RANK++LETR: Learn to Rank and Optimize Candidates for Line Segment Detection
Authors: Xin Tong
Baojie Tian
Yufei Guo
Zhe Ma
Venue: NeurIPS 2025 poster ()
Keywords: line segment detection
RANK++LETR
Transformer
wireframe parsing
Primary area: applications

Abstract: It is observed that the confidence score may fail to reflect the predicting quality accurately in previous proposal-based line segment detection methods, since the scores and the line locations are predicted simultaneously. We find that the line segment detection performance can be further improved by learning-based line candidate ranking and optimizing strategy. To this end, we build a novel end-to-end line detecting model named RANK++LETR upon deformable DETR architecture, where the encoder is used to select the line candidates while the decoder is applied to rank and optimize these candidates. We design line-aware deformable attention (LADA) module in which attention positions are distributed in a long narrow area and can align well with the elongated geometry of line segments. Moreover, we innovatively apply ranking-based supervision in line segment detection task with the design of contiguous labels according to the detection quality. Experimental results demonstrate that our method outperforms previous SOTA methods in prediction accuracy and gets faster inferring speed than other Transformer-based methods.

RANK++LETR: Learn to Rank and Optimize
Candidates for Line Segment Detection
Xin Tong
Baojie Tian
Yufei Guo
Zhe Ma'
Intelligent Science & Technology Academy of CASIC xin sin_tonge @ pku.edu.en, mazhe Sichheetth@@e__ 163.com
Abstract
It is observed that the confidence score may fail to reflect the predicting quality accurately previous proposal-basee line segment detection methods, since the scores and the line locations are predicted simultancously. We find that the line segment detection performance can be further improved by learning-batt -baedd line candidate ranking and optimizing strategy. To this end, we build a novel end-to-end line detecting model named RANK++LETR upon deformable DETR architecture, where the encoder is used to select the line candidates while the decoder is applied rank and optimize these candidates. We design line-aware deformable attention (LADA) module in which attention positions are distributed in long narrow area and can align well with elongated geometry of line segments. Moreover, we novatively apply ranking-based supervision in line segment detection task with the design of contiguous labels according to the detection quality. Experimental results demonstrate that our method outperforms previous SOTA methods in prediction accuracy and gets faster inferring speed than other Transformer-bar based methods.
Introduction
Line segments and junctions are crucial information in structured scenes and are ubiquitous in human- made environments. An accurate line segment detection algorithm can significantly enhance various
computer vision applications, such as 3D reconstruction [36] 2 camera calibration [22] 27), depth
estimation 40] scene understanding [TT. object detection
29 SLAM [88] 41 etc. Traditional
geometric-based line segment detection algorithms usually extract low-level image features and group them into line segments. These methods often run at fast speed, while may suffer from fragmented prediction. Learning-base methods achieve promising results by learning knowledge from image sets with supervision, which are able to detect longer and more meaningful line segments.
Proposal-based methods constitute a pivotal component within learning- approaches and have been extensively studied recently. These models typically output target predictions such as endpoint coordinates or midpoint coordinates with endpoint offsets for line segments. Generally, these methods first simultaneously predict both the positional coordinates and confidence scores of candidate line segments, then select the top-ranked proposal -ased on confidence scores as final predictions. Previous study [31] points out that some accurately detected line segments are assigned low confidence scores during prediction since confidence prediction and location regression of line segments are independent. Specifically, given the simultaneously predicted line candidates with confidence scores and positions, the detection performance can be significantly improved even if only proper scores are assigned. Based on the observation, we find that the line segment detection performance can be further improved by learning-based line candidate ranking and optimizing strategy.
Transformers depend attention modules gather relevant features. However, in the classic deformable attention module, the attention position of a query is usually around a reference point on
"Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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the feature map, which is not easy to adapt to the long and narrow area for detecting line segments. Thus, we specially design a novel attention module named line-aware deformable attention (LADA), which can align well with the elongated geometry of line segments for better perceiving line features.
To effectively train model for quality-award ranking of line candidates, proper supervision is essential. Ranking- based losses aim to rank the positive predictions above negative ones and sort the high-score candidates over low-score ones, which naturally suit our line ranking task. We define the contiguous label according to the quality of the predictions, which are based on the distance of the nearest ground truth and the predictions. Then, ranking- based losses can be applied promote higher scores for high-qualit, predictions.
In this work, we propose novel end-to-end line detecting model named RANK++LETH upon deformable DETR architecture. For Transformer-basse line detection methods, LETR uses DETR architecture where the backbone and the encoder are used to extract features and the decoder is used to generate line segments. RANK-LETK adopts an encoder-only network and directly predicts the lines from the encoder. Different from the above approaches. our method leverages the complete network architecture of deformable DETR in design philosophy. Specifically, we apply distinct supervision during the encoder and decoder stages: the encoder is guided to predict candidate line segments, while the decoder is responsible for ranking and refining these candidate line segments.
Our contributions can be summarized as follows: (1) We present novel DETR- like line segment detection framework, where the encoder is used to generate candidate line segment proposals, while the decoder is used to optimize their confidence scores and locations.
(2) We propose line-aware deformable attention where the perception field can be long and narrow to catch features along candidate line segments. (3) By defining the continuous label for line segment detection, employ laaking-based supervision to optimize confidence scores in the decoder. (4) Experimental results demonstrate that our method outperforms previous SOTA approaches in prediction accuracy while running faster than previous Transformer-based models.
2 Related Work
2.1 Line Segment Detection.
Traditional line detection methods often rely on grouping image gradient [32 I 21 and pre-defined rules 7 34]. Recently, ceuring-baseed methods have achieved promising results. For junetion predicts junction map and edge map in two branches before merging them. PPGNet 44 uses point-pair graph describe junctions and line segments. L-CNN [45] applies line proposal and Lol pooling to propose candidate lines and verify them. Methods with dense prediction first predict representation map and extract line segments with post-processing. AFM 335 proposes attraction field maps to represent the image space and uses a squeeze module to generate line segment maps. HAWP [88 builds hybrid model considering 4D attraction field and further extends to holistic attraction field 37 Lin et al. (17] apply deep Hough transform to introduces tri- tri-points line segment representation for end-to-end detection. M-LSD 8 presents SoL augmentation and designs an extremely efficient architecture for fast detection. SOLD2 (26 and DeepLSD 25 apply unsupervised pipelines and are able to detect fine and sufficient line segments. Transformer-based method can directly output the locations of the line segments. LETR B3 models it as object detection and predicts line segments with DETR architecture. RANK-LETR [31 applies match predicting and re-ranking to improve the training efficiency and the recall of high quality predictions. In this work, we extend Transformer and proposal-based method with pure learnable optimization module for better performance.
based methods, DWP 13]
the previous detection architectures. TP-LSD
2.2 Visual Transformer for Detection
Visual Transformer for object detection task is originated in DETR 33, in which a Transformer- bbsse encoder-decoder framework is adopted and end-to-ent supervision is applied with bipartite matching. Zhu et al. [46] further proposes deformable DETR in which each query only focuses on small set of keys with learnable locations. A denoising training method is presented in [16] and query formulation using dynamic anchor boxes is introduced in [20] to speed up training convergence of
DETRs, which are further extended in DINO 42) Hou el al. [12] designed a hierarchical query
filtering strategy to reduce the computational redundancy of DETR. Visual Transformer is widely

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Input Image
Ground Truth
content token
Decoder Supervision
pesitional token
Encodlr Suparvision
backbone
Transformer
Feature Extractor
Lime
Top Selection
Line Optimizer
Selection
2
Predicted Line Segments
Selected Position
Final
Initial
Positions Scores
Multi-level Features
Embeddings
Positions Scores
I Learnable Contents
Figure 1: Overview of the proposed RANK+ An image is first fed into CNN-based feature extractor and multi-level feature maps are obtained from different layers. Then, initial line segments generated by Transformer encoder based line segment generator with the multi-level features. Candidate line segments with high confidence scores are selected to initialize the reference points in the proposed line-aware deformable attention of the line optimizer. Finally, the confidence scores and positions of the candidate line segments are optimized, and we choose the candidate line segments according to their optimized confidence scores with maximum suppression as our final prediction. The entire method can be end-to-end training and inferring.
used in many other visual detection tasks. Xu et al. [33 apply DETR in line segment detection with multi-scale encoder-decoder strategy. Tong al. 30 vanishing point detection with Gaussian hemisphere division. Chenhang et al. [10] apply Transformer
Transformer decoders in end-to-end
in 3D object detection with set-to-set translation strategy. Tan et al. (28 utilize Transformers to represent context features and line segments for detecting and reconstructing 3D planes from single image. Liu al. [19] employed Transformer- based architecture for end-to-end lane detection. Leveraging the encoder-decoder architecture of deformable DETR, we design to generate line segment proposals and optimize line candidates successively in single framework.
2.3 Ranking-based Losses
Ranking-basee losses have received much attention in recent studies. Chen et al. S first propose Average Precision Loss to address the imbalance of brreground-backgrounn
classification problem by framing object detection as ranking task. Rank & Sort (RS) Loss that defines a ranking objective between positives and negatives well sorting objective prioritize positives with respect to their continuous IoUs is designed in [24]. Yavuz et al. [39] apply Bucketed Ranking- based (BR) Losses which group negative predictions into several buckets. Cetinkaya al. [4] extend ranking-based Loss to edge detection with uncertainty modeling. Ranking-basse loss is also used in 3D reasoning frameworks [15]. In this work, naturally employ ranking-base based losses supervise the line optimizer for better confidence prediction.
3 Method
3.1
Line Segment Detection Modeling
Building upon deformable DETR encoder-decoder architecture, model line segment detection as an end-to-end process including line proposal generation and optimization, with each line proposal parameterized by the position of its endpoints and the confidence score. Specifically, the encoder generates candidate line segment proposals with associated confidence scores and positions, while the decoder performs optimization through positional refinement and confidence re-ranking. The entire pipeline can be end-to-end training and inferring.
As shown in Fig the proposed method takes images as input and finally predicts a given number of line segments sorted according to their confidence. It is initiated by processing input images through CNN backbone to capture multi-level feature representations. Then these features subsequently

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Rank Postihesebeeeeee Neysltives
Sort Prsdihee vert. prodiction quallity
8
prediction
ground Irulh
(b)
Figure 2: (a) The attention positions (red points) query are usually around a reference point (orange point) on the feature map in classic deformable attention module (left). In our proposed line-aware deformable attention (LADA, right), the attention positions are distributed in long and narrow area and align well with the elongated geometry of candidate line segments (blue line). (b) Ranking- baaking--assddde supervisions rank the positives above the negatives and sort the positives with respect to their prediction qualities. We define the prediction quality relating to the distance between predicted line segment and its nearest ground truth. The positives and negatives are also distinguished according to prediction quality, e.g., with a proper threshold.
undergo hierarchical encoding via a deformable Transformer encoder for candidate line proposal generation. The encoder implements spatial-awar assignment mechanism where each spatial feature point assigned to detect line segments whose centroid resides within its proximal receptive field. According to the confidence score predicted by the encoder, line segments with high scores are chosen as candidates for the decoder. In this work, we rank line segments with their prediction quality and refine their location simultaneously. The decoder takes learnable content and candidate line segment positions as initiating and gradually gathers information from the encoder feature maps. Finally, the decoder predicts the optimized confidence scores and their locations simultaneously in pure learning-based manner.
3.2 Line-Aware Deformable Attention Module
In DETRs, each query adaptively aggregates information from feature vectors at specific attention positions the corresponding feature map with the attention module. Typically, given the query vector zy and feature map æ, the multi-head attention module can be represented
A(zg, x)
Σ
W
Angk W(z(p),
(I)
where indexes the attention head and is indexes the sampled keys. WA, and W are learnable projection weights. Angk is the Scaled Dot-Product Attention weight. p indicates the attention position of query vector 2g on feature map x at the li-th head and k-th sampled point. Here we adopt similar notations as deformable DETR [6) for better understanding.
For the classical head attention module, the attention positions are generally predefined cover all feature point locations across the entire feature map. Thus, p can be defined as the k-th index of the predefined locations G
G[A].
Deformable attention introduces learnable deformable attention mechanism, where the attention positions for each query are determined by a reference point P. and multiple learnable offsets, which can be determined as
Ps + Frk(
(3)
where Fre((2g) a function Zg. e.g., a linear function. It allows the attention positions to be dynamically adapted based on the query.
Since our method uses attention module for line segment verification and refinement, the query vector should focus on regions near the corresponding candidate line segment. These regions are generally long and narrow as shown in (a). However, in the classic deformable attention module, the attention position of query is usually around reference point on the feature map, which is not

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easy to adapt to the long and narrow area for detecting line segments, To address this limitation, we propose a line-aware deformable attention (LADA) module, where the attention positions are aligned with the elongated geometry of candidate line segments. Specifically, p in LADA module can be represent as
k K K-1 where 8y and E,g are two endpoints of the candidate line segment l With this design, the receptive field of the attention module is distributed along the candidate line segments. It enables the model to better perceive their alignment with semantically image features, e.g., edges and endpoints.
5q K-1 cq + Fnh((z))
(4)
3.3 Ranking Line Candidates with Prediction Quality
For further optimizing the ranking through the prediction quality of the candidate line proposal, we employ ranking-ban -riing losses to supervise the confidence scores. The key to applying ranking-baser supervision is to define contiguous labels that can reflect the line detection quality properly. We define a simple yet efficient contiguous label I, based on the distances between the predicted lines and the nearest ground truths of them. Specifically, it can be defined as
(5) where e;, 8; means the two endpoints of the i-th line segment and e;, s are two endpoints of the corresponding ground truth. is factor that controls the threshold of distance. The ranking-based solution we used consists of two components that are visually exhibited in Fig (b) for better understanding.
4 m0 1 5, mon(lle; e ||| + ||s; s 1211, lle: s 2||
- - 122).
Ranking Positives over Negatives. Given the candidate line segments and their contiguous labels, ranking loss is used to rank the positives above the negatives. We consider the lines whose 4 > 0 as positives while others are considered as negatives. P indicates the set of positive line segments and N indicates the set of negatives. We define the ranking loss Lrank using differentiable approximation of Average Precision following [4) which can be presented as
ΣjEN HIT))) iEP ΣjEPUN (ITss)
Crank
(6)
P
where (mxyy) = mar(1, min -c.) + 0.5)) is the step function with a 6 approxiaxiitoo around the step. is the confidence score.
Sorting Positives with Prediction Quality. Each candidate has a different prediction quality according to the alignment degree with the corresponding ground truth, i.e., 4. Thus, supervise to sort the positive line segments with 4,, making the well-aligned predictions tend to get higher confidence scores. To this end, we the sorting objective Leort introduced in (24), which can be presented as
EjeP ((z
ΣjEp H HI,,)) Dl, 2 41(1 -5-1))) 12) ΣSEP (x 2 4]
Sorrt
P
E5epH(E H(I.s)
where the former term is the current sorting error and the latter term is the target sorting error, respectively, More details are referred 24
3.4 Training Strategy
During training, supervisions are added on both Transformer encoder and decoder, called encoder supervision and decoder Supervision. respectively. For the encoder supervision, binary cross-entropy loss f and L2 loss Coss are applied to supervise the confidence scores and positions of the predicted line segments. For the decoder Supervision, we use ranking- based supervision Crank and Loors to learn ranking the candidate line segments. Moreover, L2 loss L is also used to optimize the positions of line segment candidates. The final loss we used can be represented as
Liotal = X. + Xep + >app + Xy + X Aaoort
(8)
Since the ranking supervision depends on the quality of the candidate line segments from the encoder, poor prediction results at the beginning will affect the training of the decoder. Therefore, we warm up the model for several epochs. Only the encoder is supervised during the beginning of training. Then both encoder and decoder are jointly trained after the encoder predict meaningful line segments.
5

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Wireframe
YUD
Method
SAPS
SAPIO
sF
sF16 LAP
sAPS
SAPIO
SF
sF LAP
FPS
LSD 32
6.7
8.8
18.7
7.5
9.2
16.1
100.0
DWP 13
3.7
5.1
6.6
2.8
2.6
3.1
2.2
AFM 35
18.3
23.9
36.7
7.0
17.5
14.1
LGNN 23
62.3
15.8
TP-LSD
57.6
57.2
61.3
27.6
27.7
34.3
20.0
L-CNN 45
58.9
62.8
61.3
62.4
59.8
25.9
28.2
36.9
37.8
32.0
16.6
M-LSD
56.4
62.1
61.5
24.6
27.3
30.7
115.4*
M-LSD
63.3
67.1
64.2
27.5
28.5
32.4
32.9
HAWPv2 38
65.5
69.5
66.4
67.4
28.2
30.4
41.0
42.0
45
LETR 33
59.2
65.6
66.1
67.4
65.1
24.0
27.6
39.6
41.1
32.5
5,4
RANK-LETR 3I
65.0
69.7
67.7
65.6
27.6
30.1
39.7
40.6
34.1
9.0
RANK++LETR (Ours)
67.9
72.1
68.8
69.7
68.3
28.8
31.2
41.2
42.1
34.8
12.4
Table 1: Quantitative comparisons on Wireframe 13 and YUD 6 datasets. We compare our TP-LSD[4], L-CNN [55, HAWPy2 (37,, M-LSD 8 LETR B3 and RANK-LETR 31 methods. M-LSD denotes the approach of combining M-LSD and HAWP. Average precision (sAP), F-score measurement (sF) and line matching average precision (LAP) are used as metrics for comprehensive comparisons. Our method outperforms previous SOTA methods in prediction accuracy and gets faster inferring speed than other Transformer-basee methods.
proposed method with LSD [32]. DWP [3], AFM (35] LGNN 23
4 Experimental Results
4.1 Experimental Setup
4.1.1 Datasets ans Metrics
We conduct experiments in two publicly available datasets including the Wireframe dataset 13 and the YorkUrban dataset [6]. which are widely used as line segment detection benchmarks. The Wireframe dataset contains 5,000 training and 462 testing images of man-made environments, while the Urban dataset contains 102 testing images. The model is only trained on the Wireframe dataset and tested both Wireframe and YorkUrban datasets comprehensive comparison, we evaluate our models based on average precision (sAP), F-score measurement (sF) and line matching average precision (LAPP.. For fair comparison, select more than 500 prediction lines with high confidence scores of each image for quantitative analysis.
typical protocol [14] 45). For
4.1.2
Implementation Details
Our training and evaluation are implemented in PyTorch. We use 4 NVIDIA V100 GPUs for training and GPU for evaluation. We train our model for 240 epochs for warming up and 120 epochs for jointly optimizing. The learning rate is 512 x 512 and 8, respectively. We use the AdamW optimizer and set weight decay as 10-4
10-4 The image size and the batch size set as
The results of our method are predicted the features of the resolution of 128 x 128. A., Aeps Xap. X,, A, are set to 1, 10, 10, 1, 1, respectively. Moreover, we use auxiliary loss on the early layer the Transformer- based encoder with factor of 0.8. K is set to 4 for sampling. Up to 500 line segments with high scores are detected with NMS for comparison in our method.
4.2 Comparison with the SOTA
We compare our method with previous state-of- the-art methods including LSD
32 DWP B3
AFM (35). LGNN (3), TP-LSD [1. L-CNN 45), HAWPy2 (37], M-LSD 8 LETR B3 and
RANK-LETR BT. All the methods are learning based methods except the classical LSD. M- LSDDdenntes method of combining M-LSD and HAWP. LETR, RANK-LETR and our proposed RANK++LETE take Transformer as key architecture while other approaches mainly use convolutional neural networks. Some methods such as (26 25 are not chosen the comparison because they are designed to tend to generate finer line segments. Thus, it is unfair for these methods to compare

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Ground Truth
LETR
RANK-LETT LET
RANK
DAA
Figure 3: Visual examples of line segment detection results of Transformer-based methods including LETR 33 RANK-LETR 31 and our proposed RANK++LETR on the Wireframe dataset [T3. For a better visual experience, we highlight some significant differences of accurate detection with red bounding boxes and complete detection with purple bounding boxes. Our method tends to produce more accurate and complete line detection results.
standard wireframe and Yrbk Dataset. The comparisons are conducted on the Wireframe dataset 13 and the YorkUrban dataset (6] and the results are listed in Table The proposed RANK+ outperforms all previous SOTA methods in prediction accuracy. Especially, RANK++LETH gets about 2.9 percents improvement over RANK-LETR and 2.4 percents improvement HAWPv2 on s.AP metric.
Moreover, RANK++LETR demonstrates superior efficiency compared to other Transformer- bassed approaches, primarily attributed to its minimalist architectural design that eliminates computationally intensive components such as global attention mechanisms and rotation augmentation operations. It worth mentioning that a speed gap still persists when benchmarked against optimized CNN-based implementations, where we think that it is mainly due to the inherent computational complexity of Transformer architectures versus the convolutions. Our future work will focus on exploring lightweight Transformer or CNN-guided architecture for more efficient line detection approaches.
Visual examples of line segment detection results of Transformer- based methods including LETR, RANK-LETR and our proposed RANK++LETR the Wireframe dataset are shown in Fig, 3 Our method tends to produce more accurate and complete line detection results. We highlight some significant differences in accurate detection with red bounding boxes and complete detection with purple bounding boxes.
To explore the generalization capability of the proposed method in non-structured scenarios, we directly applied trained model to the NKL dataset 44 for semantic line detection. This dataset primarily contains natural landscape images. The detection results are shown in the Fig The experiment demonstrates that even without training on the NKI. dataset 44 our model still exhibits perception ability for semantic lines.
7

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Line Optimizer
Ranking Loss
w/o Warm-up
Atlention Points
Wireframe
LADA
R:S
SAP
SAP
63.2 2.3)
68.1
4
65.5 (-)
70.8
4
67.0 (1 1.5)
71.4 0.6)
1:1
4
67.2 1.7)
71
4
67.9 (T44
(t
1:1
4
66.0
70.6
1:0
4
67.3
71.6
0:1
4
48.7
51.4
2
67.4
71.5
1:1
8
67.9
72.2
Table 2: Ablation and parameter study of our method on the Wireframe construct baseline method without decoder according our modeling and then gradually add different components to explore their relevance and impact. Experimental results show that a second optimization can bring performance improvement, and both the LADA module and ranking loss contribute to better detecting results. Different numbers of attention points and weights of RS losses are also tested for a comprehensive study.
13 dataset. We first
Figure 4: Visual examples for non-structured environments on NKL dataset Our method demonstrates strong generalization capability in semantic line detection scenarios.
4.3 Ablation and Parameter Study
To verify the effectiveness components and find the influence of the hyperparameters our proposed approach, we conduct ablation and parameter study of RANK++LETR. The experience is conducted on the Wireframe dataset and sAP results are reported Table 2
As baseline method, we apply ResNet50 as feature extractor and 6 layers Transformer encoder from classical deformable DETR for line segment detection with matched prediction strategy, where the feature point closest to the centroid of line segment is responsible for predicting it. Different from RAÑK-LETR, branch network and rotation enhancement network are no longer used. Based on the baseline, we use layers deformable Transformer decoder and construct the complete proposal generating and candidate optimizing pipeline, where the selected proposals are used to initialize the inputs of the decoder. It reveals significant performance gap between the proposed pipeline and baseline framework, which proves the effectiveness of our detection modeling. The line-aware deformable attention and ranking- based loss are then added individually. Both of them can bring obvious performance improvement. By combining the LADA module and ranking- based Loss together, we verify that proposed method can get the best results benefiting from each novel component. Moreover, we find the warmup of encoder is also important to get a faster convergence for better results. The experimental results are listed in Table 2 The contribution of each component can be found during the performance differences.
The parameter study is conducted LADA module and ranking-based losses. For LADA module, we explore the influence of the number of attention points, by changing the default to 2 and respectively. We find performance is not sensitive to the number of attention points when it exceeds

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After Optimization Before Optimization
Table 3: Evaluation of the outputs before and after optimization, respectively. Adding line optimizer for learnable ranking the confidence scores and refining the positions brings an intuitive detecting performance improvement.
Distribution (%)
40
75
125
175
225
275
325
375
475
Metric
Before Optimize
After Optimize
Figure 5: Statistics of the distribution the rank of corresponding ground truth being recalled. The ranking-bass based supervision contributes gaining high ranking distribution of the prediction, yield- ing a better line segment detection performance.
sAps
64.1
67.9
SAp10
68.8
72.1
sF5
64.5
66.3
SF10
67.4
68.8
Figure 6: Attention maps of our proposed line-aware deformable attention module (LADA) in the decoder. We show the attention map of the last decoder layer for clear viewing. Brighter areas mean more attention. The attention points generally appear around the image lines, making the proposed LADA casier to capture line features.
4, which may be because it obtains enough useful information such endpoints and edges. For ranking-based supervision, we test the ranking loss and sorting loss individually to find their roles in training. We observe that only adding ranking loss can bring limited improvement and only using sorting loss will even get worse performance. We think both loss terms should be used together in the task, which may be because they complement each other.
4.4 Analysis and Interpretation
In order to gain a deeper understanding of the proposed method, we conduct further analysis and feature visualization to exhibit intermediate process. As method uses encoder to get initial proposals and optimize the score and position with decoder in successive processing, an intuitive way explain the pipeline is compare the predicting performance of the two outputs in one model directly. As demonstrated in Table 3, the line optimizer brings an intuitive detecting performance improvement.
We then compare the ranking quality to verify the effectiveness of the supervision and further explore the underlying reason for performance improvement. For the output results before and after line optimization, we select 500 line segments with high confidence and gather them into two groups, respectively. For each ground truth line segment, the closest line segment be found from each group and the ranking of the line segment among its group can be recorded. It indicates that the corresponding ground truth will be recalled at the ranking in these predictions. It is obvious that better method should gain higher ranking quality. In other words, with fewer predictions, more correct line segments can be detected. We statistically analyze the distribution of the rank of corresponding ground truth being recalled and the results are shown in 5 The ranking-based supervision contributes gaining high ranking distribution of the prediction, yielding better line segment detection performance.
We also visualize the distribution of attention points in line-aware deformable attention (LADA). 6 images are randomly selected with the attention heat map masking on, which is shown in Fig We show the attention map of the last decoder layer for clear viewing. Brighter areas mean more attention. The attention points generally appear around the image lines, demonstrating that the proposed LADA easier to capture line features and more suitable for line detection tasks.
In addition to the module innovation, from a holistic perspective, the proposed method leverages the encoder-decoder design where the encoder generates line segment proposals while the decoder

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performs confidence re-ranking and position refinement of these segments. For the encoder, our method relies on its recall capabiliiy, whereas the decoder provides a secondary opportunity to enhance detection performance by ranking confidence and optimizing locations for the recalled line segments. Theoretically, since the encoder itself trained as line segment detector, cases where the proposed encoder itself demonstrates significantly inferior recall performance compared to other detection methods are unlikely to occur.
Conclusion
In this work, we develop proposal-based line segment detection methods with novel pipeline where the high-quality line proposals are ranked and optimized with learnable features. To achieve this goal, we specially design novel line-aware deformable attention (LADA) module in which attention positions are distributed in a long narrow area and can align well with the elongated geometry of line segments. For better supervising the ranking of selected proposals, ranking-based losses are employed and modified with proper contiguous labels generation to adapt line segment detection task. Building the above techniques. construct novel line segment detection model with encoder-decoder architecture named RANK+LLTRR Extensive experiments show that our method outperforms previous SOTA methods in prediction accuracy and gets faster inferring speed than other Transformer-base methods.
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Reviews:
[{"review_id": "Lo2fmJIJlC", "reviewer": "Reviewer_3gFX", "summary": "This paper proposes RANK++LETR, an end-to-end Transformer-based framework for line segment detection. The architecture builds upon deformable DETR and introduces a Line-Aware Deformable Attention (LADA) module that aligns attention patterns with elongated line structures. Furthermore, the paper innovates by using ranking-based loss functions for line candidate refinement, improving both scoring and localization of predictions. The method is evaluated on Wireframe and YorkUrban datasets, outperforming prior SOTA in accuracy and inference speed.", "questions": "How does RANK++LETR handle complex or cluttered scenes with overlapping or occluded line segments?\n\nWould the model benefit from unsupervised or semi-supervised pretraining to enhance robustness? Can LADA be adapted for use in curved feature detection (arcs or splines)?\n\nWhat is the impact of increasing the number of attention points beyond 8 on both performance and runtime? How sensitive is the model to inaccuracies in the continuous label generation process?", "limitations": "Yes. The authors adequately acknowledge the limitations of their current dataset scope and explicitly discuss the performance–complexity trade-off.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 2, "strengths_and_weaknesses": "Strengths:\nSolid architectural and methodological enhancements, with clear ablations and improvements over strong baselines.\nThe LADA module and application of ranking-based supervision for geometric primitives are novel contributions.\nDemonstrates strong gains in both accuracy and inference time over previous methods, showing practical viability. Explanations, figures, and experiments are well presented, especially the attention visualizations and ablation study.\n\nWeakness:\nSome improvements like ranking losses are incremental over RANK-LETR.\nThe method still requires multiple components (e.g., LADA, ranking, warmup), which increases implementation complexity.\nNo discussion on generalizing to open-scene or non-structured environments beyond wireframe-type datasets.", "was_revised": true, "final_justification": "I am maintaining my overall score of 5 (Accept).\n\nThis paper makes a meaningful contribution to line segment detection, combining architectural innovation (LADA) and training strategies (ranking losses) with strong empirical results. The rebuttal further solidifies its clarity and relevance.", "rebuttal": "Thank you for your detailed review and valuable suggestions. We will try our best to clearly explain your concerns point by point.\n\nQ1: Multiple components increase implementation complexity. \nA1: Ranking loss and warm up are used in the training phase. So they will not affect the efficiency of the model in inference. LADA brings a limited complexity increase, but also an obvious performance improvement.\n\nQ2: Benefit from unsupervised or semi-supervised pre-training? \nA2: It is a very good suggestion. Some studies have tackled the task with unsupervised or semi-supervised approaches (e.g., [25][26] in our original paper). Our future work will focus on unsupervised and semi-supervised methods, such as pre-training the network through bootstrapping or synthetic labeling, as well as possibly performing some appropriate model adaptation. We think that there will be a significant improvement in generalization at least.\n\nQ3: Extend LADA to curved feature detection.\nA3: We strongly believe that LADA can be used for curved feature detection since they almost share the same design motivation, which is to sample the attention points along the high-confidence line/curve features, allowing the model to learn relative information. On the contrary, a traditional attention machine tends to focus around a point. \n\nQ4: Impact of increasing the number of attention points beyond 8.\nA4: We conduct an experiment that increases the number of attention points to 16. The experimental results show that there is not much change in accuracy and inferring time. We think that the accuracy has reached saturation, and an excessive increase in the number of attention points may only continue to increase (not much) computational overhead.\n\nMetric|p=4|p=8|p=16\n--------|-----|------|------\n$sAP_5$ | 67.9|67.9|67.7\n$sAP_{10}$| 72.1|72.2|71.9\n\nQ5: Sensitive to inaccuracies in the continuous label generation.\nA5: We randomly perturb the positions of all candidate lines by 1-5 pixels, which has some impact on $sAP_5$ and little impact on the $sAP_10$. Moreover, the candidate lines are also learned and predicted by the network. So they are inherently inaccurate compared to the ground truth, but generally do not deviate significantly, as analyzed in line282-289 in the original paper.\n\nQ6: Complex scenes including overlapping/ occluded/ non-structured scenes.\nA6: (1) Overlapping： Dealing with overlapping is one of the strengths of our method. Our method recalls complete line segments because of its focus on global information, and will not be segmented or truncated due to overlapping. It is reflected in columns 2/5 of Fig.3 and the result visualization in the supplementary materials (e.g., carpets, windows and cabinets).\n(2) Occluded： For slight foreground occlusion, our method exhibits good anti-occlusion ability, as shown in the 2nd column of Fig.3, and (r1c2), (r1c3), etc., in the result visualization in the supplementary materials (e.g., foreground lights, chairs). For severely occluded scenes, our method considers them as two line segments on both sides of the occlusion, as shown in the supplementary materials (r3c5), which aligns with human intuitive perception.\n(3) Non-structured scenes： For non-structured scenes, we directly applied the trained model to the semantic line detection dataset (NKL). We find that the detection of the boundary between linear strip-shaped traces is more in line with human perception (such as sea level, bridge, road, grass), while the detection of the boundary between serrated traces is relatively random (mountain-sky boundary, rugged tree crowns). We guess that the reason for this is the large gap between the line patterns in such scenes and the images in the training set. Since no figures can be supplemented in the rebuttal, we will add some visualization of non-structural scenes in our final version."}, {"review_id": "a5QgtsCShm", "reviewer": "Reviewer_XaiL", "summary": "This paper addresses the task of line segment detection. The main claim is that previous transformer-based methods assign detection scores that do not reflect the actual quality of the predicted lines.\n\nThe proposed method builds on an encoder-decoder transformer architecture with deformable attention. The encoder makes an initial set of line predictions. The novelty lies in the decoder, which refines these predictions and adjusts the scores to better align with their geometric quality.\n\nTo enforce this alignment by the decoder, the method adopts ranking-based supervision, similar to RANK-LETR. \nA new deformable attention module is proposed, which samples reference points along narrow regions.\n\nThe method outperforms prior state-of-the-art approaches. Ablation studies highlight the benefits of using a decoder stage, in contrast to encoder-only designs like RANK-LETR.", "questions": "1. Clarify the difference and the novelty between your proposed deformable attention and the one of the decoder of DINO-DETR. (see my point in weakness)\n2. Would using DINO-DETR directly, combined with the proposed loss functions of this paper, yield similar results? This possibility should be addressed more clearly. \n3. Can you show examples of failure cases and explain them? For example, missed detections or cases where predicted scores and line quality are still not well aligned.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\nWriting: The paper is well written, easy to follow, and clearly presented.\nRelated Work: To my knowledge, the related work is satisfactory.\nTechnical Contribution: Using a decoder to align predicted scores with line quality is a novel and interesting idea. It makes good use of the encoder-decoder architecture, in contrast to RANK-LETR which uses only an encoder.\nExperiments: The method is compared to strong SOTA baselines and outperforms them. Ablation studies are provided, showing the benefits of using a decoder to optimizer the prediction, the impact of the number of attention points and different loss components used to train the model.\n\n\nWeakness\nI am really not convinced by the proposed deformable attention module. It seems very similar to the decoder deformable attention in DINO-DETR, which uses bounding boxes as reference points. If these boxes collapse onto a line, the behavior appears equivalent to the proposed method. Please clarify the difference and the novelty.\n\nCompared to RANK-LETR, the modifications appear relatively small. The main architectural change is the decoder and the deformable attention, which—again—looks very similar to DINO-DETR. Would using DINO-DETR directly, combined with the proposed loss functions of this paper, yield similar results? This possibility should be addressed more clearly. \n\nOne of the main claims is improved inference speed. But , according to Table 1, the method is not faster than RANK-LETR or LETR.\nThe ablation study should include the same proposed  architecture but without the proposed deformable attention module. This should be included to demonstrate that the new module clearly improves performance.\n\nThe paper does not discuss limitations or failure cases. Can you show examples of failure cases and explain them? For example, missed detections or cases where predicted scores and line quality are still not well aligned.\n\nSmall details:\nPlease report training in number of iterations, not just epochs. Since an epoch refers to one pass through the dataset, the number of iterations can vary across datasets and is not always clear.\n\nTable 1: Please define FPS. I assume it refers to inference speed, but this should be explicit.\n\nTable 2: It is not immediately clear that the first row corresponds to a baseline without the encoder. Please clarify.\n\nEquation 8: How did you choose the hyperparameter values for the loss weights? Please explain.\n\nFigure 1: Why is there a blue line from the ground truth to the initial position and score boxes? Please clarify its meaning.\n\n\nFigurte 1.. why is there a blue lin from grounth truth to the initial positon & scores boxes?", "was_revised": true, "final_justification": "The authors clearly answer to my main concerns by provided a detailed rebuttal.", "rebuttal": "Thank you for your detailed review and valuable suggestions. We will try our best to clearly explain your concerns point by point.\n\nQ1: Difference between LADA and the attention in DINO \nA1: The difference can be noticed in Fig.2(a). For the attention in DINO, the referencing point is the center point of the candidate box, and the attention points are generally distributed around the referencing point. Please refer to Fig.6 in the Deformable DETR article for a similar visualization. This distribution is not conducive to the perception of narrow targets such as line segments. The LADA uniformly samples the candidate line segments as the referencing points, so that the attention points are distributed along the line segments, which is beneficial for the perception of narrow areas. The results of our ablation study also confirm this point. For attention in models such as DINO, the shape of the candidate box is only related to the boundary of reference points. Not to mention that the collapsed box can only approximate horizontal or vertical line segments.\n\nQ2: Compare with DINO+LSD(+line ranking loss)\nA2: DINO+LSD can be seen as the 2nd row in Table 2 (sAP5:65.5) and the DINO+LSD(+line ranking loss) can be seen as 4th row in Table 2 (sAP5:67.2) in the original paper, which has a performance gap with our proposed method.\n\nQ3: Difference between the proposed method and RANK-LETR\nA3: (1) The proposed method uses a pure learnable Transformer-based decoder to re-rank (scores) and optimize (locations) the line segment simultaneously, while Rank-LETR uses CNN CNN-based manner to re-rank the score with pre-designed hyperparameters. (2) Design line-aware deformable attention (LADA) for line segment detection. (3) Introduce a powerful line ranking loss. The key insight here is finding ranking-based loss naturally suitable for the optimizing process in our pipeline, and designing the contiguous labels $l_i$ for the LSD task. \n\nQ4: Inference speed/Ablation study\nA4: Our method is faster than LETR and Rank-LETR in Table 1. The higher the FPS value is, the faster. In Table 2, we conduct the experiment using a module adding pipeline, and the comparison between with and w/o LADA can be obtained by comparing the results in 2/3 rows and 4/5 rows, respectively. sAP5 increase 1.7 when adding the LADA module compared to the baseline, while decrease 0.7 when subtracting the LADA module. Please note that it also reflects the ability of Line Ranking Loss. The change in improvement is caused by the ability overlapping of the two proposed modules.\n\nQ5: Limitation and failure cases\nA5: The main limitation is the speed gap benchmarked against optimized CNN-based implementations, which is mentioned at line221-227 in the original paper. Most failure cases happen in dense short line segment detecting, e.g., the 3rd column in Fig.3. It is worth noting that this has always been a common problem in learning based methods. Compared to the existing methods, our method has made improvements in this issue.\n\nQ6: Training Iterations\nA6: Our training iteration is set as 225000.\n\nQ7: Explain FPS\nA7: Yes, FPS is short for Frame Per Second, which indicates the number of images the model can process in 1 second. A large value indicates high inference speed.\n\nQ8: Row 1 in the ablation study\nA8: The first row corresponds to a baseline with an encoder and without a decoder.\n\nQ9: Explain the hyperparameter values\nA9: $\\lambda_c$, $\\lambda_{ep}$, $\\lambda_{dp}$ are set as the same as RANK-LETR, $\\lambda_r$, $\\lambda_s$ are set according to previous study on ranking based losses ([24] in the paper) and parameter study. \n\nR:S|2:1|1:1|1:2\n--------|-----|------|------\n$sAP_5$ | 67.7|67.9|67.1\n\n\nQ10: Explain the blue line from the ground truth to the initial position and score boxes in Fig.1.\nA10: The initial scores and positions are supervised by the ground truth line segments during training."}, {"review_id": "UfAcnDOdQw", "reviewer": "Reviewer_NZBC", "summary": "This paper presents RANK++LETR, a Transformer-based method for line segment detection that introduces two key innovations: (1) a line-aware deformable attention (LADA) module to better capture elongated line features by aligning attention points along candidate segments, and (2) a ranking-based supervision strategy that optimizes confidence scores by learning to prioritize high-quality predictions. Building on deformable DETR, the model separates line proposal generation (encoder) from refinement and re-ranking (decoder), achieving state-of-the-art accuracy on Wireframe and YorkUrban datasets while maintaining efficient inference. The work advances structured scene understanding by improving both detection quality and geometric alignment of line segments.", "questions": "- Most of the scenarios shown in the paper are those with more structured features. Can the algorithm perform better in some scenarios with more unstructured features, or has it been tested on relevant data? As far as the dataset is concerned, the dataset used in this paper does not seem to be a particularly large dataset (5000 training images).\n- The lines are all 3D structures, can 2D inspection have a large application?", "limitations": "The innovativeness of this paper is limited overall, although the results of this paper are particularly good. In addition, the FPS of the method in this paper is relatively low, and some training and inference overheads are not reported.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- This paper introduces an innovative line-aware deformable attention (LADA) module that effectively addresses the geometric mismatch in standard attention mechanisms by distributing attention along elongated regions, significantly improving feature alignment for linear structures.\n- The experimental validation demonstrates state-of-the-art performance on standard benchmarks (Wireframe and YorkUrban datasets), with particular emphasis on the method's computational efficiency, achieving faster inference speeds than comparable Transformer-based approaches while maintaining high precision. The clear ablation studies and visualizations further strengthen the technical rigor of this work.\n\nWeaknesses:\n- Limited innovation. The whole Transformer structure is more similar to LETR. The introduction of the ranking-based network layer and ranking-based loss function in the Transformer is also existing research.\n- While faster than Transformer baselines, it’s still slower than optimized CNNs (e.g., M-LSD). No FLOPs/parameter comparison provided. Multi-scale encoder-decoder imposes memory overhead (no graphics memory footprint reported)\n- Key thresholds (e.g., distance factor δₗ in Eq. 5) lack theoretical justification.", "was_revised": true, "final_justification": "Thanks to the author for the detailed response. I think the author's work is valuable, but given some of the existing problems with the article, I keep my original rating for the weak acceptance.", "rebuttal": "Thank you for your detailed review and valuable suggestions. We will try our best to clearly explain your concerns point by point.\n\nQ1: Concern about innovation\nA1: Our innovation can be summarized as: (1) propose a pure end-to-end learnable propose-optimize line segment detection framework. Moreover, we consider that introducing and modifying proper module structures appropriately to adapt to a specific task (LSD) for improving performance/ solving problems is also an innovation, just like LETR itself. (2) design line-aware deformable attention (LADA) for line segment detection. (3) line ranking loss. The key innovation here is finding ranking-based loss naturally suitable for the optimizing process in our pipeline, and designing the contiguous labels $l_i$ for the LSD task. (4) Performance: more accurate than previous methods and faster than previous Transformer-based method.\n\nQ2: Slower than optimized CNNs/FLOPs/parameter\nA2: We consider it a drawback of our approach, as discussed in our original paper. CNNs are truly fast. Our future work will employ efficient Transformers or even replace some modules with CNNs. We keep trying our best to establish a new milestone in LSD. Comparison on FLOPs and parameter are listed below. Rotation augmentation operation imposes much calculation overhead for Rank-LETR.\n\nMetric|Rank-LETR|Rank++LETR\n--------|-----|------\nGFLOPs |138|301\nparameter| 27M|25M\n\n\nQ3: Theoretical justification for selecting $\\delta_l$:\nA3: We choose distance factor $\\delta_l$ according to existing metrics AP/sF_x, for x in [5, 10, 15]. Thus $\\delta_l$ can be chosen as [1/5, 1/10, 1/15]. We choose it 1/10 in our experiments.\n\nQ4: Performance on unstructured scenes:\nA4: For non-structured scenes, we directly applied the trained model to the semantic line detection dataset (NKL). We find that the detection of the boundary between linear strip-shaped traces is more in line with human perception (such as sea level, bridge, road, grass), while the detection of the boundary between serrated traces is relatively random (mountain-sky boundary, rugged tree crowns). We guess that the reason for this is the large gap between the line patterns in such scenes and the images in the training set. Since no figures can be supplemented in the rebuttal, we will add some visualization of non-structural scenes in our final version.\n\nQ5: Application in 2D inspection:\nA5: Yes, it can be directly used in 2D inspection, such as document upright adjustment, graphic vectorization."}, {"review_id": "20vCIOVyE3", "reviewer": "Reviewer_2ecv", "summary": "This paper introduces a proposal-based line segment detection method with a pipeline where the high-quality line proposals are ranked and optimized with learnable features. To achieve this goal, a line-aware deformable attention (LADA) module is proposed, where attention\npositions are distributed in a long narrow area and can align well with the elongated geometry of line segments. For better supervising the ranking of the selected proposals, ranking-based losses are employed and modified with proper contiguous labels generation to adapt line segment detection task.", "questions": "(1) To explain in theory and experimentally how the proposed attention module literally helps the detection of line segments.\n(2) To explain in theory and experimentally how the proposed ranking mechanism literally helps the detection of line segments.\n(3) To show the weaknesses of the proposed solution.", "limitations": "Not really. It would be beneficial to discuss when the proposed method fails in the experiments.", "rating": 2, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Quality -\n\nThe paper has been well structured, with good balance between theory and experiments. The results seem to suggest that the proposed approach works better than the other state of the arts. However, the connection between the attention module and the line segmentation is not clear in the current form. It is not clear how the ranking of line candidates can contribute towards the optimized results.\n\nClarity -\n\nThe major concern is that it is not clear how the attention module makes the samples better aligned with semantical image features. In addition, it is not clear how the ranking theory improves the alignment of line segments. There is no evidence in the theory and experiments for justifying these two aspects.   \n\nSignificance - \n\nThe topic is important. The proposed solution seems to be incremental, compared against the other methods. The results also look minor improvement. \n\nOriginality - \n\nThe proposed method is not original. In fact, it is driven by the applications where several established modules are combined for better line segment detection.", "was_revised": true, "final_justification": "I have read the rebuttals of the authors but not convinced by the arguments. I would not change my score previously entered.", "rebuttal": "Thank you for your detailed review and valuable suggestions. We will try our best to clearly explain your concerns point by point.\n\nQ1: Evidence in the theory and experiments for justifying LADA and Line Ranking Loss. Connection/Contribution are not clear.\nA1: (1) Connection and theory of LADA are in Sec 3.2. We design line-aware deformable attention (LADA) module in which attention positions are distributed in a long narrow area and can align well with the elongated geometry of line segments. Theoretical expression style is similar to classical works such as Deformable-DETR([46]).\n(2) Connection and theory of Line Ranking Loss are in Sec 3.3. We propose line ranking loss in line segment detection task with the design of contiguous labels according to the detection quality. Theoretical expression style is similar to classical works such as RSLoss ([24]), RANKED ([4]).\n(3) The effectiveness of our proposed LADA and Line Ranking Loss are shown in our ablation study in Sec 4.3, which bring 1.5 and 1.7 improvement respectively on the $sAP_5$ metric.\n(4) Visualization of LADA is exhibited in Fig.5. Ranking quality can be reflected in Fig.4.\n\nQ2: Concern about the innovation.\nA2: Our innovation can be summarized as: (1) propose a pure end-to-end learnable propose-optimize line segment detection framework. Moreover, we consider that introducing and modifying proper module structures appropriately to adapt to a specific task (LSD) for improving performance/ solving problems is also an innovation, just like LETR itself. (2) design line-aware deformable attention (LADA) for line segment detection. (3) line ranking loss. The key innovation here is finding ranking-based loss naturally suitable for the optimizing process in our pipeline, and designing the contiguous labels $l_i$ for the LSD task. (4) Performance: more accurate than previous methods and faster than previous Transformer-based method.\n\nQ3: Concern about the improvement.\nA3: We argue that our method gets an obvious improvement. Our method get 2.4/2.6 improvement on $sAP_5$, $sAP_{10}$ compared with previous the SOTA method, and get 2.9/2.4 improvement compared with the SOTA Transformer-based method. These two previous SOTA method achieve 1.4/0.8 and 1.7/2.6 improvement on the same dataset, respectively.\n\nQ4: Show the weaknesses of the proposed solution.\nA4: The main weakness is the speed gap benchmarked against optimized CNN-based implementations, which is mentioned at line221-227 in the original paper."}]

Author rebuttal:

Meta-review: This paper proposes a Transformer-based framework for line segment detection. The proposed method is built on deformable DETR and introduces a Line-Aware Deformable Attention (LADA) module that aligns attention with elongated line geometries. It also employs ranking-based supervision to refine candidate proposals and ensure confidence scores reflect prediction quality. Experiments on benchmark datasets show SOTA accuracy with efficient inference.

Four expert reviewers initially raised several concerns including limited novelty, lack of discussion on generalisation to open-scene or non-structured environments, lack of time complexity analysis, and no discussion of limitations or failure cases. The rebuttal addressed most of these concerns, resulting in two accept, one borderline accept and one reject. The main concern from the rejecting reviewer relates to the authors rebuttal statement: "Theoretical expression style is similar to classical works such as RSLoss ([24]) and RANKED ([4])". This phrasing caused confusion for the reviewer. However, the AC believes that the authors provided a clear explanation of the term “theoretical expression style” and thus recommends acceptance.
Decision: Accept (poster)