CVGL: Causal Learning and Geometric Topology
Authors: Songsong Ouyang
Yingying Zhu
Venue: NeurIPS 2025 poster ()
Keywords: cross-view
casual learning
BEV
Primary area: deep_learning

Abstract: Cross-view geo-localization (CVGL) aims to estimate the geographic location of a street image by matching it with a corresponding aerial image. This is critical for autonomous navigation and mapping in complex real-world scenarios. However, the task remains challenging due to significant viewpoint differences and the influence of confounding factors. To tackle these issues, we propose the Causal Learning and Geometric Topology (CLGT) framework, which integrates two key components: a Causal Feature Extractor (CFE) that mitigates the influence of confounding factors by leveraging causal intervention to encourage the model to focus on stable, task-relevant semantics; and a Geometric Topology Fusion (GT Fusion) module that injects Bird’s Eye View (BEV) road topology into street features to alleviate cross-view inconsistencies caused by extreme perspective changes. Additionally, we introduce a Data-Adaptive Pooling (DA Pooling) module to enhance the representation of semantically rich regions. Extensive experiments on CVUSA, CVACT, and their robustness-enhanced variants (CVUSA-C-ALL and CVACT-C-ALL) demonstrate that CLGT achieves state-of-the-art performance, particularly under challenging real-world corruptions.

CVGL: Causal Learning and Geometric Topology
Songsong Ouyang
Yingying Zhu"
College Computer Science and Software Engineering Shenzhen University
24001010276ma edu. en, zhuyy@szu. edu cn
Abstract
Cross-view geo-localization (CVGL) aims to estimate the geographic location of a street image by matching it with corresponding aerial image. This is critical for autonomous navigation and mapping in complex real-world scenarios. However, the task remains challenging due to significant viewpoint differences and the influence of confounding factors. tackle these issues, we propose the Causal Learning and Geometric Topology (CLGT) framework, which integrates two key components: a Causal Feature Extractor (CFE) that mitigates the influence confounding factors by leveraging causal intervention encourage the model to focus on stable, task-relevant semantics; and Geometric Topology Fusion (GT Fusion) module that injects Bird's Eye View (BEV) road topology into street features to alleviate cross-view inconsistencies caused by extreme perspective changes. Additionally, we introduce Data-Adaptive Pooling (DA Pooling) module to enhance the representation of semantically rich regions. Extensive experiments CVUSA, CVACT, and their robustness- eenaances--ehancee variants (CVUSA-C-AL) and CVACT-C-ALL) demonstrate that CLGT achieves state-of-the-ar performance, particularly under challenging real-world corruptions. Our codes are available
CLGT
Introduction
Cross-view geo-localization (CVGL) aims to es- timate the geographic location of a street image by matching it to corresponding aerial image. This task plays a crucial role in applications such as autonomous driving, robotic navigation, and
space
label
cansal Ratures
cantral
non-causal features
strst image
(24 27) However, remains highly challenging due to the extreme differences in perspective, scale, appearance, confounders and occlusion between street and aerial views. Previ- ous studies have mainly explored three directions to improve cross-view matching: viewpoint mod- cling (28), spatial alignment [21], and hard nega- tive mining [[]. Despite these efforts, cross-view geo-localization remains challenging due to weather changes, misalignment, and occlusions, all of which demand stronger generalization and discriminative feature learning. To address these limitations, Mi et al. [3] introduced feature consistency constraints to enhance robustness to orienta- tion and of variations. To better reflect real-world conditions, Zhang et [3] proposed corruption-rice benchmarks for robust evaluation, while Ye et al. (88 leveraged Bird's Eye View (BEV) representation as an intermediate domain bridge the large cross-view gap.
urban mapping
acrial image
(00
Brlwerk
Figure I: Structural Causal Model (SCM) for cross-view geo-localization. Nodes represent variables and arrows denote dependencies.
'Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Origin
Lon
Middle
Fligh
Visualization of low, mid, and high frequency components of a street image. Low and high frequencies emphasize domain-specine information such as style, while the mid-frequency band retains domain-invariann cues such as structure and shape.
Figure 2:
Building upon the aforementioned challenges and recent advances, we propose a framework to address view geo-localization from both causal perspectives and geometric. Inspired by instances where classification models mistakenly associate sheep with grass, we argue that the CVGL task should not rely confounding factors such background lighting. To mitigate the interference of confounding (non- nion-cuusal) factors and spurious correlations, while enhancing model generalization, introduce causal learning concepts into the CVGL task. Drawing work and considering the characteristics of street images, we establish the first Structural Causal Model (SCM) for CVGL, and perform causal intervention guided by the SCM. Since domain-specific (non-causal) signals often reside extreme low and high frequency bands, while mid-frequency components typically preserve arructre-relevann discriminative information [ (Figure 2 design our Causal Feature Extractor as a do-operation in the frequency domain. This allows us to implement back-door adjustment -akin causal interventions -that increases the model's attention to causal factors while reducing interference from non-causal factors. To further enhance the model's geometric awareness and mitigate the impact of large viewpoint gaps, we propose the Geometric Topology Fusion (GT Fusion) module, which robustly integrates BEV road topology into street features, leveraging clearer and more localized road topology compared to complex street images.
previous causal modeling
At the feature level, conventional pooling layers often fail to capture rich semantic cues. To address this, propose DA Pooling module that dynamically refines feature representations, enabling the model to capture more context-aware information across diverse seenes and viewpoints.
In summary, main contributions are:
We are the first to introduce causal learning concepts into CVGL tasks by applying causal interventions latent confounding factors, thereby reducing their influence on feature learning. This mechanism enables the model to focus on causally relevant information, such as building structures and road layouts, leading to improved robustness and generalization in complex environments.
We propose a GT Fusion module that enhances the model's ability to perceive geometric information, mitigating the issue of large viewpoint discrepancies in CVGL tasks and providing more robust localization performance for CVGL.
We design DA Pooling to extract rich semantic information and enhance semantic repre- sentations across different environments.
Our work highlights the importance of structural reasoning and causal robustness in bridging the cross-view domain gap, setting new direction for future research in geo-localization.

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2 Related Work
2.1 Cross-view Geo-localization
Contrastive Learning-baae -based methods. Contrastive learning has been widely applied in cross- view geolocation tasks 25; 26; 21 28;28;1111 31J. It helps mitigate feature distribution discrepancies between different viewpoints. For example, ConGeo [33 leveraged both single-view and cross- view contrastive losses while incorporating view-specific augmentation strategies. This effectively extracts robust feature representations, enabling the model to maintain high matching accuracy despite viewpoint limitations and orientation deviations. Moreover, Sample4Geo []] proposed a simplified yet effective contrastive learning framework with symmetrie InfoNCE [9 loss, which fully utilizes all negative samples to accelerate model convergence.
Incorporation of Geometric Information. To address the challenges posed by drastic viewpoint variations, some approaches focused on extracting geometrie layout information or leveraging BEV images to enforce geometric consistency constraints. For instance, GeoDTR [33 employed a ge- ometric layout extractor to learn spatial correlations between aerial and street features, preventing overfitting to low-level details. Similarly, EP-BEV (28] and HC-Net 21 integrated BEV represen- tations into cross-view geolocation to bridge the substantial differences between views. EP-BEV utilized a
structure to impose geometric consistency constraints, while HC-Net (2] directly reformulated cross-view geolocation as an image alignment problem.
2.2 Causality in Computer Vision
This limitation underscores the motivation for causal inference in visual learning: relying solely on statistical correlations in data insufficient for reliably predicting counterfactual outcomes and may amplify spurious associations. Causal inference, by modeling the underlying data-ge data-generating mechanisms, aims isolate invariant causal factors and thereby enhance generalization to unseen domains and conditions. To mitigate the interference of non-causal features and extract invariant causal representations, causal mechanisms have been widely adopted computer vision [18 30 6)
In cross-view coollocalization tasks, it is essential first establish causal relationships and then apply causal inference-inclusee interventional estimation and counterfactual analysis to eliminate confounding contextual factors and domain shifts. This approach enhances model robustness against domain variations and weather conditions, which pose significant challenges in this task. Drawing on previous causal modeling work [12 TQ and considering the characteristics of street images, we formally define the causal relationships cross-view geo-localization shown in Figure]] and employ interventional estimation to block the direct influence of confounders, significantly improving generalization. There are two common methods for causal intervention: front-door adjustment and back-door adjustment. Front-door adjustment used when non-causal factors (confounders) are unobserved, requiring the introduction of an intermediate variable M to reduce the influence of confounding factors. When confounders are observable, back-door adjustment used, where confounding factors are directly intervened to reduce their impact.
Method: CLGT
This paper proposes a novel framework for cross-view geo-localization. multi-head attention- fusion module, which robustly integrates BEV features into street features via cross-attention and dual dynamic fusion, enforces geometric consistency constraints. To enhance causal features in street representations while mitigating the interference of non-causal features, we employ causal inference-based estimation and intervention. Furthermore, we introduce a DA pooling module to refine the fused features with rich semantic information. The overall model architecture is shown in Figure The following sections provide a detailed introduction to our proposed method.
3.1 Preliminary
BEV Generation. Various methods exist for generating BEV images, including geometry-based transformations [21 28), Transformer-based (35). and diffusion- based methods (2).. To balance
3

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Causal Learning
Query
Strethriew features
Qeery
Famarr
InfoNCE
Gromerry-nni Trunsform
sarping
BEN Teatures
Refervnce
Fanes
Geometric Topology Learning
Figure 3: Overview of the proposed Causal Learning and Geometric Topology (CLGT) framework. The road topology information from BEV is fused via the GT Fusion module to obtain the fused features, which are then used for location matching with aerial image features. The causally enhanced street features from the CFE module provide causal supervision, and the DA Pooling module performs final feature extraction.
efficiency and memory use, we adopt the geometric transformation from 21), which directly computes BEV point positions via geometric back-projection from panoramic images. This explicit mapping projects street-view images into BEV space without relying depth estimation or camera parameters, enabling a simple and efficient street-to-BEV transformation.
Structural Causal Model. As illustrated in Figure assumptions:
our SCM is grounded on the following
The street image X, is mainly generated from two sources: semantic content C' and domain confounder D (e.g., background, lighting), denoted C - D.
The content C' contains both discriminative and non-discriminative parts. Together with D. the non-discriminative part contributes to the generation of non-causal features F& via + F2 + C. The CFE module perturbs a portion of these non-causal components.
The causal features F. are derived from the discriminative part of C' via C + Fe-
The full feature representation 7 = [Fe. } influences the final prediction Y via 2 - Y. where Y is the matching label.
The overall cross-view matching process can be expressed as X, + f(X) + + f(X) + X., where X, denotes the aerial image. In this context, the SCM formulation Z Y is causal abstraction of the model's ' computational path X, + f(X) + Y.
3.2
Causal Learning
The complete process Causal Learning illustrated in the top-right corner of Figure where Query" is obtained through the Causal Features Extractor.
Causal Features Extractor. As shown in Figure 2 roads and buildings in street-view images tend to occupy the mid-frequency spectrum, while style variations are concentrated in the high and low ends, respectively. This aligns with the nature of CVGL, where structural elements are crucial for localization, and view-specific cues often act as noise. To isolate task-relevant features, we leverage the Discrete Cosine Transform (DCT) in our Causal Feature Extractor. Then our Content-aware Mask (CaM) constructs three concentric circular masks with initial radii of r2, and r'. dividing the frequency spectrum into four regions. Unlike prior work (23 that used fixed spectral thresholds, these radii are linearly increased based on image gradient magnitude (via Sobel operator), so that images with stronger gradients preserve more mid-frequency components, enabling better retention causal information. Larger radii correspond to stronger Gaussian perturbations in outer frequency bands. This allows the model to adaptively preserve mid-frequency, causal components while suppressing non-causal signals. The masked frequencies are then transformed back via inverse DCT. The entire

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Cuusal Feuture Extractor
Non-cuusal
Gaussian function
Query image
Frequency domnin
Causal image
DCT
CaM
Inverse DCT
Causal factors
Figure 4: Illustration of the Causal Feature Extractor (CFE). The input image is transformed into the frequency domain via Discrete Cosine Transform (DCT). A Content-aware Mask (CaM) strategy dynamically separates mid-frequency causal components from low- and high-frequency non-causal components. A Gaussian function applied to only to the non-causal components (e.g.. lighting and brightness) reduce their influence. Both causal and non-causal parts then reconstructed via inverse (IDCT) to obtain the causally enhanced image.
process of CFE is shown in Figure
The Causal Features Extractor is defined as:
CFE(s)= (1-M(r) M(T)) F(x) G(M())-
(I)
Causal
Non-Causal Randomized
where denotes the Discrete Cosine Transform and F-L is its inverse. M(r) content-aware circular band-pass mask with radius r. G(-) denotes randomization function, defined as G(X)
X
+N(0.)))
After obtaining Query" through the CFE module (do(X, := )))), we impose supervision loss between the causally enhanced features derived from Query" and the fused features weaken the path C + X, + f(X) ) > Y (where C denotes confounding variables that influence the generation of X)), achieving the effect similar back-door adjustment in causal interventions.
3.3 Geometric Topology Learning
To guide the fusion of street and BEV features, we propose the GT Fusion module. This module effectively leverages the BEV road topology infor- mation while enriching the street features output by the backbone, dynamically injecting road topol- ogy information without compromising the street details.
Ligat
GT Fusion. shown in Figure 5 (left), our fu- sion module first applies a 3 x depthwise convo- lution to backbone outputs X,, X, E xC* to extract local features, maintaining the feature shape. To capture global context, instead the common Spatial Reduction Attention (SRA) (II), which disrupts boundary spatial structure via non- adopt Overlap- ping Spatial Reduction (OSR) to preserve spatial coherence. Finally, X, acts the query and X, as key and value in cross-attention module, ef- fectively fusing street-view and BEV features.
Crwer is
Figure Overview of the GT Fusion and DA Pooling modules. GT Fusion uses cross- attention exchange semantic information be- tween street and BEV features, then uses Dual Dynamic Fusion (DDF) enhance fusion robust- ness. DA Pooling employs a gating mechanism adaptively weight features, highlighting the most informative ones.
overlapping token reduction,
Inspired by we further introduce Dual Dy-
namic Fusion (DDF) strategy to robustly integrate the original street features (denoted as F*) and
5

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their geometry -ehnntr counterparts(denoted as F DDF is defined as:
w = o1 (Avg + F*))
Adaptive ( AvgPool(F)) F Fissed Adaptive (Conv((w- F², (1 - w) FE))
(2)
where F denotes the original street-view feature. W is a x 1 convolution, while Conv refers to 3 x 3 convolution. [, denotes the concatenation operation along the channel dimension. Y is linear transformation, and a denotes the sigmoid activation function. The GT Fusion module can be formulated as follows:
X (X,) X, proj(X.)) + X
(3)
Q A.MOSR(XX) + x²):: K,V = LM(OSR(X)) + xx)
(4)
QK
Z Softmax
+ B V, lused DDF(X,
X,
Vd
where proj refers to the 3 x 3 depthwise convolution, with X, and X representing the street and BEV features output by the backbone, respectively. L.M denotes a Layer Normalization, OSR stands for Overlapping Spatial Reduction, which is used for global information extraction. B is relative position bias matrix that encodes the spatial relationships within the attention maps, d represents the number of channels in each attention head, and DDF denotes dual dynamic fusion module.
Data-Adaptive Pooling. As shown on the right side of Figure our Data-Adaptive Pooling module improves upon conventional pooling methods such as global max pooling, global average pooling, and adaptive pooling. These traditional pooling techniques, when used as the final feature aggregation step. fail to effectively capture the rich semantic information of the features. To enhance the representation capability, we combine global max pooling. global average pooling, and geometric mean (Gem) pooling into single Gate Pooling module. This allows the model to autonomously learn the pooling output, and improve the quality of the final token, thus enhancing both the model's representational power and robustness, which can also be formulated as:
Fmoz = Max Pool Favg = AugPool Fgem = Gem(Z) Foutput =
(6)
+ Favg + +F g)
(7)
where Max Pool is global max pooling, AugPool is global average pooling, Gem denotes geo- metric mean pooling, Linear is Linear Functiion, and Gate denotes a gating mechanism.
3.4 Loss Function
We apply InfoNCE loss between the fused features and the aerial image features, which serves the primary supervision signal to optimize our model. The InfoNCE loss is defined as:
C(f, IILONNE = - log cxp(f r+/T))
(8)
R cxpo rr/T))
where f denotes the fused feature guided by the query street image, and is the set of encoded aerial images with one positive J+ matching f. The InfoNCE loss computes the dot-product similarity between and each rt. maximizing similarity with r+ and minimizing it with negatives. The temperature controls distribution sharpness and can be fixed or learned.
As stated in Section 3.2, apply InfoNCE loss between the causally enhanced features and the fused features to achieve a similar effect to back-door adjustment, encouraging the fused features focus causal components. Prior fusion, encourage the BEV and street features lie in a geometrically consistent space, we also apply InfoNCE loss between the two. To preserve their complementarity, we apply scaling factor control their learning balance. Thus, obtain the overall loss of CLGT by computing the weighted sum of them as follows:
LCLGT = L(f, S) + GLf, S JinfoNCE + aL(s, (((s,))afoNCE
where a and y are scaling coefficients, s" denotes the causally enhanced street features, s represents the original street features, and b is the BEV feature.

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4 Experiment
In our evaluation we conduct experiments on four standard benchmarks, namely CVUSA 22 CVACT 8 VIGOR [6] and CVACT_ -aaLLLLal--- CVACT_tess SeeTT_estt---AAe CVUSA-C-ALL 32 In the subsequent tables we compare our approach with previous work.
4.1 Dataset and Evaluation Protocol
Dataset. We evaluate our model on three widely-used cross-view geo-localization bench- marks CVUSA, CVACT, and VIGOR-as well as their robust variants:
CVACT LeCCT_est-C-ALL and CVUSA-C-ALL, which introduce various real-world corruptions to test model robustness under challenging conditions. CVUSA and CVACT each provide 35,532 train- ing and 8,884 testing image pairs with a striet 1-to-1 ground-to-aern correspondence. In addition, CVACT offers an extra 92,802 GPS-tagged query images for large-scale retrieval evaluation, making suitable for both standard and large-scale testing scenarios. VIGOR is a more challenging benchmark that spans four metropolitan areas areas-New New York, Seattle, San Francisco, and Chicago- and includes 105,214 query and 90,618 reference images. Unlike CVUSA and CVACT, VIGOR introduces harder retrieval setup by assigning each query one true positive and three semi comi-positive samples, thus increasing the difficulty of discriminative matching. It also supports both same-city and cross-city evaluation settings to assess generalization. To further assess model robustness in realistic condi- tions, we employ corruption-augmenne
LescTTiest-C-LA. and CVUSA-C-ALL. These variants simulate 16 types of visual degradations, generating approximately 1.5 million corrupted images in total. They provide rigorous benchmark for evaluating the model's ability to maintain performance under various environmental and sensor-induced perturbations.
datasets: CVACT_val-C-AA
Evaluation Protocol. We adopt Recall K as the primary evaluation metric, where K E (1.5, 10). as well as Recall @ 1%. A query is considered correctly localized if its corresponding aerial image appears among the top-K retrieved candidates for given street panorama.
4.2 Implementation Details
During the retrieval stage, we adopt ConvNeXt- as the backbone encoder for both street images and aerial images. Our baseline is EP-BEV. To reduce computational cost and memory usage, set the image resolution to 384 384, con- sistent with EP-BEV. The model is optimized using AdamW with initial learning rate of 0.5 x 10- 3 We train the network for 40 epochs with batch size of 128. The training is con- ducted on eight 32GB NVIDIA V100 GPUs. For both a and y in Equation 9 set their values to 0.1 to provide auxiliary supervision without overwhelming the main optimization objective. When increase the value of y, the model performance improves across various datasets. However, to prevent the value from becoming too large and causing model collapse, which would negatively affect the matching between street and acrial images, we set a default value of 0.1, although this collapse was not observed during training. The optimal value is 0.5, and we will also provide hyperparameter experiments and model performance with = 0.3 in the supplementary materials. We set the initial three radii for the content-awan mask 0.1, 0.3, and 0.6, respectively. We also observe that performance is stable under small variations in the initial radius. Other training settings follow those used in Sample4Geo.
Table 1: Ablation study on causal learning: com- parisons of performance on the CVACT_val-C ALL and CVACT_Iest-C-AN lest-C-ALL datasets.
CVACT_val--AAL
CVACT ISLCAALL
Model
R@1
R@5
R@ 10
R@1
R@5
R@10
Ours
88.68
95.58
96.66
69.06
90.12
92.70
Only CL
88.11
94,91
96.04
67.88
89.31
91.85
Baseline
85.94
94.52
95.93
64.62
87.75
90.78
4.3 Comparing with State-of-the-ap Models
Cross-view Image retrieval. As shown in Table 2 our method achieves the best overall performance across CVUSA, CVACT_val, and CVACT _est datasets. Compared with the strong baseline EP-BEV, our model improves Recall @ I from 97.41 % to 98. 08.85% on CVUSA, and from 90.61 % to 91.97% on CVACT_val On the more realistic CVACT test set, we achieve 73.22% Recall 1, surpassing EP-BEV by 1.81% % points. These consistent gains demonstrate the effectiveness of our design. The
7

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integration of BEV-based geometric topology helps capture structured layout cues, while the causal learning strategy improves robustness by suppressing spurious visual signals. Together, they enable more discriminative and generalizable representations for cross-view geo-localization. Results on the VIGOR dataset are provided in the supplementary materials.
Table Comparisons with state-of-the-an models on the CVUSA, CVACT_ val and CVACT_ies datasets. (fmethods that use polar transformation.)
CVUSA
CVACT val
CVACT Jest
R@
R@5
RØ 10
R@
R@1
R@5
R@10
R@1%
R@1
Res
R@10
RØ1%
SAFAT
89.84
96.93
98.1
99.64
81.03
92.80
94.84
85.79
LPN
20
92.83
98.00
98.85
99.78
83.66
94.14
95.92
98.41
DSM
16
91.96
97.50
98.
99.67
82.49
92.44
93.99
97.32
TeunsGeu
94.08
GeoDTTR
31
93.76
98.47
99.22
99.85
85.43
94.81
96.11
98.26
62.96
87.35
90.70
98.61
GooDTR+
14
95.05
98.42
98.92
99.77
95.50
96.50
98.32
67.75
90.15
92.73
98.53
GooDTR
95.43
99.34
64.52
88
Sample4G
98.68
99.68
99.78
99.87
90.81
96.74
97.48
98.77
71.51
92.42
94.45
98.70
ConGeo
13
98.30
99,90
90.10
98.20
71.70
98.30
EP-BEV
97.41
99.40
99.60
99.76
90.61
96.57
97.32
98.71
71.41
92.38
94.37
98.77
Ours
98.73
99.71
99.80 80
99.84
91.61
96.93
97.72
98.77
73.03
93.03
94.81
98.63
Ours
0.5)
98.85
99.71
99.81
99.86
91.97
96.95
97.72
98.77
73.22
93.50
95.23
98.79
Robustness Evaluation. As shown in Table our method consistently outperforms baselines across robust datasets, with an average improvement of 5.00%. Notably, achieves a 6.62% gain on CVUSA-C-ALL, highlighting the model's s ability to extract localization-relevat cues such as edge textures of buildings and road structures, while suppressing non-causal noise like lighting LAACCTTest---AAAA
and weather conditions. On challenging splits such as CVACT CWACTTval-C-ALL. and CVUSA-C-ALL, our approach demonstrates strong robustness by mitigating the impact of 16 common perturbations and improving retrieval accuracy. Furthermore, in cross-dataset evaluation (trained on CVUSA, tested on CVACT), our method improves performance by 5.50% and 2.84% (Table 6 further validating its generalization and robustness under distribution shifts.
Table 3: Comparisons with state-of of-the--rttttt models on the CVUSA-C-ALL, CVACT val-C-ALL and CVACT test-C-ALL datasets.
CVUSA-C-ALL
CVACT -esC--LL---L
Model
10
CVM-Net
3
6.09
16.05
23.14
52.51
OHCNNN
SAFA 14
63.68
78.08
82.82
93.91
56.72
73.60
78.59
91.32
31.18
52.06
58.60
90.41
72.97
51.07
DSM [6
75.27
86,26
89.42
95.07
70.04
82.81
85.86
93.51
47.13
68.41
73.52
93.18
L2LTR
25
87.93
95.45
97.01
99.01
82.13
93.34
94.93
98.10
57.20
82.59
87.23
98.09
TransGoo
82.72
91.95
94.03
74.04
86.19
89.10
94,98
52.18
74.35
78.99
95.03
GooDTR
33
84.64
93.29
95.01
98.24
77.40
88.95
91.28
95.91
$2.87
78.84
83.17
95.84
Ours
92.64
97.21
98.21
99.35
88.68
95.58
96.66
98.49
69.06
90.12
92.70
98.41
Ours (y 0.5)
92.84
97.61
98.41
95.84
98.49
69.71
91.05
93.30
98.85
Table 4: Ablation study on DA Pooling: compar- ison with other pooling methods CVACT.
Table 5: Ablation study of the CLGT on CVACT_val
Method
CVACT val
CVACT Jest
Method
CVACT val
R@F
R85
RØ 10
RØ1 1
R
R@000
R@1
R@5
R@0
R@11%
DA pooling
91.61
96.93
97.72
73,03
93.03
94,81
CLGT
91.61
96.93
97.72
98.77
Gem
89.24
95.64
96.65
69.78
92.18
93.78
Fusion
91.24
97.41
Avg
91.19
96.52
97.33
71.58
92.52
94.51
CFE
91.01
96.66
97.51
98.64
Max
90.55
96.51
97.25
70.78
92.20
94.23
Baseline
90.61
96.57
97.32
98.71
Ablation Study. We conduct comprehensive ablation studies CVACT_ val, CVACT_val-C-Ar and -asAT-test---ALL to evaluate the individual contributions each proposed component. As reported in Table 5 employing only the Geometric Topology Fusion (GT Fusion) module

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yields a Recall@ of 91.01% on CVACT val. This result highlights the importance of geometric consistency learning, which enables the model to better capture road layouts and spatial structures. thereby improving the alignment between ground-level and aerial images. When using only the CFE module, the model achieves Recall @ 91.24%, illustrating its capability to suppress spurious correlations and guide the model toward learning causally relevant and semantically stable features. This enhancement plays a crucial role resisting interference from latent confounders. As shown in Table our DA Pooling consistently outperforms traditional pooling schemes across all evaluation metrics. Specifically, compared to GeM, DA Pooling improves Recall on CVACT by +2.37%, showing its advantage in dynamically emphasizing informative spatial regions. This confirms the importance of adaptivity in multi-vize view feature aggregation.
Table 6: Results on different cross-vies vies transfer tasks. Each task is evaluated with representative methods. (fMethods using polar transformation.)
Task
Method
R@1 1
R@5
R@ 10
R@ 1%
1.2TR
M5
47.55
70.58
91.39
L2LTR
25
52.58
75.81
93.51
CVUSA CVACT_
GeoDTR
47.79
70.52
GooDTR
33
53.16
75.62
Samp4G
56.62
77.79
87.02
94.69
EP-REV
59.32
80.79
Ours
60.70
81.40
86. 10
95.
Ours (7 0.5)
64.82
84.38
88.77
96.16
L2LTR
25
L2LTR 25
CVUSA CVACT_ISS
GooDTR
11.24
18.69
23.67
GeoDTR
33
22.09
32.22
39.59
85.53
Samp4G
27.78
$2.08
60.33
94.88
EP-REV 23
32.68
58.62
65.34
95.21
Ours
33.23
$9.59
67.53
95.31
Ours (- 0.5)
38.52
63.37
71.40
Furthermore, as shown in Table D causal learning alone brings significant improvements on the more challenging and corrupted test sets: Recall @ increases by 2.23% on CVACT_van val-C-ALL and by 3.20% on CVACT LAAAT_Lest---ALL In addition to Recall 1, other evaluation metries such as Recall @ and Recall@ @ 10 also show consistent improvements, closely approaching the performance of the full CLGT model. These results confirm the effectiveness of our causal learning strategy improving robustness under real-world corruptions and diverse input conditions. Overall, the ablation results validate that both modules- GT Fusion and CFE contribbtt meaningfully and complement each other in addressing the challenges of cross-view geo-localization.
Visualization Analysis. To qualitatively assess the effectiveness of CLGT, we visualize attention heatmaps generated by the baseline and CLGT models on test images from the CVUSA dataset. As shown in Figure 6 we first visualize the heatmaps on clean images. It can be seen that the baseline model's 's attention is more scattered, even focusing the sky and other background noise, while CLGT consistently attends to rask-relevant information, especially road structures. Under heavy snow conditions, compared to the baseline, the regions attended by our model remain almost unchanged, whereas the bascline's 's focus completely misaligned. This shows that our CLGT consistently attends to semantically meaningful structures, such as road intersections and corner layouts, which are more stable across views. This demonstrates the effectiveness of our design in guiding the model to prioritize task-relevant features and suppress distractions, leading to improved cross- view discriminability. Visualizations for other corruption types can be found in the supplementary materials.
we report both GFLOPs and average inference time Complexity Analysis. As shown in Table 7
(in milliseconds per batch of 128 images) on the CVACT_we val set. The results demonstrate that our method achieves the best @@@ accuracy with only marginal computational overhead compared to other methods.

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Table 7: Comparison of GFLOPs and average inference time per batch (batch size 128) on CVACT val. Avg inference time (ms) represents the mean time to process one batch.
Method
GFLOPs
Avg Inference Time (ms)
CVACT val R
SungledGeoo EP-BEV
90.54
2367.55
90.81
90.54
2396.53
90.61
Ours
90.56
2374.76
91.61
Ours
Baseline
Clean
Snow
Figure 6: Heatmap visualizations on CVUSA under clean and snow settings. Compared to the baseline, our method focuses more on task-relevant structural information. Under heavy snow conditions, our method remains highly robust, with attention regions largely unchanged, whereas the baseline's attention is completely misaligned.
5 Conclusion and Future Work
In this work, we present novel cross-view geo-localization framework that integrates BEV-street view fusion with causal learning mechanism. Unlike previous methods that utilize BEV merely as an auxiliary representation, our approach enables feature-level interaction that effectively and robustly incorporate road topology. further improve generalization, we introduce a causal intervention module, thereby enhancing filters out non-causal information and enhances model robustness under various conditions. Experimental results on both standard and challenging datasets demonstrate consistent performance gains. Nonetheless, the BEV representations derived from geometric transfor- mations contains considerable noise, which limits further the performance improvements. Future work will explore more advanced causal inference strategies tailored to the dynamics of complex cross-view localization tasks.
Acknowledgments
This work was supported part by Shenzhen Science and Technology Program under Grant 1722224081342550000 and Grant 202208 10142553001, in part by the Key Project of Department of Education of Guangdong Province under Grant 20237DZZX0166 and in part by the National Natural Science Foundation of China under Grant 62072318 and Grant U22A2097.
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Reviews:
[{"review_id": "rJqU1zmShN", "reviewer": "Reviewer_W79E", "summary": "This paper aims to address the cross-view geo-localization task where a street-view query image is matched with an extensive database of aerial photos. The drastic viewpoint change, weather change, misalignment, and occlusion between the ground and aerial views make the task challenging. To address these challenges, the authors in this work propose a Causal Learning and Geometric Topology (CLGT) framework, which consists of two novel architectural contributions - a causal feature extractor to focus on stable and task-relevant features, and GT fusion to efficiently inject the Bird’s Eye View (BEV) feature with road topology. The paper also proposes data-adaptive pooling, which outperforms conventional global max pooling, global average pooling, and adaptive pooling on the task at hand. State-of-the-art results on four popular benchmarks prove the efficacy of the proposed CLGT framework.", "questions": "(1) The ablation studies in this paper can be improved. I am interested in seeing a systematic comparison of (a) Causal Features Extractor vs Features using an off-the-shelf pre-trained encoder, (b) GT fusion vs normal cross-attention based fusion, and (c) data-adaptive pooling vs traditional global average/max pooling. I would like to know the author's opinion on which model component plays the most crucial role in improving performance. The authors have reported ablative experiments in Table 5, but it is not clear to me what the model exactly looks like for the rows 'w/o GT Fusion' and 'w/o CFE', i.e., when GT fusion and CFE are not used. How are the features extracted and fused in these cases?\n\n(2) The paper lacks sufficient implementation and hyperparameter details. Which hyperparameters contribute most to the training stability? \n\n(3) Another central area of improvement is the qualitative results. The authors should explicitly demonstrate examples where the proposed architectural components outperform previous methods. Since the primary contribution of this work is the novelty of the model, we should examine where the proposed system succeeds but previous systems fail.\n\n(4) I acknowledge the paper achieves SOTA results, but the authors have missed multiple important baselines (including some already published in recognized venues). I request that the authors conduct an extensive literature review and report all peer-reviewed and published baselines that have already been established.\n\n(a) Cross-View Image Sequence Geo-localization. WACV 2023.\n(b) GeoDTR+: Toward Generic Cross-View Geolocalization via Geometric Disentanglement. TAPMI 2024.\n(c) Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization. CVPR 2024.\n(d) Robust Cross-View Geo-Localization via Content-Viewpoint Disentanglement. arXiv 2025.\n(e) Where am I? Cross-View Geo-localization with Natural Language Descriptions. arXiv 2025.\n\n\nDue to these limitations, I recommend a 'Borderline rejection' at this stage, and I will wait for the authors to address the points (1), (2), and (3). I understand that the rebuttal timeframe is limited, but having a clearer picture of which model components are most effective is essential. I will be willing to increase the score if the issues are well-addressed.", "limitations": "The paper does not include any discussion about the failure modes of the proposed system. The authors should discuss the limitations of the CLGT system in a positive light and indicate how it can be further improved.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "(1) The paper is well-written and easy to digest. The authors discuss the challenges of the cross-view geo-localization task and systematically propose the model components to solve them. The architectural details are well-documented. \n(2) The strongest point of this paper is the effective results - there are several previous works on cross-view geo-localization reporting results on the CVUSA and CVACT datasets. This paper clearly achieves a state-of-the-art score on the majority of the metrics.\n\nPlease find the detailed weaknesses in the 'questions' section.", "was_revised": true, "final_justification": "As mentioned in the comments, the following concerns are addressed:\n\n(1) Systematic comparison of (a) Causal Features Extractor vs Features using an off-the-shelf pre-trained encoder, (b) GT fusion vs normal cross-attention based fusion, and (c) data-adaptive pooling vs traditional global average/max pooling; \n(2) Important Hyper-parameters, and \n(3) Adding recent baselines.\n\nThere are two important issues the paper still misses out: (1) A non-cherry-picked qualitative comparison with baselines, showing a broad variety of samples where the model performs better than existing models, (2) a honest error analysis, describing where the proposed system fail, and some discussion on future works.\n\nSince both issues can be included in the final manuscript (or, can be analyzed by future researchers if the source code and pre-trained checkpoints are released), I decide to raise my score to a 'borderline accept'.", "rebuttal": "## **Response to Reviewer 1 (R1)**\n\nWe thank the reviewers for their time and valuable feedback. We appreciate the thoughtful comments and constructive suggestions, which help us improve the clarity and depth of our work.\n\n### **Q1.**  Regarding the ablation studies.\n\n- (a) Causal Features Extractor vs Features using an off-the-shelf pre-trained encoder\n\n  **A1:** We clarify that CFE is not a replacement for the pre-trained encoder, but a lightweight module applied in the frequency domain to suppress non-causal signals. It enhances feature discriminability and generalization by emphasizing causal, structurally relevant components. The transformed image is then fed into a standard encoder (ConvNeXt-B), ensuring our models remain based on off-the-shelf backbones.\n\n- (b) GT fusion vs normal cross-attention based fusion\n\n  **A2:**  We thank the reviewer for the valuable suggestion regarding a more comprehensive ablation of fusion strategies. Following this, we compared our GT Fusion module with two recent cross-attention-based designs fusion: **GeminiFusion**: Efficient Pixel-wise Multimodal Fusion for Vision Transformer [arXiv 2024] and **CrossFuse**: A Novel Cross Attention Mechanism based Infrared and Visible Image Fusion Approach [arXiv 2024]. For GeminiFusion, which outputs dual-view features, we used the street-as-query branch for supervision to ensure fairness.\n\n  As shown in Table I, our method consistently outperforms both alternatives across CVACT, highlighting the effectiveness of our design in integrating BEV geometry with street-view features.\n\n  **Table Ⅰ**: Performance comparison of GT Fusion, GeminiFusion, and CrossFuse on the CVACT dataset.\n\n  | Method          | CVACT_val |           |           |           | CVACT_test |           |           |           |\n  | --------------- | :-------: | :-------: | :-------: | :-------: | :--------: | :-------: | :-------: | :-------: |\n  |                 |    R@1    |    R@5    |   R@10    |   R@1%    |    R@1     |    R@5    |   R@10    |   R@1%    |\n  | GT Fusion(Ours) | **91.61** | **96.93** | **97.72** | **98.77** | **73.03**  | **93.03** | **94.81** | **98.63** |\n  | GeminiFusion    |   89.01   |   95.51   |   96.33   |   98.26   |   69.51    |   92.07   |   93.55   |   98.13   |\n  | CrossFuse       |   90.32   |   96.32   |   97.15   |   98.66   |   70.62    |   92.10   |   94.16   |   98.44   |\n\n- (c) Data-adaptive pooling vs traditional global average/max pooling.\n\n  **A3:** We sincerely apologize for omitting the ablation study on pooling strategies in the main paper, and we greatly appreciate the reviewer for pointing this out. As suggested, we conducted a comparative ablation among GeM pooling, global average pooling (Avg), and global max pooling (Max). Furthermore, we applied our DA Pooling on the EP-BEV branch to demonstrate its effectiveness under varying spatial cues.\n\n  As shown in **Table Ⅱ**, our DA Pooling consistently outperforms traditional pooling schemes across all evaluation metrics. Specifically, compared to GeM, DA Pooling improves Recall@1 on CVACT by +2.27%, showing its advantage in dynamically emphasizing informative spatial regions. This confirms the importance of adaptivity in multi-view feature aggregation.\n\n  We will add these results to the final version .\n\n  **Table Ⅱ.** Ablation study of different pooling strategies on the CVACT dataset.\n\n  | Method                | CVACT_val |           |           |           | CVACT_test |           |           |           |\n  | --------------------- | --------- | --------- | --------- | --------- | ---------- | --------- | --------- | --------- |\n  |                       | R@1       | R@5       | R@10      | R@1%      | R@1        | R@5       | R@10      | R@1%      |\n  | **DA pooling (Ours)** | **91.61** | **96.93** | **97.72** | **98.77** | **73.03**  | **93.03** | **94.81** | **98.63** |\n  | Gem                   | 89.24     | 95.64     | 96.65     | 98.50     | 69.78      | 92.18     | 93.78     | 98.36     |\n  | Avg                   | 91.19     | 96.52     | 97.33     | 98.72     | 72.02      | 92.52     | 94.51     | 98.52     |\n  | Max                   | 90.55     | 96.51     | 97.25     | 98.70     | 70.78      | 92.20     | 94.23     | 98.44     |\n\n- (d) Clarification on model components and ablation settings.\n\n  **A4:** We thank the reviewer for this insightful question. As suggested, we clarify the individual contributions of each module and the ablation settings reported in Table 5. Our framework comprises three core components—GT Fusion, the CFE, and DA Pooling—each designed to address a specific challenge in cross-view geo-localization. Among them, CFE proves most critical to performance, as it directly mitigates the influence of confounding factors and enhances generalization. This is consistently evidenced by the improvements shown in both Table 1 and Table 5.\n\n  Regarding the ablation setup: (1) w/o GT Fusion removes the geometric fusion module, reverting the model to a dual-branch structure akin to EP-BEV, where causally enhanced features are directly aligned with original street features via contrastive loss, without geometry-aware fusion; (2) w/o CFE eliminates the causal intervention path and its associated supervision. This setting retains the rest of the pipeline but loses the guidance provided by causal enhancement.\n\n### **Q2.** Regarding the hyperparameters experiment.\n\n**A5:** We thank the reviewer for raising this important concern. As the reviewer suggested, we performed a sensitivity analysis on the weights of the two auxiliary objectives: $\\alpha$ for the contrastive loss between street and BEV features before fusion, and $\\gamma$ for the contrastive loss between the causally enhanced features and the fused features.\n\nThe $\\alpha$-weighted loss serves as a pre-alignment regularization mechanism, encouraging the street and BEV features to be geometrically consistent before fusion. While we conducted ablation experiments on different values of $\\alpha$, we observe that this parameter has a relatively minor impact on performance. Therefore, due to space constraints, we omit the detailed table. Intuitively, overly large $\\alpha$ may over-constrain the feature space and interfere with effective fusion, while small values provide slight regularization benefits without significant influence.\n\nFor the $\\gamma$ coefficient (**Table Ⅲ**), we conducted controlled experiments and observed that setting $\\gamma = 0.5$ yields the best performance across datasets. In the main paper, we conservatively used $\\gamma = 0.1$ to avoid potential model collapse that could undermine the street-to-satellite alignment. Although this collapse was not observed during training, we adopted a cautious setting to ensure robustness. We will include the results corresponding to the optimal $\\gamma$ value in the final version and explain this design choice accordingly.\n\n**Table Ⅲ. CVACT_val-C-ALL | Effect of Varying γ (causal supervision weight, α fixed at 0.1)**\n\n|           | R@1       | R@5       | R@10      | R@1%      |\n| --------- | --------- | --------- | --------- | --------- |\n| 0.1 (now) | 88.68     | 95.58     | 96.66     | 98.49     |\n| 0.3       | 88.92     | 95.78     | 96.84     | 98.46     |\n| 0.5       | **89.49** | **95.84** | **96.92** | 98.49     |\n| 0.7       | 89.25     | 95.83     | 96.90     | **98.63** |\n\n### **Q3.** Regarding the visualization.\n\n**A6:** We thank the reviewer for the helpful comment and apologize for the limited qualitative visualizations in the main text. While examples are included in the appendix, we will move key results into the main paper for clarity. As shown in **Figure 5** , the baseline model tends to focus on visually salient but semantically irrelevant regions (e.g., sky, background), revealing sensitivity to spurious correlations. In contrast, our model, guided by geometric topology and causal learning, consistently attends to meaningful structures such as road layouts and vegetation, capturing more stable and view-invariant features while effectively filtering out confounding noise.\n\n### **Q4.** Comparing with recent baseline.\n\n**A7:** We thank the reviewer for emphasizing the need for broader baseline comparisons and appreciate the suggested references. However, several of these works had not released official code or pretrained models at submission time, making timely reproduction difficult. Moreover, some focus on different tasks (e.g., language-based or sequential localization), which are not directly comparable to our single-image setting.\n\nTo address this concern, we conducted additional experiments comparing our method against two strong and peer-reviewed baselines that are publicly available and more aligned with our setting:  \n\n- GeoDTR+ [TAPMI 2024].\n- Cross-View Geo-Localization with Street-View and VHR Satellite Imagery in Decentrality Settings [arXiv 2025]. \n\nWe evaluated these baselines on CVUSA and CVACT_val using their official implementations. As shown in **Table Ⅳ** (to be added in the final version), our method consistently outperforms them, demonstrating its effectiveness and generalizability.\n\n**Table Ⅳ.** Comparison with recent and representative baselines on three public benchmarks: CVUSA and CVACT_val. \n\n|         | CVUSA     |           |           |           | CVACT_val |           |           |           |\n| ------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- |\n|         | R@1       | R@5       | R@10      | R@1%      | R@1       | R@5       | R@10      | R@1%      |\n| Ours    | **98.73** | **99.71** | **99.80** | **99.84** | **91.61** | **96.93** | **97.72** | **98.77** |\n| DReSS   | 98.45     | 99.61     | 99.72     | 99.83     | 91.35     | 96.67     | 97.36     | 98.62     |\n| GeoDTR+ | 95.05     | 98.42     | 98.92     | 99.77     | 87.76     | 95.50     | 96.5      | 98.32     |"}, {"review_id": "YOtD4HWi92", "reviewer": "Reviewer_gb9n", "summary": "The paper tackles cross-view geo-localization which consists in matching street view images to aerial images. This is a challenging task due to the large viewpoint changes between street and aerial images, significant illumination changes as well as potential occlusions. To deal with condition variations and the dynamic nature of the scenes, the authors introduce a causal feature extractor that isolates features relevant for localization such as structural elements while removing distracting features. The causal feature extractor predicts a content aware frequency mask to separate relevant/non-relevant features in the frequency space. Furthermore, street view images are lifted to bird eye view and the respective features from both perspectives are merged through the geometric topology fusion module which creates more robust and discriminative feature maps through. Subsequently, global descriptors are pooled from the fused feature maps through a data-adaptative pooling layer which focuses on semantically rich regions. The resulting localization pipeline is evaluated on two cross-view datasets and their variants which include.", "questions": "Overall the motivation behind introducing the street/BEV fusion and the causal mechanism are sound and the resulting method is effective and brings small accuracy gains. However given the high accuracy of the baseline method (EP-BEV) this paper is based upon and the limited novelty, some more explanations and ablations would be required to better grasp the significance of the contributions. (cf weaknesses 2 and 3).  \n\nl144.  “our method dynamically adapts to each input by generating a content-aware frequency mask”, does this refer to the radius of the band-pass mask, how is r predicted?\n\nWhat is the benefit of aligning the street and BEV features through the contrastive loss since the street/BEV features are fused anyway in the subsequent GT fusion module? Also this loss would be applied on the pixel level for dense features, do you randomly sample negative features in the image space?\n\nCould you provide a run time comparison with other baselines both for training and inference?\n\nCf. weakness 3)", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 1, "strengths_and_weaknesses": "### Strengths\n1) The causal learning mechanism is an effective way of injecting causal information in the feature representation. In addition, the causal feature extraction is an elegant way of dissociating semantically important features from distractors without relying on third party pretrained models for detection or condition correction. Its effectiveness is supported by ablation studies. \n2) Augmenting street features with BEV features gives a complementary perspective and makes the resulting representation more robust wrt viewpoint changes and more discriminative which is particularly relevant in the context of cross-view geo-localization. \n3) The proposed representation shows consistent accuracy improvements over prior cross-view geo-localization baselines across all datasets.\n\n### Weaknesses\n1) The causal feature extraction comes from [21] which limits the contributions of the proposed method. The authors state that the difference lies in their content aware frequency mask as opposed to the fixed thresholds used in [21]. However no explanation is provided as to how the content aware mask is predicted. As it is an important part of the paper, this ought to be explained and the benefits of the content aware mask compared to a fixed bandwidth should be shown.     \n\n2) I am skeptical about the benefits of the proposed data-adaptive pooling layer. The geometric mean pooling is already a generalization of global average and max pooling layers, where the learnable parameter balances between max and average pooling behaviors (as such GeM is already “data-adaptive”). I do not see any ablation study to justify the design choice of the proposed data-adaptive pooling layer. \n\n3) While I understand the motivation behind fusing street/BEV features I find the following sentences confusing and it is hard to tie them to the GT fusion module’s architecture.\nl.113 “A multi-head attention-based fusion module integrates BEV features into street features, enforcing geometric consistency constraints”, how can multi-head attention enforce geometric constraints?\nl.151 “effectively leverages the BEV geometric consistency constraints”, what does the geometric consistency constraints refer to here?\nl.152 “dynamically injecting road geometric topology information”, is geometric topology the right term to describe scene layout, doesn’t street view image then also contain geometric topology information?\nDescription of Fig. 4 “ DFF to inject BEV’s geometric topology”, isn’t the BEV features already integrated through the MHA layers\nThe GT fusion module should be more ablated and tied to the textual explanations. Ablations could include, w/o DDF, w/o OCR, maybe basic fusion mechanisms (MHA, concatenation, summation).    \n\n4) Missing reference for InfoNCE: Oord, Aaron van den, Yazhe Li, and Oriol Vinyals. \"Representation learning with contrastive predictive coding.\"", "was_revised": true, "final_justification": "Overall I find the justification/formulation around \"geometric topology\" and \"causality\" to be overly pompous at time and make it hard to identify where the contributions come from. For example, the CFE module was introduced in [21] (the overall causal framework was also established in prior works and ends up being a type of image augmentation) and the authors contribution lies in modifying the frequency threshold. Similarly, the representation learning framework follows EP-Bev and the authors introduce the GTFusion module to merge street and BEV features before the contrastive learning objectives.  \nHowever these incremental contributions are well motivated and, in the rebuttal, the authors provided additional ablations to support the effectiveness of the GTFusion / CFE and data pooling modules. \nThe resulting pipeline ends up achieving SOTA accuracy with similar computational efficiency as prior baselines. As such, I believe the reasons to accept the papers outweigh the reasons to reject and I have upgraded my score to \"borderline accept\".", "rebuttal": "## Response to Reviewer 2 (R2)\n\nWe thank the reviewers for their time and valuable feedback. We appreciate the thoughtful comments and constructive suggestions, which help us improve the clarity and depth of our work.\n\n### **Q1.** Regarding the GT Fusion and DA pooling.\n\n**1.DA pooling:**  \t\n\n**A1:** We thank the reviewer for pointing this out. While GeM pooling generalizes average and max pooling via a learnable exponent $p$, its adjustment is global and uniform, which limits its ability to capture view-dependent spatial variations.  In contrast, our DA Pooling introduces a channel-specific gating mechanism to adaptively combine avg, max, and GeM, allowing finer spatial sensitivity. As shown in Table I, it consistently outperforms all baselines on CVACT, validating its effectiveness. We will include these results in the final version.\n\n**Table Ⅰ.** Ablation study of different pooling strategies on the CVACT dataset.\n\n| Method                | CVACT_val |           |           |           | CVACT_test |           |           |           |\n| --------------------- | --------- | --------- | --------- | --------- | ---------- | --------- | --------- | --------- |\n|                       | R@1       | R@5       | R@10      | R@1%      | R@1        | R@5       | R@10      | R@1%      |\n| **DA pooling (Ours)** | **91.61** | **96.93** | **97.72** | **98.77** | **73.03**  | **93.03** | **94.81** | **98.63** |\n| Gem                   | 89.24     | 95.64     | 96.65     | 98.50     | 69.78      | 92.18     | 93.78     | 98.36     |\n| Avg                   | 91.19     | 96.52     | 97.33     | 98.72     | 72.02      | 92.52     | 94.51     | 98.52     |\n| Max                   | 90.55     | 96.51     | 97.25     | 98.70     | 70.78      | 92.20     | 94.23     | 98.44     |\n\n**2.Clarifying GT Fusion:** \n\n​\t**A2:**  We thank the reviewer for these insightful comments. Our GT Fusion uses a street-as-query strategy where street features query BEV features via multi-head attention to selectively aggregate relevant geometric information. This design aligns the fusion with the street perspective while leveraging BEV's cleaner geometric representation of scene layout. We acknowledge the terminology inconsistency—\"geometric consistency constraints\" and \"geometric topology\" both refer to structural layout cues, and we will unify this language. While street images do contain geometric information, their complexity (dynamic objects, lighting variations) makes it harder for models to focus on structural elements. BEV provides a cleaner, top-down view of these structures, which motivates our fusion approach. Regarding DDF, we clarify it serves for robust fusion by suppressing noisy BEV signals rather than \"injecting\" geometry, and we will revise the figure caption to reflect this.\n\n**3.Ablation Study of GT Fusion and Its Difference from EP-BEV:**\n\n​\t**A3:** We thank the reviewer for the suggestion to provide more detailed ablations on the GT Fusion module. Following this, we conducted experiments removing key components: without DDF, which normally enhances fusion robustness by reducing the impact of geometric distortions and noise in BEV features, performance noticeably drops under challenging conditions due to increased sensitivity to unreliable BEV information; without OSR, which supplies global context and supports long-range dependencies, the fusion’s ability to capture meaningful correspondences between street and BEV features is weakened.\n\nAs shown in Table Ⅱ, the removal of either module results in a consistent degradation across all evaluation metrics, highlighting their complementary roles in ensuring effective and robust fusion.\n\n**Table Ⅱ.**  Ablation Study of GT Fusion by Removing DDF or OSR.\n\n| Method          | CVACT_val |           |           |           | CVACT_test |           |           |           |\n| --------------- | --------- | --------- | --------- | --------- | ---------- | --------- | --------- | --------- |\n|                 | R@1       | R@5       | R@10      | R@1%      | R@1        | R@5       | R@10      | R@1%      |\n| GT Fusion(Ours) | **91.61** | **96.93** | **97.72** | **98.77** | **73.03**  | **93.03** | **94.81** | **98.63** |\n| w/o DDF         | 89.38     | 95.51     | 96.60     | 98.47     | 66.95      | 90.18     | 92.76     | 98.44     |\n| w/o OSR         | 91.22     | 96.52     | 97.45     | 98.59     | 72.61      | 92.60     | 94.62     | 98.51     |\n\n**A4.** **Unlike EP-BEV**, which directly uses potentially noisy BEV features, our method shows stronger robustness under challenging conditions by dynamically integrating BEV geometry into street representations. As shown in Table Ⅲ, our GT Fusion module significantly outperforms EP-BEV on the corrupted CVACT_val-C-ALL set, highlighting its effectiveness in suppressing unreliable BEV cues and reinforcing the novelty of our approach.\n\n**Table Ⅲ.** Robustness Comparison on the Corrupted CVACT_val-C-ALL Dataset\n\n|                |      | CVACT_val-C-ALL |           |           |           |\n| -------------- | ---- | --------------- | --------- | --------- | --------- |\n|                |      | R@1             | R@5       | R@10      | R@1%      |\n| Ours           |      | **88.68**       | **95.58** | **96.66** | **98.49** |\n| Only GT Fusion |      | 87.88           | 94.81     | 96.02     | 98.39     |\n| EP-BEV         |      | 85.94           | 94.52     | 95.93     | 98.21     |\n\n### **Q2.** Regarding the content-aware mask.\n\n**A5:**  We thank the reviewer for highlighting the need to clarify our content-aware frequency mask. Rather than using a fixed band-pass mask radius, we first apply DCT to convert the image into the frequency domain, where low to high frequencies are spatially distributed (low frequencies lie in the top-left and high frequencies in the bottom-right.). Then, our content-aware mask constructs three concentric circular masks with initial radii of 0.1, 0.3, and 0.6, dividing the frequency spectrum into four regions. These radii are linearly increased based on image gradient magnitude (via Sobel operator), so that images with stronger gradients preserve more mid-frequency components, enabling better retention of causal information. Larger radii correspond to stronger Gaussian perturbations in outer frequency bands. This allows the model to adaptively preserve mid-frequency, causal components while suppressing non-causal signals. The masked frequencies are then transformed back via inverse DCT. We also observe that performance is stable under small variations in the initial radius. \n\nWe compared our adaptive frequency mask with a fixed-threshold strategy and found that dynamically adjusting mask radii based on image gradients better preserves causal features. As shown in Table IV, our method consistently outperforms the fixed variant on CVACT_val-C-ALL, demonstrating the importance of adaptivity for robust performance in complex scenes.\n\n**Table Ⅳ.** Comparison between our content-aware frequency mask and fixed-threshold frequency mask on CVACT_val-C-ALL.\n\n|                  |      | CVACT_val-C-ALL |           |           |           |\n| ---------------- | ---- | --------------- | --------- | --------- | --------- |\n|                  |      | R@1             | R@5       | R@10      | R@1%      |\n| Ours             |      | **88.68**       | **95.58** | **96.66** | **98.49** |\n| fixed thresholds |      | 88.28           | 94.92     | 96.11     | 98.41     |\n\n### **Q3**. Regarding the loss between street and BEV features.\n\n**A6:**  We thank the reviewer for the insightful question. While GT Fusion enables deep interaction between street and BEV features, the contrastive loss serves as a complementary pre-alignment regularizer, encouraging both features to lie in a geometrically consistent space before fusion. To be clear, the contrastive loss does not serve as the main objective, but rather acts as a regularization signal to encourage consistency between the two views during encoding. In practice, we scale the contrastive loss by a small factor (0.1) to ensure it has minimal interference with the fusion learning. We also clarify that negative samples are randomly drawn from feature maps to reduce overhead. As further support, we conducted a sensitivity study on $\\alpha$ (see Table Ⅴ). We find that small values (0.1) provide slight regularization benefit, while larger values may interfere with the fusion process, consistent with its intended role as a pre-fusion alignment signal.\n\n**Table Ⅴ. CVACT_val | Effect of Varying α (street & BEV alignment weight)**\n\n|               | R@1       | R@5       | R@10      | R@1%      |\n| ------------- | --------- | --------- | --------- | --------- |\n| 0 (no loss)   | 91.55     | **97.09** | 97.69     | 98.71     |\n| 0.1 (default) | **91.61** | 96.93     | **97.72** | **98.77** |\n| 0.3           | 91.45     | 96.81     | 97.52     | 98.66     |\n\n### **Q4**. Regarding the train and inference time.\n\n**A7:**  We thank the reviewer for the valuable suggestion. Following it, we conducted runtime comparisons including GFLOPs and average inference time per batch (batch size = 128). The results are shown below:\n\n**Table Ⅵ**. Comparison of GFLOPs and average inference time per batch (batch size = 128) on CVACT_val. Avg inference time (ms) indicates the mean time to process one batch.\n\n| Method     | GFLOPs    | Avg inference time/ms | CVACT_val R@1 |      |\n| ---------- | --------- | --------------------- | ------------- | ---- |\n| Sample4Geo | **90.54** | **2367.55**           | 90.81         |      |\n| EP-BEV     | **90.54** | 2396.53               | 90.61         |      |\n| Ours       | 90.56     | 2374.76               | **91.61**     |      |\n\nWe emphasize that our method maintains comparable inference efficiency to lightweight models (Sample4Geo, EP-BEV), while achieving consistently better accuracy. This confirms the effectiveness of our design in balancing accuracy and computational cost."}, {"review_id": "BLc6BNVPsy", "reviewer": "Reviewer_YUub", "summary": "In this paper, the author introduces Causal Feature Extractor, Geometric Topology Fusion via BEV, and DA Pooling for cross-view matching. \nMy main concern is the design of Structural Causal Model. More illustrations are needed.\nFigure 3 is confusing. What is structural causal components and redundant non-causal components in the figure 3 ?\nThe Causal Feature Extractor is a data-augmentation method, but it is not neccessary better than the conventional data agumentation. \nThe Geometric Topology Fusion is similar to Qformer, which leverage street feature as query, and the transformed BEV feature as Key and Vale. \nThe DA pooling is incremental to use three kinds of pooling together.", "questions": "Please see the weakness.", "limitations": "Yes.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "In this paper, the authors propose a novel framework for cross-view geo-localization that integrates a Causal Feature Extractor (CFE), Geometric Topology Fusion (GT Fusion) using BEV, and a Data-Adaptive Pooling (DA Pooling) module.\n\nStrengths:\n\n1. The paper addresses a timely and challenging problem in cross-view geo-localization and presents a complete pipeline with causal and geometric components.\n\n2. The experimental results are strong across multiple datasets, including corrupted variants, demonstrating robustness.\n\n3. The motivation for introducing causal learning to suppress non-stable features is well-explained.\n\nMain Concerns:\n\n1. Clarity and Justification of Structural Causal Modeling (SCM):\n\nThe design of the Structural Causal Model (SCM) is briefly shown in Figure 1 but lacks sufficient justification and explanation. It remains unclear how the variables in the causal graph are defined and how interventions are operationalized in practice.\n\nFigure 3 refers to “causal” and “non-causal” components based on DCT frequency bands. However, the distinction appears heuristic and lacks empirical or theoretical justification. How confident can we be that mid-frequency bands capture \"causal\" structures while high/low frequencies are \"non-causal\"? More concrete illustrations or validation would be helpful.\n\n2. Causal Feature Extractor as Frequency Filtering:\n\nThe Causal Feature Extractor appears to perform a kind of adaptive frequency-domain filtering with randomized augmentation. While it is framed as causal intervention, the current formulation is arguably closer to frequency-based data augmentation or regularization. The causal interpretation should be further grounded or clarified.\n\n3. Comparison with Standard Augmentations:\n\nWhile the causal feature extractor is shown to improve robustness, it's not sufficiently compared with more standard or learned data augmentation techniques (e.g., RandAugment, CutMix, or recent frequency-based domain generalization approaches). This weakens the claim that causal modeling offers unique benefits.\n\n4. Similarity to Existing Architectures:\n\nThe GT Fusion module resembles prior cross-attention mechanisms such as Q-former, especially the use of street features as queries and BEV features as key/value. The novelty in the geometric fusion design seems marginal unless better distinguished from prior works.\n\n5. DA Pooling Contribution is Incremental:\n\nThe DA Pooling module combines standard pooling types (max, average, and geometric mean) with gating. This is a reasonable design choice but seems incremental in isolation. Ablation results show marginal gain; a clearer motivation or design rationale could strengthen its necessity.\n\n6. Reproducibility and Clarity of Presentation:\n\nSeveral parts of the method, especially loss terms and the frequency masking strategy, are mathematically specified but not intuitively explained. More illustrations and conceptual diagrams would help readers understand the pipeline.\n\n7. The paper title is different from the title in the Openreview system. \n\n8. Cross-view matching. It would be test on drone-view datasets, like University-1652 and Vigor.", "was_revised": true, "final_justification": "Causality part still does not convince me. \nThe cause is not well-defined. \nThe association is not intuitive. \n\nI can understand that we need to remove the noise, and do the augmentation. But I do not think it needs  ``causality'' to explain it. So it is somehow over-claimed. \n\nIf we only consider the noise and augmentation, the proposed module is not new.", "rebuttal": "## Response to Reviewer 3 (R3)\n\nWe thank the reviewers for their time and valuable feedback. We appreciate the thoughtful comments and constructive suggestions, which help us improve the clarity and depth of our work.\n\n### **Q1.** Regarding the SCM:\n\n**A1:.**  We thank the reviewer for this important question. We provide detailed clarification of our SCM design and empirical validation of frequency-based separation.\n\n**(a) SCM Design:** Our SCM variable design is not ad hoc, but follows established causal modeling principles. Specifically, we draw from *Visual Representation Learning through Causal Intervention for Controllable Image Editing* (CVPR 2025) and *Causal Representation Learning for Domain Generalization* (CVPR  2022), both of which decompose visual data into causal (semantic content) and non-causal (style or domain-specific) factors. Guided by this framework and the characteristics of street image, we construct the SCM in Figure 1.\n\n​\tOur Structural Causal Model (SCM) is constructed based on the following assumptions:\n\n- The street image $X_s$ is mainly generated from two sources: the semantic content $C$ and a domain confounder $D$ (e.g., background, lighting). This is denoted as $C \\rightarrow X_s \\leftarrow D$. \n- The content $C$ includes both discriminative and non-discriminative information. Together with $D$, the non-discriminative part contributes to the generation of non-causal features $F_n$ via the path $D \\rightarrow F_n \\leftarrow C$. The proposed **CFE** module attempts to introduce frequency-domain perturbations to part of the non-causal components, which are composed of the non-discriminative part of $C$ and $D$.\n- The causal features $F_c$, on the other hand, are derived from the discriminative part of $C$ through $C \\rightarrow F_c$.\n- Full representation $Z$ consists of both components influencing prediction $Y$: $Z \\rightarrow Y$\n\nThe overall cross-view matching process is expressed as $X_a \\rightarrow f(x) \\rightarrow Y \\leftarrow f(x) \\leftarrow X_s$, where $X_a$ denotes the aerial image.  In this context, the SCM formulation $Z \\rightarrow Y$ is a causal abstraction of the model’s computational path $X_s \\rightarrow f(X) \\rightarrow Y$. \n\n**(b) Frequency-Based Separation Justification:** Our frequency separation is grounded in prior work [7] showing that domain-specific (non-causal) signals often reside in extremely low and high frequency bands, while mid-frequency components tend to preserve structure-relevant, discriminative information. To validate this empirically, we conducted ablation studies perturbing different frequency bands. As shown in Table I, perturbing mid-frequency components significantly degrades performance compared to our default setting, confirming that mid-frequencies encode causal signals crucial for localization while extreme frequencies contain non-causal information.\n\n**Table Ⅰ.** Performance comparison when perturbing mid-frequency components instead of non-causal regions. \n\n|                               |      | CVACT_val-C-ALL |           |           |           |\n| ----------------------------- | ---- | --------------- | --------- | --------- | --------- |\n| **Frequency Band Perturbed**  |      | R@1             | R@5       | R@10      | R@1%      |\n| low and high frequency (Ours) |      | **88.68**       | **95.58** | **96.66** | **98.49** |\n| Middle-frequency              |      | 87.91           | 94.83     | 95.92     | 98.20     |\n\n### **Q2.** Regarding the Connection Between CFE and Causal Intervention:\n\n**A2:** We thank the reviewer for these important questions. We clarify that our CFE is not merely frequency-based or randomized augmentation, but a principled causal intervention module grounded in our SCM. Our SCM identifies the back-door path $C \\rightarrow X_s \\rightarrow f(x) \\rightarrow Y$, where $C$ denotes spurious domain confounders. The CFE leverages the separability of non-causal factors in the frequency domain, introducing controlled perturbations to these components and applying supervision loss to align geometry-enhanced features with perturbed features. This alignment reduces the model's reliance on spurious correlations, following the back-door adjustment strategy in causal inference.\n\nTo demonstrate CFE's effectiveness, we compared it against standard data augmentation techniques. As shown in Table-II below, our method consistently outperforms RandAugment and CutMix across CVACT_val-C-ALL, confirming that CFE offers more than regularization effects. Regarding frequency-based domain generalization methods, most recent works (e.g., DFF and PCNorm) do not provide official implementations, making fair comparison challenging within our experimental timeline. We prioritized reproducible baselines to ensure reliable evaluation. This validates that our causally-motivated intervention provides unique benefits beyond conventional augmentation strategies.\n\n**Table Ⅱ.**  Comparison of CFE with standard data augmentation methods on CVACT_val-C-ALL dataset\n\n|             |      | CVACT_val-C-ALL |           |           |           |\n| ----------- | ---- | --------------- | --------- | --------- | --------- |\n|             |      | R@1             | R@5       | R@10      | R@1%      |\n| Ours        |      | **88.68**       | **95.58** | **96.66** | **98.49** |\n| CutMix      |      | 88.05           | 95.21     | 96.42     | 98.36     |\n| RandAugment |      | 87.95           | 95.20     | 96.13     | 98.28     |\n\n### **Q3.** Regarding the design of GT Fusion.\n\n**A3:** We appreciate the reviewer’s observation. While our GT Fusion adopts a cross-attention structure, it fundamentally differs from models like Q-former, which use learnable queries for semantic alignment. In contrast, we use real street features to attend to BEV features for geometric alignment, tailored to cross-view localization. Beyond the attention itself, our design incorporates (1) BEV-Specific Robustness: We address BEV feature noise from geometric warping through dedicated DDF module, unlike standard cross-attention designs; (2) Task-Driven Design: We employ depthwise convolution and spatial attention (OSR) before fusion, specifically designed for extreme viewpoint alignment in geo-localization while reducing computational overhead;\n\n### **Q4.** Regarding the ablation study of DA pooling.\n\n**A4:**  We thank the reviewer for this point. Our DA Pooling addresses standard pooling limitations in cross-view tasks: average pooling oversmooths discriminative features, max pooling is noise-sensitive, and GeM pooling applies uniform behavior across all locations despite offering learnable adjustment.\n\nOur DA Pooling uses adaptive gating to dynamically balance max, average, and GeM pooling based on input content. This is crucial for extracting semantic representations from features enhanced by our causal and geometric strategies—inappropriate aggregation can diminish enhancement benefits. As shown in Table IV, DA Pooling consistently outperforms fixed pooling methods, validating its necessity for our enhancement framework.\n\n**Table Ⅳ.** Ablation study of different pooling strategies on the CVACT dataset.\n\n| Method                | CVACT_val |           |           |           | CVACT_test |           |           |           |\n| --------------------- | --------- | --------- | --------- | --------- | ---------- | --------- | --------- | --------- |\n|                       | R@1       | R@5       | R@10      | R@1%      | R@1        | R@5       | R@10      | R@1%      |\n| **DA pooling (Ours)** | **91.61** | **96.93** | **97.72** | **98.77** | **73.03**  | **93.03** | **94.81** | **98.63** |\n| Gem                   | 89.24     | 95.64     | 96.65     | 98.50     | 69.78      | 92.18     | 93.78     | 98.36     |\n| Avg                   | 91.19     | 96.52     | 97.33     | 98.72     | 72.02      | 92.52     | 94.51     | 98.52     |\n| Max                   | 90.55     | 96.51     | 97.25     | 98.70     | 70.78      | 92.20     | 94.23     | 98.44     |\n\n### **Q5.** Regarding the frequency masking and loss strategy:\n\n**A5:** We thank the reviewer for the suggestion and will include clearer illustrations in the final version. Our CFE  first apply DCT to convert the image into the frequency domain, where low to high frequencies are spatially distributed (low frequencies lie in the top-left and high frequencies in the bottom-right.). Then, our content-aware mask constructs three concentric circular masks with initial radii of 0.1, 0.3, and 0.6, dividing the frequency spectrum into four regions. These radii are linearly increased based on image gradient magnitude (via Sobel operator), so that images with stronger gradients preserve more mid-frequency components, enabling better retention of causal information. Larger radii correspond to stronger Gaussian perturbations in outer frequency bands.  For losses, we add two auxiliary losses: (1) contrastive loss between street and BEV features for geometric pre-alignment, and (2) contrastive loss between causally enhanced and fused features for causal supervision. Both use small weights to guide training.\n\n### **Q6.** Regarding additional datasets:\n\n**A6:** We thank the reviewer for this comment. We have provided VIGOR evaluation results in Table 6 of the appendix (omitted here due to space limits). For University-1652, since this dataset uses drone-view inputs and our method relies on BEV features generated via geometric transformation from ground-level street images, we cannot directly apply our framework to drone-view inputs. On VIGOR, our method ranks second among existing approaches. The performance gap is attributed to VIGOR's unique challenges: the one-to-many retrieval protocol and street images with high density of dynamic objects that create noisy BEV representations, degrading geometric cue effectiveness. We plan to explore VIGOR-specific adaptations in future work."}, {"review_id": "thLcDvjKZf", "reviewer": "Reviewer_Pc7t", "summary": "This paper presents a novel framework for cross-view geo-localization. The primary goal is to improve matching accuracy between street-level and aerial images, especially under challenging real-world conditions.  The authors introduce three main components, namely GT Fusion, CFE and DA-Pooling module for this task, and the proposed method is evaluated on several standard and corruption-augmented benchmarks, where it demonstrates state-of-the-art performance, particularly in robustness against visual corruptions.", "questions": "1. The paper's central claim is predicated on a causal framework, yet the connection between the SCM (Figure 1) and the CFE is not explicit. Please bridge this gap: How does the do()-operation translate to Equation 1? Why is this a principled causal intervention?\n\n2. Could you provide a comparative analysis of inference time and computational overhead against baselines and other top methods?\n\n3. The CFE relies on the heuristic that factors are separable in the frequency domain. Could you provide a more rigorous analysis or empirical validation for this assumption?\n\nIf the authors can convincingly address them, It would be likely that I raise my score.", "limitations": "Yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths\n\n- **Originality**: The introduction of a causal learning paradigm to CVGL is a significant conceptual contribution, establishing a new research avenue by framing robustness as the mitigation of confounders.\n\n- **Significance & Quality**: The extensive experimental evaluation shows compelling state-of-the-art results, especially on corruption-augmented datasets, providing robust empirical evidence for the proposed components' efficacy.\n\n- **Clarity**: the manuscript is generally well-written.\n\n# Weaknesses\n\n- The paper's central claim of applying causal inference seems undermined by the tenuous link between the formal SCM and the CFE's implementation. The manuscript does not establish how the CFE's frequency-filtering constitutes a principled do()-intervention. Consequently, the method appears to be an effective frequency-based augmentation presented with causal terminology, rather than a rigorous application of causal inference.\n\n- The CFE operates on an simplified heuristic that causal and non-causal features are separable in the frequency domain (lines 139-142) with only qualitative supports. The paper lacks substantive analysis to validate this critical assumption, leaving the CFE's foundation to appear more ad-hoc than theoretically grounded and thereby undermining its claim to causality.\n\n- The manuscript omits analysis of inference time and computational cost, precluding an assessment of the method's practical viability for applications like autonomous navigation.", "was_revised": true, "final_justification": "the rebuttal has successfully addressed my most critical technical and empirical questions. The reasons to accept now outweigh the remaining minor concerns about the framing of the causal claims. I have therefore raised my score.\n\nInitially, I had three main reservations: (1) the tenuous link between the formal causal model (SCM) and the implementation of the Causal Feature Extractor (CFE); (2) the lack of empirical validation for the key assumption that causal and non-causal features are separable in the frequency domain; and (3) the absence of a computational cost analysis.\n\nThe authors have successfully resolved concerns (2) and (3) by providing new experimental results.\n- The new data provided in Table I of the rebuttal demonstrates that the proposed method achieves its state-of-the-art accuracy with only marginal computational overhead compared to key baselines like EP-BEV. This fully addresses my concern about the method's practical viability.\n- More importantly, the new ablation study presented in Table II provides strong empirical validation for the CFE's design. By showing that performance drops when perturbing the mid-frequency bands (which are assumed to be causal), the authors give significant credence to their heuristic. This moves the CFE's design from being seemingly ad-hoc to being an empirically grounded and well-motivated engineering choice.\n\nRegarding the primary concern about the causal framework, the authors' rebuttal clarifies their reasoning significantly. While I still find the link to the formal \"back-door adjustment\" principle to be more analogical than rigorous, the authors have made it clear that their work is guided by causal *thinking* to motivate a novel data augmentation and feature consistency scheme. The framing could be more cautious, but the underlying idea is novel for this domain and, as shown by the new ablation, empirically sound.", "rebuttal": "## Response to Reviewer 4 (R4)\n\nWe thank the reviewers for their time and valuable feedback. We appreciate the thoughtful comments and constructive suggestions, which help us improve the clarity and depth of our work.\n\n### **Q1.** Regarding the SCM and causal intervention details.\n\n**A1:**  We thank the reviewer for raising this important question. Our paper’s causal framework is built on a Structural Causal Model (SCM) that formally characterizes how spurious correlations may arise in cross-view image matching. Below, we clarify the components of the SCM, how it connects to our CFE module, and why our methods corresponds to a principled causal intervention.\n\n​\t**SCM Overview.**\n​\t\tAs illustrated in Figure 1, our SCM is grounded on the following assumptions:\n\n- The street image $X_s$ is mainly generated from two sources: the semantic content $C$ and a domain confounder $D$ (e.g., background, lighting). This is denoted as $C \\rightarrow X_s \\leftarrow D$. \n- The content $C$ includes both discriminative and non-discriminative information. Together with $D$, the non-discriminative part contributes to the generation of non-causal features $F_n$ via the path $D \\rightarrow F_n \\leftarrow C$. The proposed **CFE** module attempts to introduce frequency-domain perturbations to part of the non-causal components, which are composed of the non-discriminative part of $C$ and $D$.\n- The causal features $F_c$, on the other hand, are derived from the discriminative part of $C$ through $C \\rightarrow F_c$.\n- The full feature representation $Z$ of the street image in the model consists of both causal and non-causal features, which together influence the final prediction $Y$ through $Z \\rightarrow Y$, $Y$ is the label indicating whether the two views match. \n\nThe overall cross-view matching process is expressed as $X_a \\rightarrow f(x) \\rightarrow Y \\leftarrow f(x) \\leftarrow X_s$, where $X_a$ denotes the aerial image.  In this context, the SCM formulation $Z \\rightarrow Y$ is a causal abstraction of the model’s computational path $X_s \\rightarrow f(X) \\rightarrow Y$. \n\n​\t**Causal Intervention via Back-door Adjustment.**\n\n​\t**To avoid confusion, we clarify that in this context, $C$ denotes confounding variables that influence the generation of street-view images $X_s$.**\n\n​\tOur core objective is to reduce spurious correlations by mitigating the influence of non-causal factors through causal intervention. As explained in our SCM, we aim to intervene on the non-causal components of the street image $X_s$, in order to weaken their impact along the spurious path $C \\rightarrow X_s \\rightarrow f(x) \\rightarrow Y$. To achieve this, we adopt a **do-operation** that is concretely implemented by our **CFE module**, formalized in Equation 1.\n\nSpecifically, prior work ([7]) has revealed that non-causal factors in natural images tend to exhibit identifiable distributions in the frequency domain. Based on this observation, we design the CFE module to carry out causal intervention directly on the frequency components of $X_s$. The procedure is as follows:\n\n (1) We first transform the input street image $X_s$ into the frequency domain using DCT;\n\n (2) A content-aware mask is then applied to separate the frequency components into causal and non-causal parts;\n\n (3) We perturb only the non-causal frequency components, then add them back to the causal components;\n\n (4) Finally, we apply inverse DCT to obtain a causally enhanced image $X_s^*$.\n\nConsequently, the perturbation applied to the non-causal components, expressed as $\\text{do}(X_s := X_s^*)$, is concretely instantiated in Equation 1, which formalizes the design of our CFE module.\n\nAs for **why this constitutes a principled causal intervention**: in SCM, causal intervention requires directly manipulating one or more variables to disrupt the direct path ($C \\rightarrow X_s \\rightarrow f(X) \\rightarrow Y$). In our case, the CFE module applies targeted perturbations to the non-causal components of $X_s$, and further introduces a supervision loss that encourages the fusion feature to align with the causally-enhanced one. This directly intervenes on the path $C \\rightarrow X_s \\rightarrow f(X) \\rightarrow Y$, aligning with the **back-door adjustment** principle in causal inference, and thus qualifies as a principled causal intervention.\n\n**Supplement: Front-door and Back-door Adjustment.**\n\n- **Front-door adjustment** is used when non-causal factors (confounders) are *unobserved*. In such cases, an intermediate variable $M$ is introduced to mediate the causal effect, forming pathways such as $C \\rightarrow X \\rightarrow M \\rightarrow Y$ or $C \\rightarrow M \\rightarrow X \\rightarrow Y$. Intervening on $M$ can help isolate the causal effect of $C$ on $Y$ despite the hidden confounder.\n- **Back-door adjustment** applies when the confounders are *observable or controllable*. In our setting, since we define and perturb the non-causal components  explicitly, we fall into the latter case. We directly perturb the back-door path $C \\rightarrow X \\rightarrow Y$ by intervening on the non-causal factors—this aligns with the classical back-door criterion.\n\nIn both cases, $C$ denotes a non-causal information, and our intervention aims to suppress the influence of spurious pathways on the final prediction $Y$.\n\nWe will clarify this explanation further in the final version.\n\n### **Q2.** Regarding the train and inference time.\n\n**A2:**  We thank the reviewer for the valuable suggestion. Following this recommendation (also raised by Reviewer 2), we conducted a comparative analysis of computational cost and inference speed against representative baselines.\n\n​\tAs shown in **Table Ⅰ**, we report both GFLOPs and average inference time (in milliseconds per batch of 128 images) on the **CVACT_val** set. The results demonstrate that our method achieves the best R@1 accuracy with only marginal computational overhead compared to other methods.\n\n**Table Ⅰ**. Comparison of GFLOPs and average inference time per batch (batch size = 128) on CVACT_val. Avg inference time (ms) indicates the mean time to process one batch.\n\n| Method     | GFLOPs    | Avg inference time/ms | CVACT_val R@1 |      |\n| ---------- | --------- | --------------------- | ------------- | ---- |\n| Sample4Geo | **90.54** | **2367.55**           | 90.81         |      |\n| EP-BEV     | **90.54** | 2396.53               | 90.61         |      |\n| Ours       | 90.56     | 2374.76               | **91.61**     |      |\n\nWe emphasize that our method maintains comparable inference efficiency to lightweight models (Sample4Geo, EP-BEV), while achieving consistently better accuracy. This confirms the effectiveness of our design in balancing accuracy and computational cost.\n\n### **Q3.** Regarding the definition of causal and non-causal factors on the frequency spectrum.\n\n**A3:**  We appreciate the reviewer’s thoughtful question. Rather than relying on a heuristic assumption, in Line 40 of our paper, we cite findings that the extremely high and low frequency components in the image often contain the majority of non-causal (domain-specific) information [7]. In contrast, the mid-frequency components typically preserve semantic structures like building shapes and road layouts. In our task, we observe that these structures are precisely the key cues for cross-view matching. Therefore, we follow prior empirical observations to motivate the frequency-domain operation in our CFE module, aiming to preserve such causal information while perturbing the non-causal parts. To make this assumption more transparent, we will include in the final version additional visualizations that highlight the separability of causal and non-causal factors in the frequency spectrum.\n\n​\tWe first apply the Discrete Cosine Transform (DCT) to convert the input image into the frequency domain, where ultra-low frequencies lie in the top-left corner and ultra-high frequencies in the bottom-right. To isolate mid-frequency content, we employ a content-aware mask module that constructs several concentric circular masks with radii dynamically adjusted based on the gradient magnitude of each image. After masking, the causal and non-causal frequency components are processed separately: the non-causal components are perturbed with additive noise, while the causal components remain untouched. Both parts are then summed and transformed back to the spatial domain via inverse DCT.\n\n​\tWe verify this frequency-domain separability assumption through an ablation study by perturbing the mid-frequency bands instead of the high/low ones. As shown in **Table Ⅱ**, this results in a performance drop, indicating that mid-frequency regions are more likely to encode causal (task-relevant) features. This observation aligns with our core motivation: to enhance causal representation by perturbing frequency regions that predominantly contain non-causal signals.\n\n**Table Ⅱ.** Performance comparison when perturbing mid-frequency components instead of non-causal  regions. The results show a noticeable performance drop, validating that mid-frequency regions encode causal information.\n\n|                               |      | CVACT_val-C-ALL |           |           |           |\n| ----------------------------- | ---- | --------------- | --------- | --------- | --------- |\n| **Frequency Band Perturbed**  |      | R@1             | R@5       | R@10      | R@1%      |\n| low and high frequency (Ours) |      | **88.68**       | **95.58** | **96.66** | **98.49** |\n| Middle-frequency              |      | 87.91           | 94.83     | 95.92     | 98.20     |"}]

Author rebuttal:

Meta-review: This paper aims to address the cross-view geo-localization task.  Authors propose a Causal Learning and Geometric Topology (CLGT) framework, which consists of two novel architectural contributions - a causal feature extractor to focus on stable and task-relevant features, and GT fusion to efficiently inject the Bird’s Eye View (BEV) feature with road topology. The paper also proposes data-adaptive pooling, which outperforms conventional global max pooling, global average pooling, and adaptive pooling on the task at hand.  

The rebuttal successfully addresses several queries of the reviewers related to causal features extractor, normal cross-attention based fusion, data-adaptive pooling, hyper-parameters tuning  and more recent baselines.

As recommended by reviewers, authors are encouraged more qualitative results and thorough error analysis. 
AC recommends paper acceptance
Decision: Accept (poster)