing optimization strategy, Branch Fusion Refinement. An overview of the entire pipeline shown in Figure 2
3.1 Line Sampling
Existing point-based representations 9 suffer from limited geometric continuity, structural expressiveness, and generality across diverse primitive types. To address these issues, propose
3

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Sampling
Transformer Backbone
Refinement
Pradichor
Predicion
Presittee
Line Poaling
frietiie Fastux
Layer Feature Enhancement
Prudiction
Semontic Prediction
Hadddo Primitive Features Rayed Vihue
Transformer Decoder
Query Selection
Quaries
Figure 2: Overview of VecFormer. Given a CAD drawing, VecFormer first applies line sampling build a line-based representation of primitives. A Transformer backbone is then used extract line-level features, which are subsequently aggregated into primitive-leve features. Next, these primitive-level features are enhanced by Layer Feature Enhancement module and fed into Transformer decoder for joint instance and semantic prediction. Finally, a Branch Fusion Refinement module integrates both branches to produce the final panoptic symbol spotting result.
Line Sampling, a line-based approximation that encodes primitives as sequences of line segments, enabling unified and seometry-preservinn modeling heterogeneous vector graphics.
Specifically, given a vector primitive with a unique identifier j. we first convert it into vector path (t): [0, 1] IR2. Then, we perform uniform or dynamic path sampling over its parameter interval to generate a sequence of sampled points P, = {P: = 7/ (t,) = I..... K.. J. Here, = t < tz < tk where the number of samples K and the sampling parameters t; can be dynamically adjusted based on geometric features such as the length and curvature of the primitive.
For simplicity, adopt uniform sampling strategy defined as: t; K-1 and use hyperparameter called the sampling ratio to control the number of samples K. Specifically, for line primitives, initially set K = 2: for all other types of primitives, we initially set K = 9. Given a sampling constrain the maximum allowable distance between adjacent sample points to be no greater than asamlle min( width, height), where width and height denote the dimensions of the CAD drawing. If this condition is violated, iteratively increase the number of samples by setting K + K + until the constraint is satisfied.
ratio Oksamlle-
Next, adjacent sampling points are pairwise connected to construct a sequence of line segments:
Lj = (l, = (p".p") p" = P:, = 1i+1, 1 1,..., K 1-1..
which approximates the geometric features of the original primitive.
3.2 Panoptic Symbol Spotting via Line-based Representation
The process of panoptic symbol spotting via the line-based representation consists of three main stages: first, using a backbone to extract line-level features; second, pooling the line-level features into primitive-level features; and third, utilizing 6-layer Transformer decoder to generate instance proposals and semantic predictions.
3.2.1 Backbone
We choose Point Transformer V3 (PTv3) 33
as our backbone for feature extraction due to its excellent performance in handling unordered data with irregular spatial distributions.
Given sampled line I, with its starting point pd (x1, V1) and endpoint pd (xt V2), the primitive ID indicates the primitive to which the line segment belongs, and the layer ID indicates the layer the CAD drawing where the primitive is located. we will now deseribe how convert it into the position vector coord, E 3 and the corresponding feature vector feat, E R (C is the dimensionality of the feature vector) suitable for input the PTv3 backbone.
Normalization. The initial step involves the normalization of the raw line features to a standardized range of 0.5, 0.5].

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For the starting point p* = (11,3/)), 3/1), the normalization is performed as follows:
p = (T 31) 211 Zorigin
3/1 Morigin
0.5,
height 0.5
(2)
width
In this formulation, the coordinates (Forigin Varigin) denote the origin of the coordinate system em- ployed in the CAD drawing. The terms width and height denote the dimensions of the CAD drawing. The normalization for the endpoint p" is achieved through an analogous transformation.
For the normalization of layer ID, let kiiin and kmax represent the minimum and maximum layer ID values observed within the CAD drawing, respectively. The normalized layer ID is then calculated as:
kinin
k
0.5.
Amax
Kmin
Line Position. To simultaneously capture both the position information and the layer information, we use the midpoint of the line (Cz, Cy) for the first two dimensions and the layer ID ls for the third dimension:
+
1/1
1/2
coord, (Iia, M/i 2.) (Cars Cy: id)
2
Line Feature. We set the dimensionality C = 7, and define the line feature feat, RR? as:
feat: = (1, d2 dy Cas Cy Cg. (
(5)
Here, / (s2 x1 2 (y/2 y1 represents the length of the line. The terms d, - (x) - x2)/l and dy (1) y2 denote the unit vectors for displacement in the and directions, respectively. The coordinates (Czy cy) specify the midpoint of the line. These features are chosen because any point on the line can be expressed as: (æ, y) = (cz + tdz, + tdy), E [- parametric representation of the line segment based on its center and unit vector.
2.221. 21. which provides a
Furthermore, (ch, cl)) indicates the geometric centroid the primitive. This centroid is determined by calculating the average of the midpoint coordinates from all lines sampled within the primitive to which the specific line belongs:
[Enee, Cr EnEc, Cy (1,
())=
(6)
3.2.2
Line Pooling
To obtain primitive-level features, we apply Line Pooling, which combines max and average pooling line-level features within each primitive, effectively preserving geometric information and enhancing feature richness.
For each primitive line features f, E RC from 1; Cj aggregated via both max and average pooling, whose results are summed to produce the final primitive feature F,- E RC:
Fj = mma
F
max f. + LEC;
Σ f, I,C,
CC
3.2.3 Layer Feature Enhancement
we adopt Layer Feature Enhancement (LFE) module in our method. Specifically, we aggregate the features primitives within the same layer using average pooling, max pooling, and attention pooling, and fuse the resulting layer-level context back into each primitive feature. This fusion enhances the model's ability to capture intra-layer contextual dependencies and improves the semantic discrimination of similar primitives.
Inspired by SymPoint-V2: V2 32
3.2.4 Query Decoder
Motivated by O 11, we initialize the queries using a Query Selection strategy, which widely adopted in state-of-the-art 2D object detection and instance segmentation methods [34 35 36).

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Subsequently, six-layer Transformer decoder performs self-attention on the queries and cross attention with key-value pairs derived from primitive features. The decoder outputs are then passed to an Instance Branch for generating instance proposals and a Semantic Branch for producing semantic predictions.
Query Selection. With the primitive features C E N derived from the previous stage, where N denotes the number of primitives and C' is the dimensionality of each feature vector, the Query Selection strategy randomly selects proportion Oseeett E [O, 1] of the primitive features to initialize the queries Q R- with M Clyclect representing the number of queries. Following the configuration in OneFormer3D [II]. we set Osseeect 0.5 during training to reduce computational cost, which also serves as a form data augmentation. During inference, we set Qeelect 1.0 in order to preserve the complete information of the CAD drawings.
Instance Branch. In this branch, each query emhedding is mapped to K + dimensional space as class label logits, where K denotes the number of classes and an extra +11 for the background predictions. Simultaneously, we use an einsum operation between the query embedding and the primitive features to generate the instance mask.
Semantic Branch. This branch aims to produce dense, per-primitive semantic predictions. We project the output queries from the decoder into a K + dimensional space as semantic logits. The prediction for each query is assigned to the primitive that was selected to initialize the query during the Query Selection process, thereby providing semantic label each primitive.
3.2.5 Loss Function
To jointly optimize instance and semantic predictions, we adopt a composite loss function:
Lottl Lets + Abce + Adie + Asem Lyem-
(8)
Here, Les is a cross-entropy loss for instance classification, Lice and Luiie 38 are used for instance mask prediction to balance foreground- backoruud accuracy and mask overlap, respectively. Lye denotes the cross-entropy loss for semantic segmentation. The weights ddss Abces Adices Agem control the influence of each term.
3.3 Branch Fusion Refinement
To effectively integrate information from both the Semantic Branch and the Instance Branch, we propose a post-processing strategy named Branch Fusion Refinement (BFR). This method consists of three steps: Overriding. Voting, and Remasking.
Overriding. This step is primarily designed resolve conflicts between instance predictions and semantic predictions at the per- per-primitive level. Given primitive pt. the semantic branch outputs semantic label (eom (pt) {l...., + 1} and corresponding confidence score Ssem ssem(P)) (Pi) [0, 1]. Meanwhile, if is assigned to N instance proposals, each such proposal provides an instance label V f1....... K + 1} and an associated confidence score $ E [0. 1]. where j E NJ indexes the proposals that include ps.
To resolve the conftict, we compare the semantic and instance confidence scores. If the highest instance score for is greater than the semantic score, i.e., max, s (p:) > (pi), then the semantic prediction for p; overridden by the instance label and score of the highest-confidence proposal:
prefined (p:) = 13" see
inst (pt).
relined (p.) s (pt), wherej = arg max JE(I....NN
I (pr).
(9)
If no instance score exceeds the semantic score, the original semantic prediction is retained.
Voting. Given an instance proposal that contains M primitives, its instance label is refined based on the most frequently occurring semantic class among those primitives. Formally, the instance label list for this proposal is refined as:
M (hem (p:) = k))
listt = arg max (I..., K)
(10)
RE
1=1

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where I(() is the indicator function that returns if the condition is true and 0 otherwise. This majority voting strategy ensures that the instance label aligns with the dominant semantic context of its constituent primitives.
Remasking. For each primitive pt, if it belongs instance mask Minst: but its semantic label sem (p:) disagrees with the instance's 's majority-voted label linst. it is removed from the mask:
pi Minst and (p:)) + linst
pi & Minst-
(11)
This operation effectively eliminates label contamination the mask caused by prediction incon- sistencies, thereby improving the purity and semantic consistency of the instance segmentation results.
Experiments
4.1 Dataset and Metries
FloorPlanCAD I dataset consists of 1 11,602 diverse CAD drawings of various floor plans, each annotated with fine-grained semantic and instance labels. We follow the official data split, which includes 6,965 samples for training, 810 for validation, and 3,827 for testing. The annotations cover 30 thing classes and 5 stuff classes.
Following 1 we use the Panoptic Quality (PQ) defined on vector graphics as our main metric to evaluate the performance of panoptic symbol spotting. The Panoptic Quality (PQ) serves comprehensive metric that simultaneously evaluates the recognition correctness and segmentation accuracy of symbol-level predictions vector graphics. graphical primitive is denoted where / is the semantic label, = is the instance index. A symbol is represented by a collection of =i). where J is of primitives. The metric
(4, =).
primitives and is defined is defined as:
{er J|= |= 4,
TP
ELeosyeT JoU(Sps 8g) [TP]
E( (sp: (12)
PQ
TP + F
[TP] + FP + [
Here, Sp (4,, zp)) is the predicted symbol, and sg (lg 2g) ) the ground truth symbol. (TP, |PP) and [FN] represent the number of true positives, false positives, and false negatives, respectively. A predicted symbol is matched to ground truth symbol if and only lp = ly and IoU(sp, 8g) > 0.5. The IoU between two symbols is defined as:
Eeean log L(e,))
IoU(sp: 8z) Eg, Es,us, log ((e,))
(13)
where L(e) denotes the length geometric primitive e.
4.2 Implementation Details
During training, we adopt the AdamW [39 optimizer with weight decay of 0.05. The initial learning rate is set to 0.0001, with warm-up ratio of 0.05, followed by cosine decay applied over 20% of the total training epochs. The model is trained for 500 epochs with batch size of per GPU on NVIDIA 100 GPUs To improve model generalization, we apply several data augmentation strategies during training, including random horizontal and vertical flips with probability of 0.5, random rotations, random scaling within the range [0.8, 1.2]. and random translations up to 10% of the CAD drawing size along both axes. Furthermore, we empirically set the loss weight as Asta Abee Adice Asem = 2.5 : 5.0 5.0 5.0.
4.3 Quantitative Evaluation
Panoptic Symbol Spotting, We compare our method with existing approaches on FloorPlanCAD II for panoptic symbol spotting, as shown Table 1a Our method achieves the highest Panoptic Quality (PQ) across Total, Thing, and Stuff categories, demonstrating superior and more balanced performance. Existing methods tend to perform better on Thing than Stuff categories, revealing an imbalance in recognition. For example, SymPoint [8] scores 84.1 in Thing-PQ but only 48.2 in

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Table Quantitative evaluation results
(a) Panoptic symbol spotting results FloorPlanCAD D dataset. A dash (-) indicates that the methed does not support this setting that the result is reported d in the original paper.
w/o Prior
w/ Prior
Method
PQ
PQu
PQs
PQ
PQth
PQst
PanCADNet
I
59.5
65.6
58.7
CADTransformer
4
68.9
78.5
58.6
GAT-CADNet 6
73.7
SymPoint
8
83.3
84.1
48.2
SymPoint-Ve V2
32
83.2
85.8
49.3
90.1
90.8
80.8
CADSpotting 9
88.9
89.7
80.6
DPSS
40
86.2
88.0
64.7
89.5
90.4
79.7
VecFormer (Ours)
88.4 (+2.2)
90.9 (+2.9)
85.9 (421.2)
91.1 (+1.0)
91.8 (+1.0)
90.4 (49.6)
(b) Primitive-level semantic quality. wFl: length- F1.
Method
GAT-CADNet [6
SymPoint 8
SymPoint-V2 (32
VecFormer (Ours)
F1
85.0
86.8
89.5
93.8 (+4.3)
wF1
82.3
85.5
88.3
92.2 (+3.9)
Stuff-PQ. In contrast, our method achieves more balanced results and shows marked advantage in the Stuff classes, in particular, surpassing the current state-of-the-art method, SymPoint-Y V2 (32], by 9.6 in Stuff-PQQ
To reflect real-world conditions where detailed annotations (such layers) often unavailable, we evaluate current mainstream methods without using prior information. As shown in Table la existing state-of state-of-the-ee methods exhibit strong reliance on prior, particularly for Stuff categories. Specifically, SymPoint- V2 (32) and DPSS [40] suffer significant performance drops in Stuff-PQ when evaluated without prior, decreasing by 31.5 and points, respectively. In contrast, our method VecFormer consistent performance across both settings by using primitive IDs instead of layer IDs as z-coordinate of position vector, i.e., use coord, = (Cz, Cy: j), but not coord, = (Cz, Cy. k) described in subsection 3.1 As shown in Table Ta VecFormer achieves PQ of 88.4 and 90.9 in the Total and Thing categories, outperforming the second- best methods by 2.2 and 2.9, respectively. For the more challenging Stuff category, VecFormer demonstrates particularly strong performance, achieving PQ of 85.9 with a notable gain 21.2 over the second-best result.
These results demonstrate that VecFormer maintains excellent generalization and robustness even without relying on prior information, making more suitable for practical deployment in CAD scenarios.
Primitive-Level Semantic Quality. We assess the model's semantic prediction performance for each graphical primitive by computing the F1 and wF1 score. As summarised in Table 1b our VecFormer consistently surpasses all prior methods, achieving an improvement of 4.3 in FI and 3.9 in wF1, compared to SymPoint-V2 (32). The qualitative results are shown in Figure 3 For more qualitative studies, please refer to Appendix D
4.4 Ablation Studies
Impact of Sampling Strategy, As shown in Table 2a line sampling outperforms point sampling in both settings- with and without prior information. The point sampling variant omits line-specific features (1, dz, d,), leading to inferior results, confirming the superiority of line-based representations for vector graphic understanding.
Choice of Sampling Ratio. As shown in Table 2b reducing the sampling ratio Osample from 0.1 to 0.01 steadily improves performance, with the best PQ (91.1) at O'sample = 0.01-also yielding peak
PQn and PQa scores. Further reduction to Ossmpple 0.005 slightly degrades performance while

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(a) Ground Truth
(b) VecFormer (Ours)
(c) SymPoint-V V2 32
Figure 3: Qualitative comparison of primitive-level semantic quality between VecFormer and V22 Each shows representative example, with (a) Ground Truth annotations, (b) predictions from our VecFormer, and (c) predictions from SymPoint-V2. As shown, VecFormer provides more accurate and consistent semantic predictions across various graphical primitives.
increasing computational cost, making Ossmmple 0.01 the optimal trade-off between accuracy and efficiency.
Table 2: Ablation studies on sampling strategy, sampling ratio, BFR, and prior information.
(a) Ablation studies on sampling strategy
(b) Ablation studies on sampling ratio
Prior
Strategy
PQ
PQth
PQs
Ratio
PQ
PQth
PQ
Point
87.8
89.9
85.4
0.1
90.4
91.1
89.7
w/o
0.05
90.6
91.3
89.7
Line
88.4
90.9
85.9
0.01
91.1
91.8
90.4
Point
89.8
90.0
89.5
0.005
90.4
91.0
89.8
w/
Line
91.1
91.8
90.4
(c) Ablation studies BFR
Ablation studies of prior information
Method
PQ
PQth
PQa
Base
Layer
LFE
PQ
PQn
PQ
w/o BFR
89.2
90.4
88.1
88.4
90.9
85.9
w/ BFR
91.1
91.8
90.4
90.2
91.7
88.4
Gain
(+1.9)
(+1.4)
(+2.3)
7
91.1
91.8
90.4
Effects of the Branch Fusion Refinement Strategy. We conduct controlled experiments to evaluate the effectiveness of the proposed Branch Fusion Refinement (BFR) strategy. As shown in Table 2c incorporating BFR significantly boosts performance across all metrics, demonstrating its essential role in improving prediction accuracy and robustness.
Effects of Prior Information. As shown Table 2d replacing the primitive ID j with the layer ID in the position vector boosts PQ from 88.4 to 90.2, highlighting the value of layer priors. Adding the Layer Feature Enhancement (LFF) module further improves PQ to 91.1, demonstrating that structural priors and LFE together enhance geometric understanding.

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5 Conclusions
We present VecFormer, novel method that employs expressive and type-agnostic line-based representation to enhance feature learning for vector graphical primitives by preserving geometric continuity and structural relationships, which are critical for symbol-rich vector graphics. To unify instance- and semantic-level predictions from a dual-branch Transformer decoder, we propose the Branch Fusion Refinement (BFR) module, which resolves inconsistencies and improves panoptic quality. A current limitation lies in the use of uniform line sampling for simplicity, which may underperform in regions of high geometric complexity. Future work will explore a geometry- -ware dynamic sampling strategy to better adapt to diverse structural patterns in vector graphics. To the best of our knowledge, the proposed method does not pose any identifiable negative societal risks.
Acknowledgements
This work was supported by Shanghai Artificial Intelligence Laboratory, the National Key R&D Program of China (No. 20222D0161301), the Shanghai Committee of Science and Technology (No. 22YF1461500), and the National Natural Science Foundation of China (No. 62206046).
References
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Appendix
A Detailed Visual Comparisons across Different Representations.
We present additional fine-grained visualizations to facilitate a more detailed comparison of different representations. As illustrated in Figure 4 our proposed line-based representation demonstrates closer visual alignment with the ground truth than point-based methods (e.g.. SymPoint [8], CADSpot- ting 1), effectively preserving geometric continuity and structural integrity across variety of primitive types.
(a) Ground Truth
(b) SymPoint g
(c) CADSpotting 9
(d) VecFormer (Ours)
Figure 4: Visualization of how different representations perform on different primitives.
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B Visual Comparison under Varying Sampling Ratios
To further investigate the impact of sampling density representation quality, we conduct a compar- ative analysis across different representations under varying sampling ratios Osumple- As shown in Figure 5] our line-based representation consistently maintains higher geometric fidelity and structural coherence, even under lower sampling densities. In contrast, point- bast- representations tend to suffer from fragmentation and loss of continuity as the sampling ratio decreases.
These visual results highlight the robustness of our approach in preserving essential geometric and topological features, suggesting its suitability for vector graphies tasks where structural integrity is critical under constrained sampling conditions.
(a) Ground Truth
(b) Point: Cisample 0.1
(c) Line: Chample 0.1
(d) Ground Truth
(e) Point: Oisample
0.05
Line: Cksample
0.05
(g) Ground Truth
(h) Point: Oksample 0.01
(i) Line: O'sample 0.01
Figure 5: Visual comparison of the effects of varying sampling ratios on different representations. Since the data is in vector format, zooming in allows for detailed examination of the differences between representations.
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C Sequence Length Analysis of Point- and Line-based Representations
This section analyzes the differences in sequence length between point-based and line-based repre- sentations on FloorPlanCAD [ dataset.
We begin by configuring the line-based representation with sampling ratio of Ossample 0.01, consistent with our experimental setup. For the point-based representation, we set Okaamlee 0.001, which yields similar sampling density that used in CADSpotting (9), although the sampling strategies differ. As illustrated in Figure 6] this setting results in CADSpotting, the point-based method, producing sequences that are approximately 8 times longer than line-based counterpart. Despite the significantly shorter sequence length, our method achieves higher Panoptic Quality (PQ), as demonstrated in the main results (Table lap.
To ensure a fair comparison, we further evaluate both representations under the same sampling ratio of Csample 0.01. Even in this setting, the line-based representation yields approximately 15% fewer tokens than the point-base representation. Moreover, ablation results Table 2a confirm that our approach not only reduces sequence length but also achieves superior performance.
These findings underscore the efficiency and representational strength the line-based approach: by encoding primitives through fewer yet structurally meaningful elements, it preserves geometric fidelity while enhancing learning effectiveness. This compact, structure-aware design leads to more accurate segmentation and improved overall performance, making line-based representation more effective and scalable solution for vector graphic understanding.
Line: ratio=0.01
Point: ratio=0.01
Point: ratio=0.001
6295.46
7.6 x
Train
7410.25
47917.1 1
6364.03
7.6 x
7480.72
48185. 13
6119.93
8.0 x
Test
7152.93
48627.34 34
5,000
10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000
Figure 6: Comparison of sequence lengths between point- and bane- representations FloorPlanCAD I dataset. The vertical axis indicates different dataset splits, while the horizontal axis represents the average sequence length of each representation across these splits.
15

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D Additional Qualitative Evaluation
This section provides additional qualitative results through visualizations. The color scheme for
each category is defined in Figure 7 and further examples are illustrated in Figure Figure 9 and
Figure I0
single door
double door
sliding door
folding door
revolving door
rolling door
Doors
bay window
blind window
opening symbol
Windows
window
Stairs
stairs
Home
washing machine
gas stove
refrigerator
Appliances
sofa
bed
chair
table
TV
Furnitures
wardrobe
cabinet
sink
cabinet
squat toilet
airconditionen
bath
bath tub
urinal
toilet
Equipments
elevator
escalator
Stuff
row chairs
parking spot
wall
curtain
railing
wall
Figure Color map used for category visualization, adapted from
16

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W
W
(a) Ground Truth
(b) VecFormer
(c) SymPoint- V2 32
Figure 8: More qualitative comparison of primitive-level semantic quality between VecFormer and SymPoint-V2 V2 322 Each row shows representative example, with (a) Ground Truth annotations, (b) predictions from our VecFormer, and (c) predictions from SymPoint -2 (32). As shown, VecFormer provides more accurate and consistent semantic predictions across various graphical primitives.
17

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(a) Ground Truth
(b) VecFormer
Figure 9: Results of VecFormer on FloorPlanCAD.
18

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(a) Ground Truth
(b) VecFormer
Figure 10: Results of VecFormer on FloorPlanCAD.
19

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26


Reviews:
[{"review_id": "WK9FQnf4x2", "reviewer": "Reviewer_Ye7H", "summary": "This paper presents VecFormer, a novel method for panoptic symbol spotting in CAD drawings by introducing a line-based representation instead of conventional point-based or rasterized representations. The key idea is to preserve geometric continuity and structural integrity by approximating primitives with sequences of line segments. The authors design a Transformer-based framework that leverages this representation, combined with a dual-branch decoder for instance and semantic prediction and a lightweight Branch Fusion Refinement module to improve label coherence. Extensive experiments on the FloorPlanCAD dataset demonstrate that VecFormer achieves new state-of-the-art results in terms of Panoptic Qualit and Stuff-PQ, with clear improvements over existing methods such as SymPoint-V2 and CADSpotting.", "questions": "(1) On broader generalization: Have the authors considered validating VecFormer on other vector graphic tasks or datasets to strengthen the generalizability claim? Even small-scale tests (e.g., technical drawings, maps) would help.\n(2) Sampling strategy: Could the authors provide empirical evidence comparing uniform sampling with an adaptive or curvature-aware sampling strategy? This is important given the stated limitation.\n(3) End-to-end refinement: Is it possible to integrate the Branch Fusion Refinement module as a learnable layer within the model instead of using it purely as post-processing? A brief discussion or experiment would improve the contribution.\n(4) Reproducibility: Will the authors release the complete training and inference code along with pretrained models? This is essential for verifying the impressive gains.\n(5) Statistical robustness: Could the authors provide standard deviations or error bars for main metrics over multiple seeds to support the robustness of the results?", "limitations": "Yes, the paper discusses a key limitation of uniform line sampling and suggests future directions to address it. However, the impact of this limitation is not empirically shown. I encourage the authors to either visualize failure cases or provide additional experiments to clarify when uniform sampling might fail.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths\n\nOriginality: The proposed line-based representation is conceptually simple yet effective, addressing key limitations of point-based methods.\n\nTechnical Quality: The paper provides solid methodological details, including clear mathematical formulations, ablation studies, and implementation settings. The BFR module is a thoughtful addition to improve panoptic consistency.\n\nSignificance: The improvement on Stuff-PQ is particularly noteworthy, given that this aspect is challenging for prior methods. The approach is well motivated for practical CAD applications.\n\nClarity: The paper is generally clear, with good figures and detailed appendices for qualitative results and additional analyses.\n\nWeaknesses\n\nWhile the line-based representation is well motivated, its potential limitations are only briefly mentioned and not empirically examined.\n\nThe experiments focus solely on the FloorPlanCAD benchmark. The generalizability of VecFormer to other vector graphic domains is claimed but not tested.\n\nThe paper does not provide statistical significance analysis for the reported results.\n\nThe BFR refinement is post-processing but could potentially be integrated more tightly within the model to allow end-to-end training, which is not discussed.", "was_revised": true, "final_justification": "Many thanks to authors for their detailed responses. They raised various good points, although I keep my score the same, as it was on the positive side.", "rebuttal": "Dear Reviewer Ye7H,\n\nWe sincerely thank you for your positive and constructive review. We are encouraged by your assessment of our proposed line-based representation as original and effective, and our method as technically solid. Your insightful comments have been invaluable for improving our manuscript, and we will address each of your questions below.\n\n## Concern 1: On broader generalization\n\nWe thank you for this important question and agree that demonstrating our method's generalizability is crucial.\n\nTo our knowledge, **FloorPlanCAD** was the only publicly available dataset for the Panoptic Symbol Spotting task. To further validate our approach, we actively sought additional data. We identified a recent work, **ArchCAD-400k** [1], which introduced a larger and more complex dataset for this task. Although the full dataset is not yet public, we contacted the authors and were fortunate to obtain a 40k subset. This subset alone is significantly larger and more richly annotated than the entire FloorPlanCAD dataset (which contains approximately 10k samples).\n\nWe evaluated our method on this new, more challenging data and compared it against the existing state-of-the-art method, **SymPoint-V2** [2]. The results are presented below:\n\n|                      |                                |                               |                                |\n| :------------------: | :----------------------------: | :---------------------------: | :----------------------------: |\n|        Method        |               PQ               |       PQ$_{\\text{th}}$        |        PQ$_{\\text{st}}$        |\n|     SymPoint-V2      |              62.4              |             64.9              |              53.7              |\n| **VecFormer (Ours)** | **72.6**${\\scriptsize(+10.2)}$ | **70.1**${\\scriptsize(+5.2)}$ | **79.4**${\\scriptsize(+25.7)}$ |\n\nAs the table shows, our method maintains a significant performance advantage, providing strong evidence for its generalization capabilities. Regarding other vector graphic domains, we have not yet found other publicly available datasets with the necessary panoptic annotations for a direct comparison.\n\nWe will add these new findings and a detailed description of the dataset subset to our revised manuscript. We are also committed to evaluating VecFormer on other relevant datasets as they become available.\n\n## Concern 2: On the sampling strategy\n\nThank you for this insightful question regarding our sampling strategy.\n\nOur choice of uniform sampling was initially motivated by the geometric properties of the FloorPlanCAD dataset. The vast majority of primitives in this benchmark are either straight line segments, which are perfectly represented by our sampling, or circular arcs with smooth, low-frequency curvature. For these primitives, uniform sampling provides a simple yet effective means of generating accurate line-based approximations.\n\nHowever, we concur that for more complex curves with high-frequency variations, such as the intricate Bézier curves found in graphic design, an **adaptive, curvature-aware sampling strategy** would likely yield superior performance. Intuitively, such a strategy would allocate more sample points to regions of high curvature to ensure a faithful approximation, while using fewer points in flatter regions. This would not only improve accuracy but also optimize the sequence length, mitigating the redundancy that can arise when a high uniform sampling rate is applied globally.\n\nWe acknowledge that these are currently intuitive hypotheses that we have not yet validated with empirical experiments. We believe this is a very promising direction for future work and will add a discussion of this limitation, along with the potential of adaptive sampling, to our paper.\n\n## Concern 3: On end-to-end refinement\n\nThis is an excellent suggestion. The primary motivation for designing the Branch Fusion Refinement (BFR) module as a post-processing step was its simplicity, efficiency, and modularity. It is a lightweight module that can be easily integrated to refine the outputs of any two-branch panoptic spotting framework without requiring end-to-end retraining.\n\nIntegrating the BFR module as a learnable layer for end-to-end training is indeed a fascinating prospect. Such a module could potentially learn more complex and powerful data-driven strategies for fusing semantic and instance predictions. However, this direction also presents non-trivial challenges. For instance, in our current architecture, the semantic and instance branches are guided by two distinct supervision signals. An end-to-end approach would require a carefully designed architecture to effectively fuse these disparate signals, which could introduce significant complexity and potential instability to the training pipeline.\n\nWe agree this is a valuable avenue for future research. We will add a discussion to the limitations section of our revised manuscript, elaborating on our current design choice and the potential, as well as the challenges, of an end-to-end BFR module.\n\n## Concern 4: On reproducibility\n\nYes. We are fully committed to open and reproducible research. Upon acceptance, we will release the complete source code for training and inference, all pretrained models, and detailed instructions to reproduce our results.\n\n## Concern 5: On statistical robustness\n\nThank you for raising this important point. As retraining the model multiple times incurs significant computational costs, we instead conducted multiple inference experiments on the test set using different random seeds. The results are as follows:\n\n|                  |                              |                              |                              |\n| :--------------: | :--------------------------: | :--------------------------: | :--------------------------: |\n|      Method      |              PQ              |       PQ$_{\\text{th}}$       |       PQ$_{\\text{st}}$       |\n| VecFormer (Ours) | 91.1${\\scriptsize(\\pm 0.1)}$ | 91.9${\\scriptsize(\\pm 0.1)}$ | 90.3${\\scriptsize(\\pm 0.2)}$ |\n\nOnce again, we thank you for your valuable feedback, which has helped us significantly strengthen our manuscript.\n\n## References\n\n[1] Ruifeng Luo, et al. \"ArchCAD-400k: An Open Large-Scale Architectural CAD Dataset and New Baseline for Panoptic Symbol Spotting.\" _arXiv preprint arXiv:2503.22346_ (2025).\n\n[2] Wenlong Liu, et al. \"Sympoint Revolutionized: Boosting Panoptic Symbol Spotting with Layer Feature Enhancement.\" _arXiv preprint arXiv:2407.01928_ (2024)."}, {"review_id": "eTlloK8WaK", "reviewer": "Reviewer_hNNU", "summary": "The paper targets panoptic symbol spotting task which is a challenging task in the domain of vector drawing analysis aiming to predict semantic and instant labels for countable and amorphous symbols in the input vector drawing. To address this task, the authors propose to enrich the input representation available to the deep learning approaches by approximating all input primitives by sequences of straight lines. This enables a more complete input representation that pairs well with the modern deep learning architectures such as transformers. The authors then use this input representation to feed into a point transformer model and refined using a conventional refinement strategy to consolidate semantic and instance predictions and confidence scores. A thorough evaluation is performed on the FloorPlanCAD dataset that shows how the proposed approach compares both regarding the baselines and regarding various design choices.", "questions": "The only concern I have right now regarding the experimental evaluation is regarding the use of the BFR strategy used to refine results produced by the raw method. I think that the most compelling way to rebut that would be to conduct an experiment with one of the baseline methods and basically refine its results also using the BFR strategy. I do not know if this is an easy task or if it makes total sense to executive this comparison and exactly this way.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The effectiveness of the proposed input representation is clear and compelling. I also appreciate that the authors provides the districts on the number of samples required to represent and given input drawing with various representations and how their method is more economical compared to the baseline.\n2. The effectiveness of the series of design stages in the proposed approach is demonstrated in the compelling way. \n3. The experimental validation is reasonable compelling and shows the advantages of the proposed approach quite clearly. \n4. The paper is sufficiently verbose with respect to the design of the method the experiments conducted and the outcomes. I believe that all the necessary information to reproduce the findings of this paper is presented.\n\nWeaknesses:\n1. The only major weakness that I see at this point is the performance drop from not including the Branch Fusion Refinement (BFR) strategy. This begs the question whether including branch fusion refinement in the baseline approaches would also improve their performance?", "was_revised": true, "final_justification": "I have read the reviews and the authors' replies to them. I'm inclined to keep my original ratings, and I hope those were encouraging to the authors. \n\nI do not know if the other reviewers shall see this note, but it seems to me that the authors had in mind a practical, concrete task, for which they developed and studied a concrete solution. My experience with vectorization tasks lays exactly within the architectural/mechanical drawings domains, and an accurate symbol segmentation would definitely not harm the final result for those kinds of tasks. I would imaging that a solid segmentation would be quite useful for retrieval, too. \n\nSo while I can agree that the authors might have sticked to a bit safer plan in terms of technical methodology (obtaining perhaps less \"exciting\" results), I still believe their work constitutes visible progress on a useful image processing problem. Thus I'm in favor of this paper being accepted.", "rebuttal": "Dear Reviewer hNNU,\n\nThank you for your positive and insightful feedback on our work. We are very encouraged by your appreciation for our proposed line-based representation, the rigor of our experimental validation, and the overall clarity of our paper. We are especially grateful for your constructive suggestion regarding our Branch Fusion Refinement (BFR) strategy.\n\n## Regarding your question on the Branch Fusion Refinement (BFR) strategy:\n\nYour question regarding the generalizability of our BFR strategy and whether it would confer similar performance enhancements to baseline methods is an excellent one. We appreciate this critical question, as it prompted us to conduct further experiments to address this matter directly.\n\nYou correctly observed that the performance degradation without BFR highlights its importance within our framework. However, the BFR module, as its name (Branch Fusion Refinement) suggests, is intrinsically designed to fuse predictions from a multi-branch decoder architecture. To the best of our knowledge, most existing baseline methods for this task employ a single-branch decoder, which renders them architecturally incompatible with our BFR module.\n\nTo empirically validate this and directly address your concern, we identified a baseline with a compatible multi-branch structure, **CADSpotting** [1]. We then integrated our BFR strategy into the CADSpotting model to evaluate its impact. The results are presented below:\n\n|                   |                           |                           |                           |\n| :---------------: | :-----------------------: | :-----------------------: | :-----------------------: |\n|      Method       |            PQ             |     PQ$_{\\text{th}}$      |     PQ$_{\\text{st}}$      |\n|    CADSpotting    |           88.9            |           88.7            |           81.8            |\n| CADSpotting + BFR | 89.9${\\scriptsize(+1.0)}$ | 89.8${\\scriptsize(+1.1)}$ | 84.7${\\scriptsize(+2.9)}$ |\n|     **VecFormer(Ours)**      |         **91.1**          |         **91.8**          |         **90.4**          |\n\nAs the results indicate, applying our BFR strategy to CADSpotting yields a consistent and notable performance improvement across all metrics. This outcome confirms your intuition and demonstrates that our BFR module is indeed a generalizable component that can benefit other multi-branch architectures.\n\nCrucially, however, even with the BFR enhancement, the performance of CADSpotting remains significantly below that of our proposed method. We believe this comparison strongly substantiates our central claim: the superior performance of our method is primarily driven by our **novel line-based representation**. This distinction is crucial, as it confirms that our core contribution, the representation itself, is the principal driver of our results, not just the refinement module. We believe this new experiment robustly addresses your concern and further validates the primary contributions of our paper.\n\nThis line of inquiry, prompted by your valuable suggestion, has also inspired a new direction for our future work: exploring the application of BFR to other domains that utilize multi-branch models, such as image segmentation. This will allow us to further test and hopefully confirm the broader utility of the BFR strategy.\n\nThank you once again for your time and your constructive review.\n\n## References\n\n[1] Jiazuo Mu, et al. \"CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings.\" _arXiv preprint arXiv:2412.07377_ (2024)."}, {"review_id": "2zVOwGY3ht", "reviewer": "Reviewer_BWhE", "summary": "The paper tackles the problem of classification and counting of vector graphic CAD drawings to classify and enumerate (or group) the curve primitives within. The work uses a transformer architecture as well as other enchancements, e.g., LFE and BFR, to achieve state-of-the-art performance on the FloorPlanCAD dataset. The contribution is primarily just this: the new architecture and improved performance for this specific task. The tools used for the method, e.g., line-based features and the transformer architecture seem to be relatively natural generalizations and applications of existing methods.", "questions": "These are mostly clarity questions, that I think could be easily addressed with some small bits of text.\n\n1. Line 128: Please clarify the types of primitives. Is a \"line\" primitive necessarily a straight line segment, or could it be a Bezier curve? Later on \"line\" is used for line segments sampled from primitives, so I wasn't sure. Are there other types of primitives, e.g., fill regions or polygons?\n2. Line 141: Where is PTv3 used? The section outlines features that are explicitly given by formulae and then pooling. Are these then fed into PTv3? This seems to conflict with the diagram in Figure 2, where the transformer backbone precedes the pooling of line features.\n3. Line 155: What are the layer IDs? Does each one relate to the semantic label? Why does it need to be normalized?\n4. Eq (5): Is there a reason you explicitly have the first 5 entries instead of just putting the 2 endpoints of the line segment? They could be derived from just these 2, and perhaps it would lead to a more compact model (or not, who knows). Maybe an ablation would be interesting.\n5. Section 3.2.3: Could this section be expanded to be a bit more explicit, at the cost of compressing 3.2.1? The formulae in 3.2.1 are relatively obvious and could be relegated to an appendix, in my opinion.", "limitations": "Yes.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The paper provides state-of-the-art performance on a relevant dataset.\n2. A novel architecture and set of features for the specific problem are used.\n3. Competing methods have been published at ICCV and other top-tier conferences, so while the problem seems a bit niche, it has warranted publication in such venues.\n\nWeaknesses:\n1. It will be hard to reproduce the method without a link to code or a repo. \n2. I found the presentation of the problem at hand to be unclear. It took me until the Experimental Results section to understand what semantic and instance labels would be. There are also other clarity issues with respect to basic definitions for readers unfamiliar with the Panoptic Symbol Spotting problem. See \"Questions\" below.\n3. I am not an expert in deep learning architectures and their application in this problem domain, but it's unclear to me if insights in this work will extend to other problems.", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer BWhE,\n\nWe are very grateful for your detailed and insightful review. Your constructive comments have been instrumental in helping us enhance the clarity and impact of our work. We are pleased that you recognized our method's state-of-the-art performance and the significance of the research problem.\n\nBelow, we address each of your concerns.\n\n## Concern 1: On ensuring the reproducibility of our work.\n\nWe agree that reproducibility is crucial for scientific advancement. To ensure our results can be fully and easily reproduced, we commit to making our complete project public upon the paper's acceptance. This release will include the full source code for both training and inference, along with the final model weights.\n\n## Concern 2: On the clarity of the problem definition and key terminology.\n\nThank you for highlighting this critical point. We agree that a clear, upfront definition is essential for the paper's accessibility. We will significantly revise the Introduction to explicitly define the **Panoptic Symbol Spotting** task, its objectives, and all key terminology at the very beginning. The revised section will ensure that readers not deeply familiar with this specific field can grasp the core challenges and our contributions without needing to read ahead.\n\n## Concern 3: On the generalizability of our method beyond architectural CAD drawings.\n\nWe appreciate you raising this important point. While our method is demonstrated on architectural CAD drawings, we believe the core architectural concepts are applicable to other domains that utilize **structured vector graphics**. The central idea of using a line-based representation to approximate vector graphics is fundamentally not limited to architectural drawings. This approach could be extended to other technical illustration domains, such as recognizing components in electrical schematics or mechanical engineering diagrams, where symbols are defined by arrangements of geometric primitives. We will add a brief discussion on these potential future directions to the Conclusion section.\n\n## Concern 4: On clarifications regarding methodology and paper structure.\n\nThank you for the detailed questions, which help us identify areas for improvement. We address them below in order:\n\n1. **Line 128 (Clarification of \"Line\" Primitives):** Thank you for pointing out this ambiguity. In this paper, we consistently use the term '**line**' as a shorthand for a '**straight line segment**'. A 'line' primitive can serve as its own sampled representation (a single line segment) without any loss of geometric fidelity. In contrast, other primitives, such as curves, must be discretized into multiple segments to approximate their geometry. This fundamental distinction warrants the application of a different initial sampling number (K) for 'line' primitives compared to other types.\n    \n2. **Line 141 (Use of PTv3):** The **Transformer Backbone** in our framework is indeed **PTv3** [1]. The data pipeline is as follows: 1) The **Line Sampling** module generates a set of line segments. 2) These segments' endpoint coordinates are encoded into position and feature vectors (as detailed in **Section 3.2.1**). 3) These vectors are fed directly into **PTv3**, which processes them to produce contextualized features. 4) The **Line Pooling** module operates on these output features. We used the general term _Transformer Backbone_ in Figure 2 to highlight the modularity of our framework; while we selected PTv3 for our implementation, it can be substituted with other similar backbone architectures.\n    \n3. **Line 155 (Layer IDs):** In CAD drawings, layers are used to group related entities. For example, a layer named \"doors\" might contain various types of doors (single, double, sliding). By including the layer ID, we provide the model with this crucial semantic grouping information. We **normalize** this information to ensure that all dimensions of the input position vector are on a uniform scale (i.e., within the ($[-0.5, 0.5]$) range). This prevents any single feature from dominating the distance calculations and improves the numerical stability of the model during training. We will expand this explanation in the paper.\n    \n4. **Eq (5) (Feature Formulation):** This is an excellent observation that touches upon a key aspect of our model's design. While these features are geometrically derivable, we found empirically that providing them explicitly leads to a significant performance improvement. Actually, our earliest models, which used only endpoint coordinates, underperformed compared to the current feature set. Our hypothesis is that while these higher-level geometric features are derivable from the endpoints, it is non-trivial for the model to learn these relationships implicitly. By providing higher-level features explicitly, we allow the model to more effectively leverage the key advantage of a line-based representation: the rich geometric information that is inherently lost in point-based methods. Below are the results of the ablation study:\n\n|              |             |             |             |\n| :----------: | :---------: | :---------: | :---------: |\n|Features|PQ|PQ$_{\\text{th}}$|PQ$_{\\text{st}}$|\n| 2 endpoints  |    89.2     |    90.7     |    87.7     |\n| **Higher-level** | **91.1**${\\scriptsize(+1.9)}$ | **91.8**${\\scriptsize(+1.1)}$ | **90.4**${\\scriptsize(+2.7)}$ |\n\n5. **Section 3.2.3 vs. 3.2.1:** Thank you for this constructive suggestion. We agree that it would improve the paper's flow and clarity. In the revised manuscript, we will condense **Section 3.2.1** by moving the detailed formulas to an appendix. We will then use the freed space to provide a more intuitive and thorough explanation of the LFE module's functionality in **Section 3.2.3**.\n\nWe are confident that these revisions will significantly improve the quality and clarity of our paper. Thank you once again for your time and insightful feedback.\n\n## References\n\n[1] Xiaoyang Wu, et al. \"Point Transformer V3: Simpler, Faster, Stronger.\" _arXiv preprint arXiv:2312.10035_ (2023)."}, {"review_id": "Zr3OoFK3r4", "reviewer": "Reviewer_g5pP", "summary": "The paper is about panoptic symbol spotting, which can be seen as instance/panoptic segmentation on vector graphics. The paper proposed a line-based representation by discretizing vector graphics into a set of line segments. Then an instant/panoptic segmentation method is applied.", "questions": "None", "limitations": "The claim \"but these approaches often suffer from high computational costs, limited generality, and loss of geometric structural information\" in the abstract is not well-supported. If they are in the abstract which means they are important, they should be emphasized somewhere.", "rating": 2, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Both the writing and figures are clear to illustrate the core idea. However, I have the following concerns:\n\n1. The method looks like a combination of vector graphics discretization and panoptic segmentation. For the discretization, the authors claim a novel line representation is proposed. However, to me, this is still point based which is similar to prior work. It is just people might be using different pooling methods for the handcrafted features (despite Fig 4 in the appendix).\n\n2. To prove the effectiveness of the proposed representation, I believe the authors need a better experimental setup. For example, the same network backbone (similar number of parameters), the same panoptic segmentation method, ... so that we can ablate the proposed representation does work.\n\n3. I do not know this field very well. However, I checked the competitor methods shown in this paper. They all followed a similar way (see 1). I do not believe the paper has enough novelty. The paper lacks conceptual depth and is quite incremental compared to other similar papers.\n\n4. The large-scale CAD dataset proposed by CADSpotting is missing in the paper. It might be important to test the scaling and generalization ability of the method.", "was_revised": true, "final_justification": "Thanks for the response. It solved some of my concerns. Since I am not an expert in this area, I would like to listen other reviewers' opinions.", "rebuttal": "Dear Reviewer g5pP,\n\nWe sincerely thank you for your time and for providing a thorough and constructive review of our manuscript. We have carefully considered all your comments and will revise our manuscript accordingly.\n\nBelow, we address each of your concerns point-by-point.\n\n## Concern 1: On the novelty of the line-based representation and the support for claims in the abstract.\n\nA core limitation of point-based representations is that they simplify complex vector graphics into discrete points, retaining only intrinsic point attributes such as coordinates. This not only leads to the loss of geometric structure but also requires denser sampling and greater computational overhead to compensate for the missing contextual information.\n\nOur line-based representation adopts a more expressive and structured sampling strategy by sparsely sampling line segments as geometric primitives, instead of using dense point clouds.\n\nThis approach enables the explicit encoding of a richer set of intrinsic geometric features (such as length, orientation, and local connectivity, etc) that are inherently lost in point-based sampling. This distinction is not merely an alternative sampling strategy but a shift in the representational paradigm, which we argue is crucial for effectively understanding vector graphics.\n\nTo demonstrate its novelty, we will now describe how our line-based representation addresses the three challenges mentioned in the abstract:\n\n- **Addressing** _**Loss of Geometric Structural Information**_**:** Point-based representations reduce primitives to disconnected coordinates, thereby discarding the topological and geometric relationships between them. This approximation inherently leads to a substantial loss of structural information. In contrast, our line-based method explicitly encodes a richer set of intrinsic geometric features (such as length, orientation, and local connectivity, etc) by treating lines as fundamental primitives. Qualitatively, as illustrated in **Figure 1**, our line-based representation visually approximates the original image more closely. Furthermore, as demonstrated in **Appendix B**, our method preserves high visual fidelity even at reduced sampling densities. Quantitatively, the results presented in **Table 2(b) of Section 4.4** show that our method maintains high performance despite a significant reduction in sampling density. These findings collectively demonstrate that the line-based representation more effectively preserves critical geometric information.\n    \n- **Addressing** _**High Computational Costs**_**:** For Transformer-based models, the computational cost is proportional to the square of the input sequence length ($O(n^2)$). In **Appendix C**, we analyze the sequence lengths of both representations and find that, at the same sampling density, the sequence length of the line-based representation is approximately 15% shorter than that of the point-based representation. More importantly, as shown in **Appendix B** and **Table 2(b) of Section 4.4**, our line-based representation demonstrates strong robustness to a reduction in sampling density. Even when the sampling density is reduced tenfold, its performance (90.4 PQ) still slightly surpasses the previous state-of-the-art result (90.1 PQ by SymPoint-V2 [1]). This implies that we can achieve or even exceed existing performance with a much shorter sequence length, thereby significantly reducing computational costs. While direct runtime comparisons were outside the scope of this study, this sequence length analysis provides compelling evidence that our method offers a path to significantly lower computational overhead within the same network architecture.\n    \n- **Addressing** _**Limited Generality**_**:** Some existing methods, such as SymPoint [2], rely on manually designed sampling and feature engineering rules for different types of primitives. For instance, in the FloorPlanCAD [3] dataset, they designed specific sampling methods for only four primitive types (lines, arcs, circles, and ellipses). This approach lacks generality, as it requires new manual rules when encountering new primitive types (e.g., Bézier curves, complex polylines). In contrast, our method treats all primitives uniformly as vector paths and applies a unified sampling strategy to convert them into a set of line segments. This universal framework allows our method to naturally generalize to vector graphics containing more diverse and complex primitives without any manual modifications. As discussed in **Lines 271-284**, our approach demonstrates exceptional performance even in the absence of priors, which is a critical advantage for real-world applications where such information is often unavailable. Our evaluation shows that existing state-of-the-art methods exhibit a strong reliance on priors, with performance on _Stuff_ categories dropping significantly (e.g., by 31.5 PQ for SymPoint-V2) when priors are removed. In contrast, our method maintains robust performance, achieving a remarkable 21.2 PQ gain over the second-best result in the challenging _Stuff_ category. This highlights the superior practical generality and robustness of our framework, making it more suitable for deployment in real-world scenarios.\n\nIn summary, our method is not merely a new representation but an effective solution targeting the core deficiencies of existing approaches. In the revised manuscript, we will restructure the Introduction and Related Work sections to more clearly introduce these arguments and explicitly point to the supporting evidence within the paper (including the appendices) to clarify the motivation and originality of our work.\n\n## Concern 2: The need for a better experimental setup for ablation.\n\nWe thank you for this valuable suggestion. In fact, we had already conducted such an experiment in our original manuscript (**Line 291-294** and **Table 2(a) of Section 4.4**).\n\nWe performed a direct comparative ablation study between the line-based and point-based representations.\n\n- We employed the **exact same network architecture and hyperparameter settings** to process both the point-based and line-based representations.\n    \n- For the point-based baseline, we simply **removed the higher-order geometric features unique to lines** (e.g., length and orientation) from the input feature vector, leaving only coordinate information. All other aspects of the model remained identical to our full model.\n    \n\nThe results of this experiment are presented in the original manuscript in **Table 2(a) of Section 4.4** (discussed in **Line 291-294**). The table clearly shows that our line-based representation significantly outperforms the point-based one, both with and without the use of prior information. This provides strong evidence that our proposed representation captures geometric information more effectively, thereby boosting model performance. We will emphasize the setup and conclusion of this experiment more prominently in the revised version to make it more explicit.\n\n## Concern 3: Missing evaluation on the large-scale CAD dataset.\n\nWe thank you for this suggestion. We also believe that evaluating our method on a large-scale dataset like LS-CAD is important for demonstrating its scalability and generalization. However, to the best of our knowledge, the LS-CAD dataset mentioned in the CADSpotting [4] paper has not yet been publicly released. Therefore, we are currently unable to conduct experiments on it.\n\nShould the LS-CAD dataset become publicly available, we are committed to evaluating our method on it and will report the analysis in future work. We will add a note to this effect in the conclusion or future work section of our revised manuscript.\n\n## References\n\n[1] Wenlong Liu, et al. \"Sympoint Revolutionized: Boosting Panoptic Symbol Spotting with Layer Feature Enhancement.\" _arXiv preprint arXiv:2407.01928_ (2024).\n\n[2] Wenlong Liu, et al. \"Symbol as Points: Panoptic Symbol Spotting via Point-based Representation.\" _arXiv preprint arXiv:2401.10556_ (2024).\n\n[3] Zhiwen Fan, et al. \"FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting.\" _arXiv preprint arXiv:2105.07147_ (2021).\n\n[4] Jiazuo Mu, et al. \"CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings.\" _arXiv preprint arXiv:2412.07377_ (2024)."}]

Author rebuttal:

Meta-review: This paper proposes VecFormer, introducing a line-based representation for panoptic symbol spotting in CAD drawings, combined with a dual-branch architecture and a Branch Fusion Refinement module. The method is well motivated, addressing limitations of point-based and rasterized approaches by preserving geometric continuity and enabling more efficient representations. Experiments show strong improvements, particularly in Stuff-PQ, establishing new state-of-the-art performance on FloorPlanCAD and demonstrating generalization to additional data. While one reviewer raised concerns about novelty, they explicitly noted a lack of expertise in this area, and the other reviews—both confident and positive—found the contribution original, technically solid, and practically relevant. The area chair concurs with the positive assessments and views the method as clear progress on an important and established research problem. I recommend acceptance.
Decision: Accept (poster)