Point2RBox-v3: Self-Bootstrapping from Point Annotations via Integrated Pseudo-Label Refinement and Utilization
Authors: Teng Zhang
Ziqian Fan
Mingxin Liu
Xin Zhang
Xudong Lu
Wentong Li
Yue Zhou
Yi Yu
Xiang Li
Junchi Yan
Xue Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Oriented Object Detection
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Driven by the growing need for Oriented Object Detection (OOD), learning from point annotations under a weakly-supervised framework has emerged as a promising alternative to costly and laborious manual labeling. In this paper, we discuss two deficiencies in existing point-supervised methods: inefficient utilization and poor quality of pseudo labels. Therefore, we present Point2RBox-v3. At the core are two principles: $\textbf{1) Progressive Label Assignment (PLA)}$. It dynamically estimates instance sizes in a coarse yet intelligent manner at different stages of the training process, enabling the use of label assignment methods. $\textbf{2) Prior-Guided Dynamic Mask Loss (PGDM-Loss)}$. It is an enhancement of the Voronoi Watershed Loss from Point2RBox-v2, which overcomes the shortcomings of Watershed in its poor performance in sparse scenes and SAM's poor performance in dense scenes. To our knowledge, Point2RBox-v3 is the first model to employ dynamic pseudo labels for label assignment, and it creatively complements the advantages of SAM model with the watershed algorithm, which achieves excellent performance in both sparse and dense scenes. Our solution gives competitive performance, especially in scenarios with large variations in object size or sparse object occurrences: 66.09\%/56.86\%/41.28\%/46.40\%/19.60\%/45.96\% on DOTA-v1.0/DOTA-v1.5/DOTA-v2.0/DIOR/STAR/RSAR.

Published as a conference paper at ICLR 2026
POINT2RBOX-V3: SELF-BOOTSTRAPPING FROM POINT ANNOTATIONS VIA INTEGRATED
PSEUDO-LABEL REFINEMENT AND UTILIZATION
Teng Zhang"", Ziqian Fan"*, Mingxin Liu', Xin Zhang", Xudong Lu", Wentong Li
Yue Zhou', Yi Yu?, Xiang Li Junchi Yan', Xue Yang 163
School of Automation and Intelligent Sensing & SAI, Shanghai Jiao Tong University
2 South China University of Technology Nankai University
"The Chinese University Hong Kong 'Nanjing University of Aeronautics and Astronautics
"Eat China Normal University The Ohio State University
"Equal contributinn on Eoorresponding ng author
ABSTRACT
Driven by the growing need for Oriented Object Detection (OOD), learning from point annotations under weakly-suueervied framework has emerged as promis- ing alternative costly and laborious manual labeling. In this paper, we discuss two deficiencies in existing point-supervised methods: inefficient utilization and poor quality of pseudo labels. Therefore, we present Boxnt2RBox-v3. At the core two principles: 1) Progressive Label Assignment (PLA). It dynamically timates instance sizes in a coarse yet intelligent manner at different stages of the training process, enabling the use of label assignment methods. 2) Prior-Guided Dynamic Mask Loss (PGDM-Loss). It is an enhancement of the Voronoi Wa- tershed Loss from Boint2RBox-v2, which overcomes the shortcomings of Water- shed in its poor performance in sparse scenes and SAM's poor performance in dense scenes. To our knowledge, Point2RBox-v3 dynamic pseudo labels for label assignment, and it creatively complements the advantages of SAM model with the watershed algorithm, which achieves excel- lent performance in both sparse and dense scenes. Our solution gives competitive performance, especially in scenarios with large variations object size or sparse object occurrences: 66..6666.66666 /11.//66666////// W..0DOTTA...500TTA222000OSSSSSSS
the first model employ
96% on DOTA-
INTRODUCTION
Oriented object detection (OOD) has attracted growing attention due to the increasing demand for object direction estimation diverse fields, including autonomous driving (Feng et al., 2021), acrial images (Fu et al., 2020; Liu et al., 2017; Xia et al., 2018; Yang & Yan, 2022; Yang et al., 2018), scene text (Liao al., 2018; Liu et al., 2018; Zhou al., 2017), retail (Goldman al., 2019; Pan et al., 2020), and industrial inspection (Liu et al., 2020; Wu et al., 2022).
Training OOD models requires annotations as supervisory signals. Traditional rotated bounding boxes (RBoxes) provide accurate supervision but are costly: annotating each RBox is 36.5% more expensive than a horizontal box (HBox) and 104.8% more than point (Yu et al., 2024), high- lighting the potential of weakly-supervised OOD. HBox-based approaches such as H2RBox (Yang et al., 2023) and H2RBox-v2 (Yu et al., 2023; 2025b) have shown strong results, while point-based methods are rapidly advancing.
Current pupet-sup methods fall into four categories: (1) Pseudo generation using multiple instance learning and class probability maps (Luo et al., 2024; Ren et al., 2025); (2) Knowledge combination with one-shot learning (Yu et al., 2024); (3) Point-prompt OOD leveraging SAM's zero-shot capability (Cao et al., 2023; Zhang al., 2024a; Liu et al., 2025b; Lu & Bie, 2025;

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Point2RBox-13 Point2RBox-v2
RS46
2735
Potece
NA 42
NA100R5 1000
Figure I: (Left) Visual comparisons with the state-of-the-an method Point2RBxx-v2. The boxes de- tected by our method wrap the objects more tightly, with fewer missed detections. (Right) Radar plot comparing the performance of our method with 10 other state-of-the-art methods across bench- mark datasets. Methods with '(e)" in the legend indicate end-to-end version, while those without "()' represent two-stage version.
Kirillov et al., 2023); (4) Spatial layout for pseudo label generation (Yu al., 2025a). Despite these advances, pseudo label quality remains bottleneck, which motivates our approach.
Motivation. Our motivation mainly stems from the lack of quality and utilization efficiency of pseudo labels in all current end-to-end methods. End-to-end point supervision method requires label assignment for the Feature Pyramid Network (FPN) (Lin et al., 2017a), which plays a crucial role in terms of detecting scale information. Most existing methods simply distribute them to the same layer, such as Point2RBox-v2 (Yu et al., 2025a). This wastes the scale information contained in the pseudo labels generated by the model. Poimt2BBox-v2 benefits from the "Voronoi Watershed Loss" for generating masks as pseudo labels. However, the watershed algorithm is less effective in sparse scenes. As SAM can also provide masks, investigated it alternative but found that SAM fails in dense scenes. Can we enhance the detection capability of the model by focusing on these two aspects? In this paper, we have an in-depth discussion about this issue to explore how to fully uncover the potential of pseudo labels.
Highlights. 1) Point2RBox-v3 proposed for supet-supervised OOD, advancing the state of the art (SOTA) as displayed in Figure and Tables 1-2. 2) We improve the quality and utilization efficiency of pseudo labels, compared to Pox-22 without incurring a significant increase in training costs, which reveals more possibilities for the application and quality of pseudo labels in wcakly-supervised object detection for future researchers.
Contributions. 1) To our knowledge, Point2RBox-v3 -3 is the first end-to-end point supervision model to explore how to assign pseudo labels to multiple FPN levels, and it complements the advantages of the SAM model with the watershed algorithm, which achieves excellent performance in both sparse and dense scenes. 2) We extend our method to partial weakly-supervised tasks (Liu et al., 2025a) beyond point supervision, demonstrating its adaptability and scalability. 3) The training pipeline and detailed implementation are elucidated. The source code will be made publicly available.
2 RELATED WORK
Point-supervised Oriented Detection. Due to the low-cost and high-quality requirements in the field object detection, point-supervised oriented object detection has become one of the research focuses. Some models apply the powerful zero-shot segmentation performance of the Segment Anything Model (SAM) (Kirillov et al., 2023) in their pipelines, such as P2RBox (Cao et al, 2023), PMHO (Zhang et al., 2024b), PointSAM (Liu al., 2025b) and PMS-SAM-RO Bie, 2025). Apart from these models, there are also some other models whose strategies involve creating pseudo labels using traditional machine learning other methods. The PointOBB series
(Lu &
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PGDM
Pseudo Masks
flip
PLA
pgdia
Reg. Subnet
rotate
scale
0, Res Ner
h
L
otthers
Shared Backbone & Neck
Cls. Subnet
cls
Utilization: Progressive Label Assign
Refinement: Prior-Guided Dynamic Mask
Prior-Guiden Selector
Pseudo Label
Hierarchy
sparso
Soow
Label Assign
14,9 0.67
scale info
SAM
Score: 0.87
WaterShed (narly stage!
dense
Comb ned Btore 5.0 @
NetForward
0ate stage!
WaterShed
Figure 2: The training pipeline of Point2RBox-v3. Progressive Label Assign utilizes scale info from Dynamic Mask provides
pseudo label to dynamically assign gt point (see Figure 3). enhanced mask supervision information (see Figure 4). Lothers are the loss functions inherited from Pox-22RBox-v2 (see Appendix A.2).
(Luo et al., 2024; Ren et al., 2025; Zhang et al., 2025b) employs techniques such as multi-instance learning and class probability graphs to generate pseudo RBoxes. Differently, Point2RBox (Yu et al., 2024) acquires knowledge from single sample examples through a knowledge combination method, and Point2RBox-v2 (Yu al., 2025a) applies traditional machine learning algorithms such as watershed and edge detection. The technology based on SAM can achieve simpler applications by leveraging powerful base models, while other methods offer solution that does not require liance on other components. However, existing approaches have not yet explored their combination enhance model performance.
Label Assignment. Point-supervised oriented object detection lacks scale information, and there- fore cannot directly apply label assignment. PointSAM et, 2025b) adopts ViT (Dosovitskiy et al., 2020) backbone. Point2RBox (Yu et al., 2024). Point2RBox-v2 (Yu et al., 2025a) adopt only single FPN feature level. PointOBB (Zhang al., 2025a) selects grid points within the central region around each ground-truth point across all feature levels positive samples. PointOBB-v3 (Zhang et al., 2025b) applies gating mechanism each FPN feature layer during training to pro- duce corresponding gating scores, which are then used to automatically aggregate the multiple FPN output layers into a fused feature map for feature extraction. Unfortunately, these methods discard the classical multi-level label assignment in FPNs. We show that this omission largely explains the gap between point and full supervision, and that leveraging coarse scale cues from pseudo labels can effectively narrow this gap.
3 METHOD
3.1 OVERVIEW AND PRELIMINARY
In brief, the definition of the Puint-supervised Object Detection task is follows: during the train- ing phase, the model inputs consist an image and the point annotations P {(x,, y1) for all instances within the image (these points typically, though not strictly, defined the center points
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label assign by WaterShed
label assign by NetForward
Training epoch
Assign P2
Assign P3
Assign P4
Assign PS
Assign P6
Assign
X
Wrong Assign
888
Switch epoch
Figure 3: The process of Progressive Label Assignment (PLA). Points of different colors represent those assigned to different feature pyramid levels P2, P3, P4, P5, P6 for label assignment. As training progresses, the label assignment strategy evolves. begins with using fixed Watershed regions in the early stages and transitions to leveraging dynamic, network-generater dimensions in the middle- to-late phases. This evolution guides ground truth points to be assigned to more suitable FPN levels over time.
of instances). The training objective is to output the Rotated Bounding Box (RBox) representation
[1x, v), (w, h)), 0] and the instance category [els] for each instance, where [(x; y), (w, h)) 0) denotes the precise circumscribed rectangle of the instance. During the inference phase, the input is solely the image 7, and the model outputs the RBoxes [(xr y). (w, h), 0 of all potential instances in the im- age, along with their corresponding categories [cls] and confidence scores. The core difficulty of this task lies in overcoming the extreme deficiency of scale and orientation information inherent in point annotations. Specifically, the model is required to "reconstruct" precise geometric information from scratch. This requires resolving mutual boundary interference in densely packed scenes and addressing segmentation difficulties sparse scenes caused by the scarcity of spatial constraints, while overcoming the challenge of diversity in scales.
The architectural overview of Point2RBox-v3 is presented in Figure 2. The simple evolutionary context of Point2RBox series is as follows: Point2RBox (Yu et al., 2024) established the angle pre- diction module via symmetry learning. while Point2RBox-v2 (Yu al., 2025a) introduced the scale prediction module based on spatial layout learning. The detailed explanation is in Appendix A.I. While retaining the foundational components of this strong baseline (i.e., ResNet50 (He al,, 2016) backbone, FPN (Lin et al., 2017a) head, PSC (Yu & Da, 2023) angle coder, and losses Lothers as shown in the Figure 2), Boxnt2RBox-v3 focuses on two core innovations: Progressive Label As- signment (PLA) and Prior-Guider Dynamic Mask Loss (PGDM-Loss). These modules not only reinforce scale learning but, crucially, PLA enables the utilization of dynamic scale information for framework.
multi-level label assignment within the FPN under a weakly-supervie
PLA iteratively generates and refines online pseudo bounding boxes a dynamic manner for la- bel assignment. It re-establishes the effectiveness of FPN in point-supervised learning scenarios. PGDM-Loss enhances the supervisory quality in the object scale learning task by selecting the mask-generating method between SAM and watershed. Incorporated with other components inher- ited from Box-22, our method achieves a consistent improvement (59.6% vs 51.0%) over the previous SOTA end-to-end performance. In subsequent subsections, PLA and PGDM-Loss detailed.
3.2 PROGRESSIVE LABEL ASSIGNMENT
For both anchor-fres (Tian et al., 2019) and anchor-based (Lin et al., 2017b) object detection al- gorithms, the scale information of objects plays indispensable role in label assignment. Points
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lack scale, rendering label assignment infeasible. The scarcity of scale information persists and is even magnified in the loss constraints for scale learning. In Poimt2RBox-v2 the authors employ various approaches for scale learning, including explicit methods such as copy-paste loss, Voronoi Watershed loss, and edge loss, as well as implicit techniques like layout loss. Among these, wa- tershed segmentation algorithm plays the most pivotal role in the scale learning task of the entire framework. Inspired by this, we introduce the pseudo labels originally used for scale constraints in the loss function into the label assignment module to provide approximate scale information.
We re-adopted the standard Feature Pyramid Network (FPN) architecture and the label assignment strategy. In the initial phase of model training, scale cues are obtained from watershed-generet generated pseudo labels. The computation of pseudo labels can be represented by the formula:
V Voronoi(X] (X), S Watershed 7, X, V);
(1)
(2)
PL = minAreaRect(S)),
(3)
where Voronoi (Aurenhammer, 1991) is a partitioning of a space based on a set of points; X are the annotated points within a training image; / is the input image; V are the output ridges; S are the output basin regions (pixel coordinates) corresponding to each annotated instance; minAreaRect is used to calculate the minimum enclosing rotated rectangle of point set; PL is for Pseudo Label.
Static segmentation algorithms such as watershed produce immutable segmentation regions. When such algorithms yield suhoptimal segmentation for specific samples, the resulting defects persist throughout the entire training cycle without correction. Consequently, during the mid-to-late training phases, we utilize the forward network-predicted boxes associated with each GT point supply requisite dimension information for label assignment. For each feature level in the FPN, it selects the predicted boxes from the anchor point closest to the target point the candidate boxes. The final pseudo label is then chosen from these candidate boxes based on their corresponding scores. The computation of pseudo labels can be represented by the formula:
PLy arg maxscore(b) bEC,
where PLg is the Pseudo Label for ground truth point g: C. refers to the candidate prediction boxes, selected by picking the prediction box associated with the anchor point closest to the ground-truth point g at each FPN level; function (b) is the raw classification confidence directly output by the detection network. This approach can partially mitigate label assignment issues caused by solely relying on fixed segmentation regions generated by the watershed algorithm.
The improvement the above design is verified in the Table 4 of the ablation study. Algorithm in Appendix A.3 outlines the workings of the proposed method. Figure 3 illustrates the limitations of using fixed segmentation regions, such as those generated by watershed, for label assignment scale information. It also shows how, in the later stages of training, the dynamic forward predictions of the network progressively improve label assignment accuracy.
3.3 PRIOR-GUIDED DYNAMIC MASK LOSS
Poin (Yu et al., 2025a) leverages Watershed Loss to generate pseudo labels from spatial layouts. While effective in dense scenes, this approach struggles in sparse scenarios where spatial cues are minimal, often leading over- or under-segmeenattio. detailed analysis of this limitation is provided in Appendix A.4. Conversely, the Segment Anything Model (SAM) (Kirillov et al., 2023) offers greater robustness in these challenging scenarios, but its high computational cost and weaker performance in dense scenes (Cai et al., 2024) preclude its direct application.
To harness the strengths of both methods, we propose the PGDM-Loss (Crgdm), a hybrid loss that dynamically routes images for mask generation. Images with a sparse instance count (total instances
< Nyhr) are directed to a SAM branch, while denser scenes are processed by the original Watershed branch (see Figure 4). This strategy enhances segmentation accuracy for difficult cases without significant computational overhead, preserving the efficiency of the Voronoi Watershed method in dense scenes. We choose the lightweight MobileSAM (Zhang et al., 2023) our SAM model. On DOTA-v its APso is the same as the result obtained using the basic SAM model, but its training time is 10 hours less than that of the basic one.
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Num:1
Numa3
Numu4
Num:115
Num
SAM Model
Acceptable Speed Accurate Segmentation
Slow Speed
Messy Segmentation
Instance number
sparse
dense
Fast Speed
Fast Speed Accurate Segmentation
WaterShed
Inaccurate Segmentation
Num:1
Num:3
Numo4
Nume
Num:348
Figure 4: Comparison between watershed and SAM masks on DOTA-v1.0. The red patches with yellow edges represent the masks generated by the model. The processing result in the top-right corner shows sigmificant sver-segmentation by SAM, which causes the masks to visually merge into a large, incorrect region.
integrate SAM not as simple mask proposal tool, but as a source of weak supervision. There- fore, the SAM model does not participate in the inference process at all, ensuring its fast inference speed. For an instance j class routed to SAM branch, SAM generates a set of candi- date masks, M, {m1, ......., m We select the optimal mask my via prior-guided filtering mechanism governed by a scoring function:
m = arg max M.EM,
Σ Wh,cy ØR (m4),
(5)
where Φu (m:) represents metrics calculated from the masks, and Wk.e, a class-specific weight based on simple prior knowledge (e.g.. expected shape). A full description of the five metries (center alignment, color consistency, rectangularity, circularity, and aspect ratio reliability) is available in Appendix A.5.
After obtaining masks from both branches, we compute the losses uniformly, following Poim2RBox-v2 (Yu et al., 2025a). First, the mask S rotated to align with the current predic- tion to obtain the regression targets wy and he:
We he
2 max R T (s ye
)
(6)
The width-height regression loss for single instance, which we denote as Lmass is then computed using the Gaussian Wasserstein Distance loss (LowD) (Yang et al., 2021):
Lmask LOWD ((1/2
0 h/2
ww/2
he/2
where w and the output of the detection head. The total loss, LPGDM, is the mean these individual losses over all N instances in image. We denote the loss for instance Cj and formulate the total loss as:
LPGDM
1 N
Σ Cj jEImage
(8)

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Published as a conference paper at ICLR 2026
where each Cj is computed for the corresponding instance using its optimal mask according to the formula for mask in Eq. 7.
EXPERIMENTS
4.1 EXPERIMENTAL DETAILS
Implementation Details. Experiments carried using PyTorch 1.13.1 (Paszke et al., 2019) and the rotation detection toolkit MMRotate 1.0.0 (Zhou et al., 2022). All experiments follow the same hyper-parameters We adopt Average Precision (AP) as the primary evaluation metric. All models are based on the ResNet50 (He et al., 2016) backbone and are trained using AdamW (Loshchilov & Hutter, 2018).
1. Learning rate: Initialized at 10-5, with warm-up for 500 iterations, and divided by 10 each decay step.
Epochs:
12 for datasets.
Augmentation: Random flip for all datasets.
4. Image size: Images from DOTA/STAR are split into 1,024 1,024 patches with overlap of
200 pixels; images from DIOR are scaled to 800 x 800; images from RSAR are scaled to 1024 1024.
5. Multi-scale: All experiments are evaluated without multi-scale techniques (Zhou et al., 2022).
Datasets. The experiments utilize six remote sensing datasets and one retail scene dataset, covering those used by the main counterparts:
DOTA (Xia et al., 2018). DOTA-v1.0 contains 2,806 aerial images with 15 categories. DOTA- v1.5/2.0 are its extended versions with more small objects and new categories.
DIOR (Li et al., 2020). an aerial image dataset re-annotteed with RBoxes based on its original HBox version (Cheng et al., 2022), featuring high variation in object scale and high intra-class diversity.
STAR (Li al., 2025). An extensive dataset for scene graph generation, covering over 210,000 objects with diverse spatial resolutions and 48 fine-graines categories.
RSAR (Zhang et al., 2025c). remote sensing dataset based on Synthetic Aperture Radar (SAR) imagery, containing categories.
4.2
MAIN RESULTS DOTA-V1.0
Table compares Poimt2BBxx-v3 with SOTA methods. These methods can be categorized into two tracks:
1.
End-to-end training. These methods apply the trained veakly-supervise detector directly to the test set. Our method, demonstrates an improvement of 8.61% % (59.61% vs. 51.00%). Notably, method also outperforms P2RBox (59.04%), which also uses pre-trained SAM.
2. Two-stage training. In this track. RBox pseudo labels generated the train/val sets, which are then used to train a standard FCOS detector. In this mode, Point2RBox-vt achieves an accuracy of 66.099 69%, considerably surpassing the PointOBB series. Compared to its predecessor, Point2RBox-v2 (62.61%) 61%). our method shows an improvement of 3.48%. It also outperforms the SA.--powrre P2RBox (59.04%) by 7.05%.
Class-wise Analysis. Our method demonstrates robust performance on high-density categories (SH, SV, LV, PL, ST, TC), matching the strong Point2Box--v2 baseline. More significantly, achieves notable gains on large-sized, low-density calegories-SSoccr-bball field (SBF), Bridge (BR), and where traditional puint-sueervised methods often fail infer accurate rotated
Roundabout (RA) where boxes from sparse points. This leads to ofte-oof-he--a results these challenging categories, emplified by a dramatic improvement on BR (41.6% vs. 8.0%), strong performance on RA (55.4%), and solid result SBF (44.4%).

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Published as a conference paper at ICLR 2026
Table 1: Detection performance of all categories and the mean Ao on the DOTA-v
Methods
PL
BD
BR
GTF
SV
LV
SH
TC
BC
ST
SBF
RA
HA
SP
HC
AP
RBox supervised
RepPoints (2019)
86.7
81.1
41.6
62.0
76.2
56.3
75.7
90.7
80.8
85.3
63.3
66.6
59.1
67.6
33.7
68.45
RetinaNet (2017b)
88.2
77.0
45.0
69.4
71.5
59.0
74.5
90.8
84.9
79.3
57.3
64.7
62.7
66.5
39.6
68.69
GWD (2021)
89.3
75.4
47.8
61.9
79.5
73.8
86.1
90.9
84.5
79.4
55.9
59.7
63.2
71.0
45.4
71.66
FCOS (2019)
89.1
76.9
63.2
79.8
90.4
80.8
59.7
66.3
71.3
41.7
72.44
89.2
83.0
52.5
74.6
78.8
79.2
87.5
90.9
84.9
84.8
61.9
68.0
70.7
71.4
59.8
75.81
H #Bots-opervised 00D
Sun al. (2021)
X
51.5
38.7
16.1
36.8
29.8
19.2
23.4
83.9
50.6
80.0
18.9
50.2
25.6
28.7
25.5
38.60
Roxlna-RBox (2021)3
68.4
40.8
33.1
32.3
46.9
55.4
56.6
79.5
66.8
82.1
41.2
52.8
65.0
30.0
53.59
H2RBox (2023)
88,5
73.5
40.8
56.9
77.5
65.4
77.9
90.9
83.2
85.3
55.3
62.9
52.4
63.6
43.3
67.82
EIE-Det (2024)
87.7
70.2
41.5
60.5
80.7
76.3
86.3
90.9
82.6
84.7
53.1
64.5
58.1
70.4
43.8
70.10
H2RBox-v2 (2023)
89.0
74.4
75.3
90.9
85.1
63.2
70.5
49.7
72.31
Point chont-ooperviied 00D
Point2Mask-RRoo (2023))
4.0
23.1
3.8
1.3
15.1
1.0
3.3
19.0
1.0
29.1
0.0
9.5
7.4
21.1
7.1
9.72
P28Net+H2RBox (3023)
X
24.7
35.9
7.1
27.9
3.3
12.1
17.5
17.5
0.8
34.0
6.3
49.6
11.6
27.2
18.8
19:63
P3BNet+12RBox (2023)
11.0
44.8
36.8
16.7
27.8
12.1
1.8
50.6
12.6
36.7
12.5
21.87
P2RBox (2024)
87.8
65.7
15.0
60.7
73.0
71.7
78.9
81.5
44.5
81.2
41.2
39.3
45.5
57.5
41.2
59.04
PointOBB (2024)
26.1
65.7
9.1
59.4
65.8
34.9
29.8
0.5
2.3
16.7
0.6
49.0
21.8
41.0
36.7
30.08
Poim/2RBox (2024)
62.9
64.3
38.9
33.3
25.2
2.2
48.1
45.0
22.6
34.07
Poin (2024)
53.3
63.9
40.0
39.2
45.7
76.7
10.5
56.1
5.4
49.5
51.2
33.8
40.27
Poin22Boxx (2024)
66.4
59.5
5.2
52.6
54.1
53.9
57.3
90.8
3.2
$7.8
6.1
47.4
22.9
55.7
40.5
44.90
PeiniOBB-42 (2025)
64.5
27.8
1.9
36.2
58.8
47.2
53.4
90.5
62.2
45.3
12.1
41.7
8.1
43.7
32.0
41.68
PMS-SAM-RSD
69.0
39.5
64.7
71.9
79.8
32.6
44.8
42.5
46.00
PointOBB-r3 (2025b)
30.9
39.4
13.5
22.7
61.2
7.0
43.1
62.4
59.8
47.3
2.7
45.1
16.8
55.2
11.4
41.29
PointORB-v3 (2025b)
x
52.9
54.4
21.3
52.7
65.6
44.9
67.8
87.2
26.7
73.4
32.6
53.3
39.0
56.4
10.2
49:24
Peim/2RBox-w2 (2025a)
78.4
52.7
60.5
88.7
65.5
24.4
26,1
50.7
21.0
51.00
Point2RBox-v2 (2025s)
88.0
72.6
8.0
46.2
79.6
76.3
86.9
89.1
79.7
82.9
26.2
45.3
45.8
66.3
46.3
62.61
Point2Roxxvv4PLLA (ours)
82.3
47.8
23.2
36.0
77.9
75.5
86.6
85.3
72.0
76.5
28.0
33.4
39.4
52.7
31.7
56.55
Poin(2Box:w<4PLA (ours)
88.3
71.5
26.3
45.9
78.8
76.9
87.5
86.9
74.6
83.1
47.7
45,2
49.0
60.7
46.7
64.61
PoinE2RRos.v2PP.AV A (ours)
84.6
51.5
25.4
37.2
78.7
75.3
86.5
86.4
75.8
74.9
31.3
33.6
40.2
54.5
34.2
58.00
Point2RBoxww3 (ours)
86.5
53.4
35.7
37.5
78.8
75.3
86.3
86.6
66.0
80.2
29.7
49.9
36.7
59.1
32.5
59.61
Point2RBox-v3 (ours)
89.0
72.5
41.6
45.1
79.4
76.4
87.7
85.1
75.7
84.8
44.4
55.4
45.7
59.8
48.8
66.09
CCmmuriioo trucks: /= End end training and testing: x Generating preudo labels train the FCOS detector (two stage training).
P2RBax/PMS.S SERBRR//MS.SMM.BBRR SES-SAM-RRDDAA JAM-RRSDPPini2RBo...o
Pre tre-trained SAM model; Poin2RBex+SK: One-shot sketches for each class.
PL.: Plane, BD: Buseball diamond, BR: Bridge. GTF: Ground track field, SV: Small vehicle, LV: Large vehicle, SH: Ship. TC: Tennis court.
BC: Basketball court, ST: Storage lank, SBF Socear-bll field, RA: Roundabout, HA. Harbor, SP: Swimming pool, HC: Helisopter.
PRBox: The minimum rectungle operation is performed on the output Mask to obtain the Box. RCS: CS denotes the abbreviation for Class Specific Watershed. Appendix A.6 contains detailed information on Class Specific Watershed. The hest score in bold and the second best is in underline
4.3 RESULTS ON MORE DATASETS
As shown in Table 2, Point2RBox-v3 also performs excellently on other datasets. On the more chal- lenging DOTA-v1.5/2.0 datasets, Poin shows a similar leading trend. In the end-to-end track, its APoo is 17.09% and 11.65% higher than PointOBB-v3, respectively, and it consistently out- performs its predecessor, Poin (56.86% / 41.28% vs. 54.06% / 38.79%). On the DIOR dataset, where object distribution is relatively sparse, our method achieves an AP of 46.40%. This result slightly higher than PMHO (46. 40%).. whose stage "point proposal bag mask hori- zontal box- rotated box" pipeline makes it expensive to train and susceptible to error accumulation, while evidently higher than other methods. On the fine-grained dataset STAR, our method achieves competitive APso of 19.60%. Furthermore, the SAR image dataset RSAR, Point2RBox-v3 obtains an AP of 43.96%, demonstrating its effectiveness across different imaging modalities.
4.4 ABLATION STUDIES
Tables 3-6 display the ablation studies on DOTA-v1.0. "E2E" denotes end-to-end training: "FCOS" denotes two-stage training (i.e. generating pseudo labels to train FCOS). The final values adopted are highlighted in gray.
Incremental addition of modules. Table demonstrates the impact of different modules on the final result. Adding only the PLA module increases the E2E and two-stage metrics by 5.6% and 2.0%, reaching 56.6% and 64.6%, respectively. Adding only the PGDM-Loss module enhances these metrics by 3.2% and 1.3%, achieving 54.2% and 63.9% respectively. The improvements from the two modules exhibit a high degree of orthogonality. When integrated simultancously, they yield combined gain of 8.6% and 3.5 5 on the E2E and two-stage metrics, respectively.
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Table 2: 50 comparisons on the DOTA-vL.0/1.52.000 DIOR, STAR, and RSAR datasets.
Methods
DOTA-v1.0 DOTA-v1.5
DOTA-v2.0 D0TTA---..5DOAA222 DIOR STAR
RSAR
RBox-supervised 0OD
RetinaNct (2017b)
68.69
60.57
47.00
54.96
21.80
57.67
GWD (2021)
71.66
63.27
48.87
57.60
25.30
57.80
FCOS (2019)
72.44
64.53
51.77
59.83
28.10
66.66
S-A-Net (2022)
75,81
66.53
52.39
61.41
27.30
66.47
V HBox- OOD
Sun (2021)
38,
H2RBox (2023)
70.05
61.70
48.68
57.80
17.20
49.92
H2RBox-v2 (2023)
72.31
64.76
50.33
57.64
27.30
65.16
AFWS (2024)
72.55
65.92
51.73
59.07
Point-supervised OOD
P2RBox (2024)
59.04
PMHO (2024b)
46.20
PointOBB (2024)
X
30.08
10.66
5.53
37.31
9.19
13.80
Poi (2024)
V
40.27
30.51
23.43
27.34
7.86
27.81
PointOBB-v2 (2025)
41.68
30.59
20.64
39.56
9.00
18.99
PMS-SAM-RSD (2025)
46.00
PointOBB-v3 (2025b)
41.20
31,25
22.82
37.60
11.31
15.84
PointOBB-v3 (2025b)
X
49.24
33.79
23.52
40.18
12.85
22.60
Point2RBox-v2 (2025a)
51.00
39.45
27.11
34.70
7.80
28.60
Point2RBox-v2 (2025a)
62,61
54.06
38.79
44.45
14.20
30.90
Point2RBox-v3 (ours)
59.61
48.34
34.47
41.50
14.60
40.80
Point2RBox-v3 (ours)
X
66.09
56.86
41.28
46.40
19.60
45.96
Comparison tracks: / End-to-end training and testing: x Generating pseudo labels to train the FCOS detector (two-stage training). v3: Pre-trained SAM model; Poim2RBox+SK: One-shot sketches for each class. The hest score is in bold and the second-best in underline.
Table 3: Ablation with addi- tion of modules.
Table 4: Ablation with switch
Table 5: Ablation sparse scene threshold in PGDM- Loss.
epoch of PLA.
Modules
DOTA FCOS
switch opoch
E2E
FCOS
PLA PGDM
0
56.6
64.4
Threshold
E2E
FCOS
Time
51.0
62.6
3
59.5
66.4
0
56.6
64.6
13.6h
56.6
64.6
6
59.6
66.1
59.6
66.1
19.5h
4
54.2
63.9
56.2
65.5
66.1
23.5h
66.1
12
56.3
65.3
57.2
66.
79.0h
Switch epoch of PLA. Table 4 studies the hyperparameter switch epoch of PLA. Our ablation study confirms that the phased assignment strategy crucial. The extreme methods only network predictions (switch epoch 0) only watershed boxes (switch epoch 12) for the entire training- perfoomee substantially worse than mid-training transition. The optimal switch epoch was found to be 6; we selected for subsequent experiments.
Hyperparameters of PGDM-Loss. We investigated the hyperparameter Nur- threshold on the number of target instances used to define sparse scenes. scene classified sparse its instance count is no more than Nurr As shown in Table 5, the model achieves optimal E2E performance of 59.6% Nehr
and their two-stage training performance on the FCOS platform is very close. This indicates that the model is robust to the choice of Nuhr within a reasonable range, obviating the need for meticulous tuning. Notably, setting Nthr to infinity, which directs instances to the SAM branch, quadruples the training time and significantly degrades precision. This result, consistent with our findings Figure 4, underscores the importance of our hybrid approach that applies SAM and watershed strategies to different scenes based sparsity.
is crucial to note that the reliance on prior general-purport datasets. We find that while SAM can effectively segment instances based on edge features in remote sensing scenes, its native confidence scores often fail accurately reflect the quality of the generated masks in this specific
Necessity of Prior Knowledge in PGDM-Loss.
knowledge stems from the fact that SAM is trained

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Published as a conference paper at ICLR 2026
domain. For example, in Figure 2, the Prior-Guided Selector' part shows that SAM will not tend to give the most correct answer a higher score. To validate this, we conducted an ablation study on DOTA-v1.0 as shown in Table 6. The results indicate that relying solely on SAM's native confidence scores leads to a performance degradation, with AP dropping by 1.75 and 2.5 points for the end- to-end and two-stage frameworks, respectively. This empirical evidence strongly demonstrates the superiority of our proposed method.
Table 6: Ablation with Prior Knowledge in PGDM-Loss
Table 7: APso comparison DOTA-v1.0/0
Method
DOTA-vI.0
DOTA-v1.5
Modules
E2E
FCOS
10%
20%
30%
full
10%
20%
30%
full
PWOOD
42.35
45.01
49.12
55.87
35.33
41.54
43.02
46.83
NoPsior-Loss
57.86
63.59
PGDM
12.81
17.70
51.02
57.96
37.29
42.02
44.06
49,08
PGDM-Loss
59.61
66.09
PLA
45.18
50.33
55.67
62.41
40.36
45.24
48.13
53.23
PGDD
58,02
64.57
43.25
47.05
56.18
We integrated this method into the "Partially Weakly Supervised Oriented Object Detection", i.e. PWOOD framework (Liu et al., 2025a), to demonstrate the universality and practicality of our ap- proach. The operating principle of this framework is to train the model using a small portion of weakly labeled data (such as point annotations) and a large portion of unlabeled samples. We added our modules to the training process of the PWOOD framework and conducted experiments on the DOTA-v 1.0 and DOTA-v1.5 datasets following the training process of PWOOD, setting the propor- tion of weakly labeled (point labeled) data to 10%, 20%, and 30% respectively.
As shown in Table 7, these results indicate that the approach we adopted in this partially weakly- supervised environment is successful. We conducted ablation experiments and multi-data exper- iments. In both datasets, our modular approach consistently and significantly outperformed the PWOOD baseline. For example, on the DOTA-v1.0 dataset with only 10% point-labeled data, our method increased the AP from 42.35% to 50.67%, an improvement of 8.32% The performance improvement brought by our method is also robust in cases of higher weak supervision. In the 100% point-labeled (marked as "full") scenario, our method achieved an AP50 of 64.57%, 8.7% higher than the baseline. It is notable that these improvements were also significant on the more challeng- ing D.TA-v1.5 dataset: for instance, in 10% of the scenarios, it increased from 35.33% to 43.25%, an improvement of 7.92%.
The above experiments have confirmed that our method can be applied in partially weakly- supervised scenarios, significantly improving the performance of the PWOOD framework, whether in the case of only a small amount of weak supervision or with more supervision. This provides a powerful and cost-effective solution for oriented object detection.
5 CONCLUSION
This paper introduces Point2RBox-v3, an upgraded framework that significantly improves both the quality and utilization efficiency of pseudo labels. We introduce Progressive Label Assignment (PLA), which revitalizes point-supervised FPN and unifies label assignment with fully-supervised paradigms through dynamically generated pseudo labels. To address potential limitations of Voronoi Watershed Loss object scale learning, we propose EhiieG Dynamic Mask Loss (PGDM- Loss), which incorporates Segment Anything Model (SAM) to enhance the quality of pseudo labels.
Experiments yield the following observations: 1) point-supervised tasks, both the quality and uti- lization efficiency of pseudo labels are critical factors that can yield substantial performance gains. 2) The label assignment module distributes objects to FPN layers based on their scale-specific re- ceptive fields. Due to the intrinsic spatial approximation of convolutional operations and empirically defined assignment thresholds, this approach exhibits inherent tolerance to pseudo labels imperfec- tions while maintaining detection accuracy. 3) It advances the state of the art by large amount, achieving 66.09%, 56.86% 41.28%, 46.40% 19.60% and 45.96% on the DOTA-v1.0, DOTA-v1.5, DOTA-v2.0, DIOR, STAR and RSAR respectively.
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ACKNOWLEDGMENT
This work was supported by the National Natural Science Foundation of China (62506229), Natural Science Foundation of Shanghai under 155214002266 and Shanghai Yuan Innovation Foundation.
ETHICS STATEMENT
This research adheres to the ICLR ethical guidelines and upholds the principles of responsible search. We ensure that no personally identifiable, sensitive, or harmful data were used. Our ex- periments were based on publicly available datasets and did involve any human subjects or vuinerable groups, We have considered the potential societal impact of our methods, including the risk of misuse, and believe that these contributions primarily advance scientific understanding and do not pose foreseeable harm.
REPRODUCIBILITY STATEMENT
We follow the reproducibility guidelines in the ICLR 2026 author guidelines. We will open source code, configuration files, scripts and checkpoints to reproduce our results, including dataset con- struction, model training. and evaluation, on Github as soon as possible.
REFERENCES
Franz Aurenhammer. Voronoi diagrams- survey of fundamental geometric data structure. ACM Computing Surveys, 23(3):345-400 September 1991. ISSN 0360-0300. doi: 10.1145/116873.
116880. URL https:ttp/.... /woiodoooooooooo//ooo org. 100 11
6880.
Zhi Cai, Yingjie Gao, Yaoyan Zheng, Nan Zhou, and Di Huang. Crowd-sam: Sam as smart annotator for object detection in crowded scenes. In Proceedings of the European Conference on Computer Vision (ECCV), 2024.
Guangming Cao. Xuehui Yu, Wenwen Yu, Xumeng Han, Xue Yang, Guorong Li, Jianbin Jiao, and Zhenjun Han. P2rbox: Point prompt oriented object detection with sam. arXiv preprint arXiv:2311.13128, 2023.
Gong Cheng, Jiabao Wang, Ke Li, Xingxing Xie, Chunbo Lang. Yanqing Yao, and Junwei Han. Anchor-free oriented proposal generator for object detection. IEEE Transactions on Geoscience and Remote Sensing, 2022.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition scale. arXiv preprint arXiv:2010.1 1929, 2020.
Di Feng, Christian Seasee,chütz, Lars Rosenbaum, Heinz Hertlein, Claudius Gläser, Fabian Timm, Werner Wiesbeck, and Klaus Dietmayer. Deep multi-modal object detection and semantic seg- mentation for autonomous driving: Datasets, methods, and challenges. IEEE Transactions on Intelligent Transportation Systems, 22(3): 2021. doi: 10.1109/TTTS. N0.1109/TTS.22020229799999 2020.2972974.
Kun Fu, Zhonghan Chang, Yue Zhang, Guangluan Xu, Keshu Zhang, and Xian Sun. Rotation- aware and multi-scale convolutional neural network for object detection in remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 161:294-300 308, 2020. ISSN 0924-2716. doi: https://doi.org/ 10. 101 is00.jp
Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung- Yi Lin, Ekin D. Cubuk, Quoc V. Le, and Barret Zoph. Simple popte is strong data augmentation method for instance segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2918 8-2928, June 2021.
11

---

Published as a conference paper at ICLR 2026
Eran Goldman, Roei Herzig, Aviv Eisenschtat, Jacob Goldberger, and Tal Hassner. Precise detection in densely packed scenes. In /EEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5227-5236, 2019.
Jiaming Han, Jian Ding, Jie Li, and Gui-Song& Xia. Align deep features for oriented object detection. IEEE Transactions Geoscience and Remote Sensing, 60:1-11, 2022. doi: 10.1109/TGRS.
20612406
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan- Yen Lo, et al. Segment anything. In Proceed- ings of the IEEE/CVF international conference computer vision, pp. 4015 2023.
Ke Li, Gang Wan, Gong Cheng, Liqiu Meng, and Junwei Han. Object detection in optical remote sensing images: A survey and new benchmark. ISPRS Journal of Photogranmmetry and Remote Sensing. 359:296-307, 2020.
Wentong Li, Yuqian Yuan, Song Wang. Jianke Zhu, Jianshu Li, Jian Liu, and Lei Zhang. Poi:t2mask: Pupet-sup panoptic segmentation via optimal transport. In IEEE Interna- fional Conference on Computer Vision, 2023.
Yansheng Li, Linlin Wang. Tingzhu Wang. Xue Yang, Junwei Luo, Qi Wang. Youming Deng, Wen- bin Wang, Xian Sun, Haifeng Li, et al. Star: A first-ever dataset and large-scale benchmark for scene graph generation in large-size satellite imagery. IEEE Trans. Pattern Anal. Mach. Intell., 47(3):1832-1844 1849, 2025.
Minghui Liao, Zhen Zhu, Baoguang Shi, Gui-Song Xia, and Xiang Bai. Rotation-sensitiv regres- sion for oriented scene text detection. In IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 9909-5918, 2018.
doi: 10.1109/CVPR.2011 8.0019..
Tsung- Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 21 2117-2125, 2017a.
Tsung- Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pp- 2980-2988, 2017b.
Mingxin Liu, Peiyuan Zhang, Yuan Liu, Wei Zhang, Yue Zhou, Ning Liao, Ziyang Gong, Junwei Luo, Zhirui Wang, Yi Yu, al. Partial weakly-supervee supervised oriented object detection. arXiv preprint arXiv:2507.02751, 2025a.
Nanqing Liu, Xun Xu, Yongyi Su, Haojie Zhang, and Heng-Chao Li. Pointsam: Pointly-supervised segment anything model for remote sensing images. IEEE Transactions on Geoscience and Re- mote Sensing, 2025b.
Xuebo Liu, Ding Liang, Shi Yan, Dagui Chen, Yu Qiao, and Junjie Yan. Fots: Fast oriented text spotting with a unified network. In IEEE/CVF Conference on Computer Vision and Puttern Recog- nition, pp. 5676-5687. 2018.
doi: 10.1 0t009///CC...218.005555555
Yuekai Liu, Hongli Gao, Liang Guo, Aoping Qin, Canyu Cai, and Zhichao You. A data-flow oriented deep ensemble learning method for real-time surface defect inspection. IEEE Transactiony on Instrumentation and Measurement, 69(7):4681-469) 2020. doi: 10. 109/TTM.2019.99577
Zikun Liu, Liu Yuan, Lubin Weng, and Yiping Yang. A high resolution optical satellite image dataset for ship recognition and some new baselines. In International conference on pattern recognition applications and methods, volume 2, pp. 324-331. SciTePress, 2017.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Confer- ence on Learning Representations, 2018.
12

---

Published as a conference paper at ICLR 2026
Junyan Lu, Qinglei Hu, Ruifei Zhu, Yali Wei, and Tie Li. Afws: Angle-free weakly-superviee tating object detection for remote sensing images. IEEE Transactions on Geoscience and Remote Sensing. 2024.
Xuran Lu and Zhisong Bie. Semantic segmentation everything model for point-prompted oriented object detection. Electronics Letters, 61(1):470254 2025.
Junwei Luo, Xue Yang, Yi Yu, Qingyun Li, Junchi Yan, and Yansheng Li. Pointobb: Learning ori- ented object detection via single point supervision. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 16730-16740, 2024.
Xingjia Pan, Yuqiang Ren, Kekai Sheng, Weiming Dong, Haolei Yuan, Xiaowei Guo, Chongyang Ma, and Changsheng Xu. Dynamic refinement network for oriented and densely packed object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11207- 11216, 2020.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang. Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems, volume 32, pp- 8024-8033 2019.
Botao Ren, Xue Yang, Yi Yu, Junwei Luo, and Zhidong Deng. Pointobb-v2: Towards simpler, faster, and stronger single point supervised oriented object detection. In International Conference on Learning Representations, 2025.
Yongqing Sun, Jie Ran, Feng Yang, Chenqiang Gao, Takayuki Kurozumi, Hideaki Kimata, and Ziqi Ye. Oriented object detection for remote sensing images based on weakly supervised learning. In IEEE International Conference Multimedia & Expo Workshops, pp. 1-6, 2021.
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. Feos: Fully convolutional one-stage object detection. In IEEE/CVF International Conference on Computer Vision, pp. 9626-9635, 2019.
doi: 10. 10.1109/CCCV2201900009
Zhi Tian, Chunhua Shen, Xinlong Wang, and Hao Chen. Boxinst: High High-performance instance segmentation with box annotations. In IEEE/CVF Conference Computer Vision and Patlern Recognition, pp. 5443-5452, 2021.
Linfei Wang, Yibing Zhan, Xu Lin, Baosheng Yu, Liang Ding, Jianqing Zhu, and Dapeng Tao. rotated object detection.
Explicit and implicit box equivariance learning for IEEE Transactions Emerging Topics in Computational Intelligence, 2024.
Hongjin Wu, Ruoshan Lei, and Yibing Peng. Pebnet: A lightweight convolutional neural network for defect inspection in surface mount technology. IEEE Transactions on Instrumentation and Measurement, 71:1-14, 2022. doi: 00.10097TM..2022.31399
Gui-Song Xia, Xiang Bai, Jian Ding, Zhen Zhu, Serge Belongic, Jiebo Luo, Mihai Datcu, Marcello Pelillo, and Liangpei Zhang. Dota: A large-scale dataset for object detection in aerial images. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 3974-3983, 2018.
Xue Yang and Junchi Yan. On the arbitrary-oriented object detection: Classification based ap- proaches revisited. International Journal of Computer Vision, 130(5))1340-1333)) 2022.
Xue Yang, Hao Sun, Kun Fu, Jirui Yang, Xian Sun, Menglong Yan, and Zhi Guo. Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks. Remote sensing, 10(1):132, 2018.
Xue Yang, Junchi Yan, Ming Qi, Wentao Wang, Xiaopeng Zhang, and Tian Qi. Rethinking rotated object detection with gaussian wasserstein distance loss. In 38th International Conference on Machine Learning, volume 139, pp. 11830-1184) 2021.
13

---

Published as a conference paper at ICLR 2026
Xue Yang, Gefan Zhang, Wentong Li, Xuehui Wang. Yue Zhou, and Junchi Yan. H2rbox: Horizontal box annotation is all you need for oriented object detection. In International Conference on Learning Representations, 2023.
Ze Yang. Shaohui Liu, Han Hu, Liwei Wang, and Stephen Lin. Reppoints: Point representation for object detection. In IEFF/CVF International Conference on Computer Vision, pp. 9656-9665, 2019. doi: 10.1109/CCC.2209.00009
Yi Yu and Feipeng Da. Phase-shifting coder: Predicting accurate orientation in oriented object detection. In IEEE/CVT Conference on Computer Vision and Puttern Recognition, 2023.
Yi Yu, Xue Yang, Qingyun Li, Yue Zhou, Feipeng Da, and Junchi Yan. H2rbox-v2: Incorporating symmetry for boosting horizontal box supervised oriented object detection. In Advances in Neural Information Processing Systems, 2023.
Yi Yu, Xue Yang, Qingyun Li, Feipeng Da, Jifeng Dai, Yu Qiao, and Junchi Yan. Point2rbox: Combine knowledge from synthetic visual patterns for end-to-end oriented object detection with single point supervision. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16783-16793, 2024.
Yi Yu, Botao Ren, Peiyuan Zhang, Mingxin Liu, Junwei Luo, Shaofeng Zhang, Feipeng Da Junchi Yan, and Xue Yang. Point2rbox-v2: Rethinking point-supervised oriented object detection with spatial layout among instances. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 19283-19295 2025a.
Yi Yu, Xue Yang, Yansheng Li, Zhenjun Han, Feipeng Da, and Junchi Yan. Wholly-wood: Wholly leveraging diversified-quality labels for weakly-supervised oriented object detection. JEEE Trans- actions Pattern Analysis and Machine Intelligence, 2025b.
Chaoning Zhang, Dongshen Han, Yu Qiao, Jung Uk Kim, Sung- Ho Bae, Seungkyu Lee, and Choong Seon Hong. Faster segment anything: Towards lightweight sam for mobile applications. arXiv preprint arXiv:2306. 14289, 2023.
Peiyuan Zhang, Junwei Luo, Xue Yang, Yi Yu, Qingyun Li, Yue Zhou, Xiaosong Jia, Xudong Lu, Jingdong Chen, Xiang Li, et al. Pointobb-v3: Expanding performance boundaries of single point- supervised oriented object detection. International Journal of Computer Vision, pp. 1-21, 2025a.
Peiyuan Zhang, Junwei Luo, Xue Yang, Yi Yu, Qingyun Li, Yue Zhou, Xiaosong Jia, Xudong Lu, Jingdong Chen, Xiang Li, et al. Pointobb-v3: Expanding performance boundaries of single point- supervised oriented object detection. arXiv preprint arXiv:2501.13898, 2025b.
Shun Zhang, Jihui Long, Yaohui Xu, and Shaohui Mei. Pmho: Point-supervised oriented object detection based on eegmentatoon-rriver proposal generation. IEEE Transactions on Geoscience and Remote Sensing, 62:1-18, 2024a. doi: 20211099/
Shun Zhang, Jihui Long, Yaohui Xu, and Shaohui Mei. Pmho: Point-supervised oriented object detection based on segmentation-driven proposal generation. IEEE Transactions on Geoscience and Remote Sensing, 2024b.
Xin Zhang, Xue Yang, Yuxuan Li, Jian Yang, Ming-Ming Cheng, and Xiang Li. Rsar: Restricted state angle resolver and rotated sar benchmark. arXiv preprint MxXv:2501.04444 2025c.
Xinyu Zhou, Cong Yao. He Wen. Yuzhi Wang, Shuchang Zhou, Weiran He, and Jiajun Liang, East: An efficient and accurate scene text detector. IEEE Conference on Computer Vision and Pattern Recognition, pp. 2642-2651, 2017. doi: 10. 11110///VP.220...222
Yue Zhou, Xue Yang, Gefan Zhang, Jiabao Wang, Yanyi Liu, Liping Hou, Xue Jiang, Xingzhao Liu, Junchi Yan, Chengqi Lyu, et al. Mmrotate: rotated object detection benchmark using pytorch. In 30th ACM International Conference on Multimedia, pp. 7331-7334, 2022.
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A APPENDIX
A.1 THE RELATIONSHIP AMONG POINT2RBOX SERIES
The evolution of the Point2RBox series represents progressive exploration into resolving the scale ambiguity inherent in point-supervised oriented object detection (OOD). The initial Point2RBox (Yu et al., 2024) pioneered this series by introducing "knowledge combination" paradigm. It uti- lized overlaid synthetic visual patterns with known geometries to provide proxy regression supervi- sion, while simultaneously incorporating symmetry-aware learning to exploit the inherent geo- metric symmetry prevalent in made structures for self-supervision, thereby circumventing the lack of size information. Building upon this, Point2RBox-v2 (Yu et al., 2025a) shifted the focus towards the intrinsic spatial layout among instances. It proposed Voronoi Watershed and Gaussian Overlap losses to constrain object extent by exploiting mutual spatial exclusivity, yet it exhibited lim- itations in sparse scenarios where such spatial constraints are weak. In this work, Point2RBox-v2 addresses these deficiencies. It introduces Progressive Label Assignment (PLA) to restore the effi- cacy of multi-scale Feature Pyramid Networks (FPN) often neglected in point-supervised settings, and incorporates Prior-Guided Dynamic Mask (PGDM) strategy that synergizes the robustness of SAM with the efficiency of watershed algorithms, ensuring consistent high performance across both sparse and dense scenes.
A.2 LOss FUNCTIONS INHERITED FROM POINT2RBOX-v2
In our Point2RBox-v3, we incorporate several loss functions that were originally proposed in Pox-2RBox-v2 (Yu al., 2025a). These methods are not part of our novel contributions but are utilized to maintain a strong baseline performance. We group them under single loss term, Coters which is a weighted sum of the following components.
A.2.1 GAUSSIAN OVERLAP Loss (Co)
This loss function is designed to enforce mutual exclusivity among instances, which particularly effective in densely packed scenes. As detailed in Point2RBox-v2, it operates by representing each oriented object as 2D Gaussian distribution, (n,,, Σ), and then minimizing the spatial overlap between these distributions. The overlap between any two distributions, N₁ and N₁. is quantified using the Bhattacharyya coefficient (Yang et al., 2021). For a given image with N instances, an overlap matrix M E R N is constructed, where each element Mi, represents the coefficient between instance and j. The Gaussian overlap loss then formulated as the sum of all off-diagonal elements, encouraging the detector to produce more compact and non-overlapping predictions:
Lo =
Ma.j.
N
A.2.2 EDGE LOSS (Cæ)
The Edge Loss employed to refine the predicted bounding boxes by aligning their boundaries pre- cisely with the object edges visible in the image. The process begins by applying an edge detection filter to the input image. For each predicted rotated bounding box (RBox), a corresponding fea- ture region is extracted from the resulting edge map via Rotated Rol Align. By analyzing the edge intensity distribution within this region, new, more accurate regression targets for the box's 's width (wr) and height (h₂) are computed. The loss is then calculated using smooth I,, loss function, which penalizes the deviation between the predicted dimensions (wr, h) and the edge-aligned targets (we, hx):
Le = smooth ([w, 1t], [we, he]).
(10)
A.2.3 COPY-PASTE AUGMENTATION (Loox)
To enhance the model's robustness and generalization, especially in varied contexts, we utilize the copy-paste data augmentation strategy (Ghiasi et al., 2021), following its implementation in Point2BBox-v2. During the training process, object instances detected in a preceding step (e.g., step k)) are cropped and subsequently pasted onto the training images for the current step (k 1). The
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Table Detailed Configuration of Point2RBox-v3 Loss Functions. All of them (except PGDM-
Loss) are inherited from Point2RBox-v2.
Loss Name
Symbol
Weight (A)
Function Type
Classification Loss
Lecls
1.0
Focal Loss
Regression Loss
Lobox
5.0
GDLoss (GWD)
Gaussian Overlap Loss
Coverlap
10.0
GaussianOverlapl..
Edge Loss
Labon.edg
0.3
EdgeLoss
Consistency Loss
Lss
1.0
PGDM-Loss
Epgdm
5.0
VoronoiWWtteshhedLoos
bounding boxes of these pasted instances serve additional ground-truth targets. The associated regression loss, which we denote as Lbox and include in our Lothers term, is calculated using the Gaussian Wasserstein Distance Loss (Yang et al., 2021).
A.2.4 SELF-SUPERVISED CONSISTENCY Loss (Css)
To further regularize the model in supf-supe manner, adopt the consistency loss introduced in Point2RBox-v2. This loss enforces prediction consistency between an original training image and its augmented counterpart (e.g., random rotation, flip, scaling). Specifically, given image and its transformed view /ug. the detector outputs two sets of Gaussian representations and rotation angles:
E 8) fun (Eaug Oaug) fun (Faug).
The consistency loss is then computed as the sum of two terms: Gaussian Wasserstein Distance loss that measures the discrepancy between the covariance matrices, and an angular regression loss that aligns the rotation predictions:
L. Low .Euu) LANG R. 0
(11)
where is the transformation matrix applied to I. R is the rotation angle of augmentation, and m E [1,-1} accounts for symmetry in flips. This self-supervised term encourages the detec- tor to capture consistent scale and orientation variations under different geometric transformations, thereby improving robustness.
A.3 PROGRESSIVE LABEL ASSIGNMENT (PLA)
Algorithm I delineates the detailed workflow of the PLA algorithm throughout the entire model training cycle.
A.4 ANALYSIS VORONOL WATERSHED IN SPARSE SCENARIOS
The Voronoi Watershed method in Point2RBox-v2 (Yu al., 2025a) constructs Voronoi diagram. The annotation point of each object serves as a foreground marker for the watershed algorithm, and the background markers are defined by the Voronoi cell boundaries and regions where the fixed-size Gaussian distribution around the annotation point falls below certain probability threshold. The Voronoi cells and Gaussian distributions are deeply related to the spatial layout of the objects.
This approach has critical limitation in sparse scenes with minimal spatial layout information. The fixed size of the Gaussian distribution can lead improperly sized foreground or background regions. This imbalance often causes the watershed algorithm to produce segmentation emror,, in- cluding over-segmentation or segdemttttttnniionn.. In contrast, the Segment Anything Model (SAM) (Kirillov et al., 2023) focuses primarily object edges, textures, and other semantic visual cues. This makes it less dependent on the spatial layout of instances and allows to produce more reliable segmentations in such challenging, sparse scenarios.
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Algorithm Progressive Label Assignment (PLA)
Input:
e is the number of current training epoch
E, is the number of switch epoch
G' is set of ground-truth points on the image
V are the output Voronoi ridges (pixel coordinates)
I is the input image
are the basin regions (pixel coordinates) corresponding to each annotated instance
PL is set of pseudo labels the image
the number of feature pyramid levels
A is set of all anchor points
A, is set of anchor points on the i-th pyramid levels
a, is anchor point of A₁
Output:
P is set of positive samples
N is a set of negative samples
1: if E, then Voronoi (G)
2:
3:
S Watershed (I, G, V)
4: PL minAreaReet (S)
5:
P, N StandardLabelAssipn (PL, A)
6:
else
7:
PT 2
8:
for each G do
9:
build an empty set for candidate pseudo label of g: C for each level E [1, L] do
10:
5: - Select the prediction box with the closest a, to g as the candidate
12:
C U
13:
end for
14:
PL arg maxbEc, score(b)
15:
PL PL=PLLPLL
16:
end for
17: P. NN= Standard abelAssin (PL., A)
18: end if
A.5 DETAILED FORMULATION OF MASK SELECTION INDICATORS
To select the optimal mask from the candidates generated by SAM, we use five indicators score each mask based on simple prior knowledge provided by the user. This prior selection process is designed to be straightforward: the user only needs make simple judgment about the target's general shape (e.g., more rectangular or circular) and the rough range of its aspect ratio, guided by example images from the dataset. The five indicators center alignment, color consistency, rectangularity, circularity, and aspect ratio reliability. The setting of Wk.c, is as follows. When an object class has significant corresponding geometric feature, the weight If the geometric feature is unimportant or ambiguous, it is set to 0. If the class does not possess the feature but is prone being misinterpreted by part that does, the weight set to negative value. For example, the weight for circularity for basketball court is set to a negative value to penalize segmentations that only capture the center circle.
a positive value.
Briefly, center alignment, inspired by P2RBox al,,, 2023), prioritizes masks whose centroids are close to the prompt point. Color consistency favors masks with a uniform color distribution, calculated based on the standard deviation of pixel values within the mask. Rectangularity and cir- cularity measure how closely mask's 's shape resembles a standard rectangle or circle, respectively. Finally, aspect ratio reliability ensures the mask's 's aspect ratio falls within plausible, user-defined range. The mathematical formulation for each indicator is designed to be simple and computation- ally efficient.
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1) Center alignment. This metric first checks if the prompt point is inside the mask's minimum area bounding rectangle. If not, the mask is heavily penalized. it is inside, the score Salign is calculated based on the distance d between the prompt point and the center of the rectangle using Gaussian function.
d²
Salign = exp
(12)
20
where o is scaling factor proportional to the diagonal of the image, ensuring that smaller distance yields a higher score.
2) Color consistency. This metric evaluates color uniformity by calculating the weighted standard deviation of pixel values within the mask, giving higher weight to pixels near the prompt point. The final score Scotor is mapped from the average weighted standard deviation o. using an exponential decay function:
Scotor exp
(13)
where À is an empirical scaling constant. lower deviation (more uniform color) results in a score closer to 1.
3) Rectangularity and Circularity. These metrics quantify how closely the mask's 's shape resembles rectangle circle. They are defined by the following ratios:
Amask
Amask
Sreet
Seire
(14)
Amabr
Amoc
where Amask is the mask area, while Amabr and Amec are the areas of the minimum area bounding rectangle and the minimum circumscribed circle, respectively. To handle shapes near the edge of the image, the areas Amabr and Amar are calculated from the parts of the shapes that lie within the image boundaries, that is, the part beyond the image boundaries is cropped and discarded.
4) Aspect ratio reliability. This metric checks if the aspect ratio of the mask's minimum area max(w.A) is within a predefined range [Rmin Rmaa]. If AR is within bounding rectangle, AR minlway)
this range, the score is 1. If it falls outside, the score Sar is calculated based on its deviation D from the range:
=xp( D).
(15)
where l is decay coefficient and the deviation D is defined as (Rmsn/AR- AR Rmin or (AR/ Rezzz - 1) if AR > Rmaz- We found that for all non-extremely long-to-wide ratio categories the remote sensing field, their long-to-wide ratio ranges were within the interval [1, 5J, while the predicted incorrect masks were generally outside this range. For example, the fuselage of plane (excluding the wings). Therefore, in order to enhance the usability of the model, we always set [Rmin Rmoz] to be [1, 5].
A.6 WATERSHED ALGORITHM VS CLASS-SPECIRE WATERSHED ALGORITHM
The quality of masks is critical for point-supervised learning. We observe that the mask quality produced by the Watershed algorithm severely degrades when processing overlapping objects, as illustrated in the second row of Figure 5. Motivated by this observation, we introduce a simple yet effective trick, termed class-specific Watershed. The key idea is to construct Voronoi diagrams and perform the Watershed algorithm on a per-class basis, rather than applying the algorithm indiscrim- inately all objects in the image. Taking the ground track field (GTF) and soccer ball field (SBF) categories, which suffer from the most severe overlaps, as examples, the third and fourth rows of Figure visually demonstrate the effectiveness of our class-specific Watershed. The results show a noticeable improvement in the mask quality for overlapping objects. This visual gain is corrob- orated by quantitative metrics; as indicated in Table (see Doin2RBox-V24PLAHCSS this trick brings an overall performance gain of 1.45% (56.55% vs. 58.00%). Notably, the proposed trick not only ameliorates the segmentation of overlapping objects but also enhances the model's 's overall performance. Class-specific Watershed and PGDM share a similar functional role. They can be con- sidered as complementary strategies, and the optimal choice depends on the specific characteristics of the application scenario.
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overlap cases
native watershed
class-specine specific: gtf
class- specifee: shf
Figure 5: Efficacy of Class-Specific Watershed on Overlapping Instances. The top row shows chal- lenging cases with overlapping objects. The standard Watershed algorithm produces unsatisfactory masks under these conditions (second row). Our class-specific Watershed (third and fourth rows) effectively mitigates this issue, leading to noticeable improvement in mask separation and quality.
A.7 LIMITATION
The primary advantage of our Progressive Label Assignment (PLA) module-its adaptability to large scale variations-also defines its main limitation. The performance gain offered by PLA might be less pronounced on datasets where object scales relatively uniform, its benefits are most apparent in multi-scale scenarios. While introducing inductive biases like PLA into point-supervised task is effective, we believe that more fundamental advancements will likely stem from holistic architectural upgrades and more inventive loss function designs. Such directions could potentially elicit richer learning signals from extremely sparse annotations.
Furthermore, while our method alleviates the shortcomings of Point2RBox-v2 in sparse scenes by imposing spatial constraints, its performance ultimately still limited by the characteristics of the existing SAM model. Since SAM is significantly more sensitive to color rather than texture and edges, the effectiveness rounding environment or have unclear boundaries (such as basketball courts and ground track fields, see table 1), which led to its performance some categories being even worse than that of previous models. In the future, researchers consider enriching the prompts for SAM, such introducing
our method is limited in those sparse instances that blend with the sur-
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SAM model
Watershed
Figure 6: The situation where both splitting methods fail on DIOR dataset. The reasons for the failure of the segmentation of the four images from left right are as follows: excessive internal texture, unclear boundaries, overly thin edge shapes, and distorted shapes.
Failed case (angle)
Failed case (angle)
Failed case (scale)
Failed case (scale)
Figure 7: Qualitative analysis on failed cases. Point2BBxx-v3 still exhibits errors in angle prediction for objects in complex scenarios (e.g., harbor targets) as well as for single objects; meanwhile, boundary detection remains challenging for low-resolution or texture-complex objects.
edge-aware algorithms to generate rough mask prompts, thereby improving performance in these challenging scenarios.
cases where both SAM and watershed fail. In Figure 6, present some examples that failed both methods. The detailed analysis is as follows: as segmentation model/algorithm, SAM and Watershed encounter certain bottlenecks, which mainly attributed to the complexity of the spectral characteristics and the diversity of the geometric structures of the ob- jects in the remote sensing scene. For example, the textures of the objects introduce high-frequency gradient noise, leading to over-segmentation under- segiee-teetmtaiinn; the unclear boundaries weaken the robustness of edge perception, causing mask overflow or positioning deviation; and overly thin edge shapes and distorted geometric forms challenge the model's ability to maintain the integrity non-convex geometries, ultimately resulting in topological breaks or mor- phological distortions in the segmentation results. Future work could consider improving the SAM model to accommodate these circumstances, or introduce other constraints to solve this problem.
In addition, study
of slender topologies
Finally, we examine cases where our method fails. The qualitative analysis the failed cases is shown in Figure 1) Angle. Angle prediction for objects in complex environments like harbor, as well for single object, still has room for improvement. 2) Scale. Although significant progress has been made, boundary delineation continues to face challenges in low-resolution contexts and with complex-texturee objects.
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Table 9: Granularly Ablation on Sparse Scene Threshold in PGDM-Loss on DOTA-v1.0.
Threshold
0
2
4
6
8
E2E FCOS
56.6
58.6
58.6
58.7
59.6
59.6
60.1
59.8
58.9
56.6
64.6
65.1
65.2
65.2
66.1
65.9
66.3
65.5
66.1
64.6
Table 10: Accuracy (AP50) comparisons non-remote-sensing domains.
Methods
OCDPCB
DIATOM
Pox-22RBox-v2
36.4
69.0
Point2RBox-v3
40.9
77.6
A.8 Nor: DATASET GENERALITY AND PARAMETER SENSITIVITY
As shown in Table 9, more granular ablation study reveals that the model metrics are not highly sensitive to the threshold, exhibiting no drastic fluctuations with its variation. Based on the result, recommend an optimal range of 4 to 6. In practice, threshold of 4 is adopted for all other datasets, with the exception of the RSAR dataset, where it is set to 6.
A.9 GENERALIZABILITY TO NON-REMONE REMOTE-SENSING SENSING DOMAINS
OCDPCB OCDPCB is dataset for oriented component detection in printed circuit boards aimed at automated optical inspection. The dataset consists of 636 images, of which 445 images are used for training and 191 for testing. The resolution of the images 1280 1280.
Diatoms are group of algae found in oceans, freshwater, moist soils, and surfaces. The dataset consists of 2197 images, of which 1758 images are used for training and 439 for testing. The resolution of the images is 2112 x 1584.
DIATOM
As shown in Table 10, the superiority of Point2RBox-v3 generalizes to non-remote sensing scenar- ios. Specifically. it outperforms Pox-t2RBox-v2 by margin of 4.5% on the OCDPCB dataset and 8.6% the DIATOM dataset.
THE USE OF LARGE LANGUAGE MODELS (LLMs)
In this study, the ideas, analysis, and conclusions presented the sole product of the authors' origi- nal thought and research. We utilized large language models (L.LMs) to accomplish several auxiliary tasks in order enhance the presentation quality and clarity of our manuscripts. Specifically, LLMs were employed for:
Table Generation: Create tables to reduce manual formatting workload.
Text Optimization: Simplify and refine sentence structures make the expression clearer.
Consistency Check: Ensure uniformity in terms and style across all sections.
All the content generated by the large language model is carefully reviewed and verified by the author to ensure the technical accuracy and compliance with our scientific contributions.
hhttps:///bi.org// 34740 wttpa/////ggg////////aa///aaaae/ 11175/99 187591.
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Reviews:
[{"review_id": "ZJPQDljoRQ", "reviewer": "Reviewer_1W1D", "summary": "This paper builds upon Point2RBox-v2 with incremental improvements. First, it generates more pseudo-label candidates by leveraging features from different levels of the network. Second, it addresses the limitations of the watershed loss in Point2RBox-v2 for simple scenes by incorporating SAM.", "questions": "1. What is the definition of the $score$ function in Eq. (4)?\n2. I don't understand how to dynamically select which mask (SAM or Watershed) to use as the basis for pseudo-labeling. Based on earlier statements in the paper, it seems that the method determines the number of instances in each scene and uses that as the criterion. However, I couldn't find a corresponding step in the actual implementation described in the paper.\n3. I'm curious about the proportion of each layer serving as the most likely pseudo-label in Eq. (4). Does the final layer play the most critical role? Because in your two examples, the final layer's pseudo-labels appear to be the most accurate.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The proposed method significantly improves the performance of point-supervised oriented object detection.\nThe figures in this paper are clear.", "weaknesses": "Unfriendly to non-experts and new readers, as the author likely assumes familiarity with previous versions of Point2RBox.\n\nRecommend formally defining tasks. \n\nProvide necessary explanations for letters and functions appearing in formulas, e.g., $I$ and $minAreaRect$.\n\nLine 214: gt should be capitalized.\n\nLine 301: Remove equation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "b5Z3laZcGS", "reviewer": "Reviewer_fDqa", "summary": "This work improves upon the baseline **Point2RBox-v2** by proposing a well-designed framework featuring two innovative components, achieving a remarkable enhancement in detection accuracy and representing a substantial advancement in the domain of point-supervised OOD detection. The proposed **Progressive Label Assignment (PLA)** effectively restores the multi-level feature utilization capability of FPN, thereby compensating for the critical performance gap caused by rigid and inflexible label assignment strategies in point-supervised methods. In addition, the **Prior-Guided Dynamic Mask Loss (PGDM-Loss)** elegantly integrates the robustness of SAM in sparse scenes with the efficiency of the Watershed algorithm in dense scenes, significantly improving pseudo-label quality. Extensive experiments demonstrate the superiority of this approach on six major aerial image datasets, outperforming existing state-of-the-art (SOTA) models.", "questions": "1. Does the performance improvement of the new method lack stability or have bias, especially in certain challenging categories?\n\n2. The \"Class-Specific Watershed\" technique introduced by you in the appendix aims to enhance the mask quality of overlapping objects (such as GTF and SBF), and brings a 1.45% performance improvement. Is this technique a necessary patch for the model when dealing with overlapping scenarios? Why didn't you integrate it into the official version of the model?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. **Clear motivation and precise problem targeting.**\n\n   Point-supervised object detection suffers from the absence of scale information, leading to suboptimal accuracy in mainstream methods. This paper insightfully identifies the inefficiency and poor quality of pseudo-label utilization in existing approaches and proposes a clear and elegant solution. The introduction of PLA enables multi-level label assignment under point supervision through FPN, effectively narrowing the performance gap between point supervision and full or box-level supervision in OOD tasks. Meanwhile, PGDM-Loss achieves an effective balance between accuracy and efficiency by dynamically selecting between the SAM model and the Voronoi-Watershed algorithm.\n\n\n\n2. **Superior performance surpassing current SOTA models.**\n\n   The proposed model achieves state-of-the-art results across six popular benchmark datasets (DOTA-v1.0/1.5/2.0, DIOR, STAR, RSAR), demonstrating strong generalization capability and practical impact across diverse datasets and imaging modalities, including SAR imagery.\n\n\n\n3. **Methodological transferability across paradigms.**\n\n   The paper successfully extends the method to a *partially weakly supervised learning* setting (PWOOD framework), achieving consistent and significant performance gains under various ratios of weak supervision. This highlights the modular utility and scalability of the PLA and PGDM components beyond pure point-supervised tasks.", "weaknesses": "1. In the performance comparison of categories shown in Table 1, the performance improvement of some categories over the baseline Point2RBox-v2 is not significant, and even decreases in some cases. For example, for BC (basketball court): 79.7 -> 75.7.\n\n2. The authors mentioned using the masks of the SAM Model as pseudo labels to measure the loss. However, training with SAM on the entire image dataset incurs huge computational costs, especially in scenarios such as remote sensing where there may be a dense scene with many instances.\n\n3. Although ( L_{\\text{others}} ) is inherited from the baseline model, its specific weight configuration should not be omitted, as this omission may hinder readers' understanding and reproducibility of the model.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Tdq7NhJrxF", "reviewer": "Reviewer_gitE", "summary": "This paper proposes Point2RBox-v3, a weakly supervised oriented object detection framework that learns from point annotations through two key modules: Progressive Label Assignment (PLA) for dynamic FPN-level label assignment, and Prior-Guided Dynamic Mask Loss (PGDM-Loss) that combines SAM and watershed masks according to scene density. The approach effectively improves the quality and utilization of pseudo labels, achieving state-of-the-art results across multiple datasets. The writing is clear and easy to follow, and the experimental evidence is solid, though some design motivations and ablations could be further clarified.", "questions": "1. Can the authors provide a simple fusion baseline of SAM and watershed to isolate the benefit of the prior-guided design?\n\n2. Do PLA and PGDM work independently, or is there performance redundancy between them?\n\n3. Is the same N_thr value used for all datasets, and how sensitive is the model to it?\n\n4. Could the method generalize to non-remote-sensing domains, or are there assumptions limiting its applicability?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.Writing is clear and logically organized, making the method easy to follow.\n\n2.Proposed PGDM-Loss reasonably integrates SAM and watershed, improving pseudo-label quality.\n\n3.PLA design enhances FPN label assignment and complements PGDM-Loss effectively.\n\n4.The method achieves strong SOTA performance across six benchmark datasets.\n\n5.Ablation studies are complete and show consistent improvements from each module.", "weaknesses": "1.No ablation comparing PGDM with a simple SAM+watershed fusion baseline.\n\n2.The necessity of PLA is not fully clarified when PGDM already refines pseudo labels.\n\n3.The generality of the hyperparameter N_thr across datasets remains uncertain.\n\n4.No analysis or visualization of failure cases where both SAM and watershed fail.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7KpoRHL4nD", "reviewer": "Reviewer_4iiD", "summary": "Due to its potential to replace the time-consuming and laborious manual annotation, the learning method based on point annotation under weak supervision framework has published several related work in high-quality academic conferences or magazines in the past three years, but its indicators are still far from fully supervised oriented object detection. This paper insightfully puts forward that there are two improvements in the existing scheme: more effective label assign and more refined scale loss constraint, and tries to solve it. Two components, PLA and PGDM, are proposed. The PLA component innovatively uses the scale information of the pseudo label generated in the detection pipeline for the label assignment; PGDM component adopts Sam and watershed segmentation algorithms with different characteristics, and observes their advantages in sparse scenes and dense scenes, respectively. The combined use provides a more refined scale loss constraint. This scheme shows a considerable increase in indicators on six test sets (dota-v1.0/1.5/2.0, dior/star/rsar) compared with the previous SOTA baseline Point2RBox-v2. The effectiveness of the scheme is also shown by moving it to partial weakly-supervised tasks.", "questions": "Question:\n1. Is the Class-Specific Watered trick first proposed in this paper? Will there be hyper parameters or other prior information? Can its pseudo code or core code be provided? How much more training time will class specific watered take than watered?", "limitations": "", "rating": 10, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "Strengths:\n1. Effectiveness: on the relatively strong baseline Point2RBox-v2, there is still a big improvement: taking DOTA-v1.0 as an example, the end-2-end mode is improved by 8.61 points, and the two stage mode is improved by 3.48 points;\nCompared with the fully supervised SOTA index of 75.81, gap has been reduced to less than 10 points for the first time.\n2. Generalization: SOTA has been achieved not only on DOTA series datasets (DOTA-v1.0/1.5/2.0), but also on DIOR/STAR/RSAR datasets; In addition to the point supervision task, the effect is also verified on partial weakly-supervised tasks.\n3. Easy to follow: it clearly points out two classic applicable scenarios: the target size changes significantly and the sparse target scenario, which is refined and consistent with intuitive cognition and easy for readers to follow.\n4. Unified paradigm: based on the verification of the effectiveness of PLA module, it can be predicted that the model paradigms of point supervision, rectangular box supervision and rotating rectangular box supervision tasks will be unified in the future.\n5. The logic forms a closed loop: the source of motivation, the design of the method, quantitative indicators, the presentation of ablation experiments, and the comparison of visual images before and after optimization. The logic forms a closed loop self consistently.", "weaknesses": "Weekness:\n1. Although point2rbox-v3 has improved significantly compared with baseline, there is still a certain gap in indicators from the fully supervised scheme.\nAs for the case that the scheme fails to deal with, the paper had better involve this aspect.\n2. When displaying the indicators in Tables 1 and 2, bold indicators and underlined indicators should represent Top1 indicators and top2 indicators respectively. However, there is no description in caption.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)