BevSplat: Resolving Height Ambiguity via Feature-Based Gaussian Primitives for Weakly-Supervised Cross-View Localization
Authors: Qiwei Wang
Wu Shaoxun
Yujiao Shi
Venue: NeurIPS 2025 spotlight ()
Keywords: Cross-view Localization; Weakly-supervised
Primary area: applications

Abstract: This paper addresses the problem of weakly supervised cross-view localization, where the goal is to estimate the pose of a ground camera relative to a satellite image with noisy ground truth annotations. A common approach to bridge the cross-view domain gap for pose estimation is Bird’s-Eye View (BEV) synthesis. However, existing methods struggle with height ambiguity due to the lack of depth information in ground images and satellite height maps. Previous solutions either assume a flat ground plane or rely on complex models, such as cross-view transformers.
We propose BevSplat, a novel method that resolves height ambiguity by using feature-based Gaussian primitives. Each pixel in the ground image is represented by a 3D Gaussian with semantic and spatial features, which are synthesized into a BEV feature map for relative pose estimation.
We validate our method on the widely used KITTI and VIGOR datasets, which include both pinhole and panoramic query images. Experimental results show that BevSplat significantly improves localization accuracy over prior approaches.

BevSplat: Resolving Height Ambiguity via Feature-Based Gaussian Primitives for Weakly-Supervised Cross- Pross-View Localization
Qiwei Wang'. Shaoxun Wu', Yujiao Shi!
Tean University, Shanghai, China. Correspondence to: Yujiao Shi <shiyj2 @ whwn@@hhthicccchccu..c.m
Abstract
This paper addresses the problem of weakly supervised cross-view localization, where the goal is to estimate the pose of ground camera relative to satellite image with noisy ground truth annotations. A common approach to bridge the cross-view domain gap for pose estimation is Bird's-Eye View (BEV) synthesis. However, existing methods struggle with height ambiguity due to the lack of depth information in ground images and satellite height maps. Because a single 2D pixel could represent points at various depths and heights. its true 3D position is anbiguous. Previous solutions either assume a flat ground plane or rely on complex models, such as cross-view transformers. We propose BevSplat, a novel method that resolves height ambiguity by using feature-based Gaussian primitives. Each pixel in the ground image is represented by 3D Gaussian with semantic and spatial features, which are synthesized into BEV feature map for relative pose estimation. We validate our method on the widely used KITTI and VIGOR datasets, which include both pinhole and panoramic query images. Experimental results show that BevSplat significantly improves localization accuracy over prior approaches. Our code is available at
Inotroduction
Cross-view localization, the task of estimating the pose of ground camera with respect to a satellite acrial image, is critical problem in computer vision and remote sensing. This task is especially important for applications such as autonomous driving, urban planning, and geospatial analysis, where accurately aligning ground-level and satellite views is crucial. However, it presents significant challenges due to the inherent differences in scale, perspective, and environmental context between ground-level images and satellite views.
To navigate these complexities, particularly the common difficulty of acquiring precise ground- truth (GT) camera locations at scale, weakly supervised learning [1 2 has recently emerged as a promising paradigm. this setting, models are trained using only noisy annotations, such as approximate camera locations with errors potentially reaching tens of meters, which adds another layer of complexity to the task. Nevertheless, the primary advantage of weak supervision lies in data collection, making it more scalable and practical
its ability to leverage less approach for many real-world applications where extensive precise annotations are infeasible.
A key strategy to address cross-view localization is Bird's-Eye View (BEV) synthesis 35 6) which generates bird's-ey s-eye view representation from the ground-level image. The BEV image can then be compared directly to satellite image, facilitating relative pose estimation. However, existing methods often rely on Inverse Perspective Mapping (IPM), which assumes a flat ground plane [ 6
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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BevSplat
Ingut Ground Image
Reconstruction
IPM Projection
BevSplal Projection
BelSplal
Ingur Image
Reconsmaction
Projection
BevSplat Projection
Map
Figure 1: Our BevSplat cross-view localization process begins by using depth prediction network on single ground-level image to acquire its depth map. This depth map then employed for 3D reconstruction into Gaussian splats, which are finally projected to a Bird's-Ey s-Eye View (BEV). In comparison to the Inverse Perspective Mapping (IPM) approach, our method demonstrates improved recovery of BEV curves, more effective handling of building occlusions, and enhanced practical localization performance.
or on tigh-coomlexity models like cross-view transformers 3 to address height ambiguity, the challenge of resolving the elevation difference between the ground and satellite views.
The flat terrain assumption used IPM leads the loss of critical scene information above the ground plane and introduces distortions for objects farther from the camera, as shown in Fig. [ On the other hand, while cross-view transformers are effective handling distortions and objects above the ground plane, they are computationally expensive. Furthermore, in weakly supervised settings, noisy ground camera pose annotations provide weak supervision, making it difficult for tigh-complexity models like transformers to converge []. ultimately leading to suboptimal localization performance.
In this paper, we propose BevSplat address these challenges. BevSplat generates feature-based 3D Gaussian primitives for BEV synthesis. Unlike previous 3D Gaussian Splatting (3DGS) (7] methods that rely on color-based representations, we represent each pixel in the ground-level image as a 3D Gaussian with semantic and spatial features. These Gaussians are associated with attributes such as position 3D space, scale, rotation, and density, which are synthesized a visibility-aww aware rendering algorithm that supports anisotropic splatting. This approach enables us to handle height ambiguity and complex cross-view occlusions, improving the alignment between the ground-level image and the satellite view for more accurate pose estimation, without the need for expensive depth sensors or complex model architectures.
BEV feature map using
We validate our approach on the widely used KITTI and VIGOR datasets, where the former localizes images captured by pin-hole cameras, and the latter aims localize panoramic images, demonstrating that the proposed BevSplat significantly outperforms existing techniques in terms of localization accuracy in various localization scenarios.
2
Related Work
2.1 Cross-view Localization
Cross-view localization, which aligns ground-level images with satellite imagery, has evolved from image retrieval to fine-grained pose estimation. Early approaches framed this as an image retrieval task, using metric learning to match ground queries to satellite image slices [8H12). While modern transformers have improved retrieval performance, practical application remains challenging [4] Many recent methods adopt coarse-to- tone--to-fneeee pipeline. This typically involves coarse retrieval step 15] followed by fine- fine-grained (pixel-level) localization identify the precise camera pose 20 22). A key limitation of these methods is their reliance on precise, GPS-base based training data, which is often prone to inaccuracies. To overcome this, weakly supervised settings have been proposed to learn from noisy pose annotations []] 2 These weakly supervised settings differ their assumptions.
16
18 3 19
5
(2 assumes the availability of GT labels in a source domain and access to cross-view pairs in the target domain. In contrast, addresses more challenging scenario where

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source domain GT labels are unavailable and no target domain pairs are accessible. In this work, we tackle the same task setting as T
2.2 B-rd View Synthesis
BEV synthesis, which generates bird's-eye view images from ground-level perspectives, has been widely applied to cross-view localization. While LiDAR and Radar sensors offer high aceuracy for localization tasks (23 their high cost limits their use. For camera-only systems, multi- camera setups commonly employed (27 primarily focusing tasks like segmentation and recognition. In localization, methods like Inverse Perspective Mapping (IMP) assume a flat ground plane for BEV synthesis Transformer- based models address these challenges but struggle with weak supervision and noisy pose annotations (35)] While methods such as 33 32 also employ feature Gaussians for the image- to-BEV transformation, they typically benefit from rich depth and semantic information afforded by multi-sensor setups. While effective in some contexts, they face limitations in resource-constrained real-world scenarios. In stark contrast, our approach is constrained to rely exclusively on weakly supervised signals derived purely from images, presenting a considerably more challenging task.
6 20 18 which can be overly simplistic for complex environments.
2.3 Sparse- View 3D Reconstruction
In our method, we adopt algorithms similar to 3D reconstruction to represent ground scenes. Sparse- view 3D reconstruction has been major focus of the community. Nerf-based approaches [33] and their adaptations (34) have shown the potential for single-view 3D reconstruction, though their application is limited by small-scale scenes and high computational cost. Recent works using diffusion models (35 and 3D Gaussian representations [7] [8), as well as transformer- and
Gaussian- -ased models 41 42, have achieved sparse-view 3D reconstruction on larger scale, but the complexity of these models still restricts their use due to computational demands. Approaches like 45 leverage pre-trained models directly generate Gaussian primitives, avoiding the limitations of complex models while enabling scene reconstruction from sparse views. We apply such methods to single-view reconstruction, achieving high-accuracy cross-view localization.
3 Method
In this paper, we address cross-view localization by aligning ground-level and satellite images under weak supervision, where initial ground camera locations are only approximate. Our objective is to accurately estimate camera pose from these noisy priors by leveraging Gaussian primitives, which effectively manage height ambiguity and enable efficient generation of Bird's-Ey View (BEV) feature maps. First, we employ orientation prediction network analogous to that in G2SWeakly to align the orientations the ground and satellite images. Subsequently, our BevSplat method lifts the ground image to 3D (Section 3.I) and projects the corresponding Feature Gaussians into the BEV perspective to render the ground view BEV features (Section F.nally these features are compared against the satellite features by computing similarity score (Section 3.2.2)
3.1 Geometric Gaussian Primitives Generation
Inspired by 3D Gaussian Splatting (3DGS) [7]. we represent the 3D scene as a collection of Gaussian primitives. Our generation process first establishes their initial geometry and appearance. Given the inherent difficulty of directly learning accurate depth in our weakly supervised framework, we utilize pre-trained depth estimation model to predict per-pixel depth D; from the ground-level image. The initial 3D coordinate μi for primitives associated with each pixel (w, )) then determined from D; and the specific camera model.
For pinhole cameras, μ4 is computed by back 2D image coordinates (u,, uz) using depth D, and camera intrinsics K as μ4 K- ''' [. U4 1]"".
For panoramic cameras, where pixel coordinates (u,, v2) represent viewing angles (e.g., azimuth u, and polar angle v;), the initial 3D coordinate 14 is obtained by scaling the depth D; along a unit direction vector d; = [r,, Vi, =1{". The components are defined as:
z sin(vi, (v.) cos(u)) 3/1 = -cos(e)), 2, sin(v.) sin(u:).
(I)
3

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Weights
Depth Model
Caussians Means
Festure Gaussaas
Weighes
Gradient- Free
BEV Confidence
Gradient braddenl-bused
Query Ground Image
ReaNet
Ax,dy,áz
BEV
Scales
Render
MLP
BEV
Opacities
Dino DPT
Feature Confidence
Reference Satellite Image
Satellite Feature Fs
Localization Result
Dino DPT
Similarity Matching
Figure 2: BevSplat Framework Overview. Query ground image Gaussian primitive initialization involves: (1) A pre-trained depth model for initial 3D positions (J:). (2) A ResNet and MLP to predict offsets (Aps), scale (S rotation (R&), and opacity (Ox). (3) A DPT-fine-tund DINOv2 for extracting semantic features (f,) and confidences (c²), which are then bound to these Gaussians. These feature Gaussians are subsequently rendered into BEV feature and confidence maps. Satellite image features extracted using identical DINOv2-DPT backbone (note: weights shared for KITTI but differ for VIGOR, similar to G2SWeakly ().. Localization is achieved by matching satellite features with the rendered query BEV features via cosine similarity within sliding window.
The initial 3D coordinate is thus μu = D,d,.
Subsequently, ResNet 446 extracts local features fixe for each pixel from the ground-level image. These features serve as input to a multi-layer perceptron (MLP (47]), denoted Fye, which predicts attributes for N2 3 distinct Gaussian primitives originating from each pixel. The predicted attributes for each of these Np Gaussian primitives include positional offsets Ap& = (Ar Ayk. A relative /ı;, anisotropic scale Sx, rotation quaternion R,, and opacity value O₃- Predicting multiple primitives per pixel enhances single-image representation density. The collection of parameters G; for these No primitives from pixel i, incorporating their final 3D positions, is:
G, = (SS,, RR, Ou, + ATK, 14 + Ayke us + Azz)
(2)
Here, k indexes the Np primitives associated with that pixel, the set of parameters {SS, Re, OR, AZk, Ayk, ) is the direct output of Fgs and flee is the ResNet feature vector for pixel This process yields an initial set of geometric and appearance-based Gaussian primitives. These primitives are subsequently enriched with semantic features for localization, as detailed next.
3.2 Feature-based Gaussian Primitives for Relative Pose Estimation
we fine-tune pre-trained DINOv2 49 model augmented with a DPT (50) module. From the ground image, this pipeline yields a feature map F, E H xC and a confidence map C2 E R x1 The confidence map Cg- derived from F, via an additional convolutional layer and a sigmoid activation, assigns lower weights to dynamic objects (e.g., vehicles) and higher weights to static elements (e.g., road surfaces), indicating feature reliability for localization. For the predominantly static satellite image, we solely extract its feature map F. E HRH..W.CC.
For robust semantic feature extraction from ground and satellite images. inspired by 4%
3.2.1 BEV Feature Rendering
The extracted ground features F, and confidences C are then bound to the Gaussian primitives generated as described in Section Specifically, each of the Np 3 Gaussian primitives originating

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from a ground image pixel is augmented with the corresponding per per-pixel feature vector fü (sampled from Fg) and confidence score c; (from C₁). All N, primitives derived from the same pixel thus share identical f; and 6; values, effectively embedding semantic information and its reliability into the 3D representation.
Next, viewing the scene from a BEV perspective (camera directed downwards), the features f. and confidences cs bound to each Gaussian primitive are rendered onto 2D plane. This differentiable a-blending process, analogous to RGB rendering in 3DGS 77]. yields the BEV feature map F BEV and confidence map CBEV:
No F BEV = Σ TooTT, CBEV =
No
(3)
b=1
where primitives b E Ng) are sorted by depth from the BEV camera. Here, T3 Ng is the total number of ground-image Gaussian primitives, or is the opacity of primitive b, and fa, Cb are its bound feature vector and confidence score (inherited from its source pixel), respectively,
II
3.2.2 Pose Estimation via Confidence-Guided Similarity Learning
The location probability map P(u, v), representing the similarity between satellite image features Fs Fs(u, and confidence-weightee ground BEV features CBE FEV, is computed as:
(F v), CBEVEBEV)
P(u, v) (u, w)ll [CBEyF
Here, F, denotes the satellite image features, while F BEV and CBE are the BEV features and confidence map derived from the ground image, respectively. ||
signifies the L2 norm.
Supervision. Following M a deep metric learning objective supervises the network. For each query ground image, we compute location probability maps: Poss against its positive satellite image and P Pneg.idx against each of the M negative satellite images. The weakly supervised loss W aims to maximize the peak of P pos while minimizing the peak for each Paog.ixx
M
Weakkl
Σ idx=1
log (1 + ex PPeak(P mos.an)) Peaa((P..))) Pro))
(5)
M
where the hyperparameter a (set to 10) controls convergence speed.
If more accurate (though potentially noisy) location labels (x" .²") are available during training (e.g.. GPS with error up to = meters, where is the ground resolution in m/pixel of P pos), an auxiliary loss LGes is introduced:
LGpS PPak(P pos) Peaa((Pool) pos = dd/3,y" dd/3))
(6)
This objective encourages the global peak of P Pos to align with the local peak probability found within the d-meter radius neighborhood of the noisy label (=", ,y").
The total optimization objective then:
Calt LWeakly ArLors,
where A if noisy location labels are utilized during training, and \ = otherwise.
4 Experiments
In this section, we first describe the benchmark datasets and evaluation metrics for evaluating the effectiveness of cross-view localization models, followed by implementation details of our method. Subsequently, we compare our method with of-te--o-thh-arr approaches and conduct experiments to demonstrate the necessity of each component of the proposed method.
KITTI dataset. The KITTI dataset [51 consists of ground-level images captured by a forward-facing pinhole camera with restricted field of view, complemented by acrial images [52), where each
5

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BerSplat Imoage
Map
BevSplat Image
Map
BevSplat Image
Location Map
BevSplar Image
Map
Figure 3: Visualization of the query ground image (up) and the estimated relative pose with respect to the satellite image (bottom right) on VIGOR dataset. The BEV image projected from the query ground image using the estimated Gaussian primitives is presented in the bottom left for each example.
aerial patch covers a ground area of approximately 100 x 100m². The dataset includes a training set and test sets(Same-Aren and Cross-Area). For the Same-Area test set, the test query images are from the same geographical regions as the training set, but are not the same images. For the Cross-Area test set, the test images come from entirely new geographical regions that were not seen during training, testing the model's ability to generalize. The location search range of ground images approximately 56 x 56m², with an orientation noise of 100
VIGOR dataset. The VIGOR dataset [15] includes geo-tagged ground panoramas and satellite images from four US cities: Chicago, New York, San Francisco, and Seattle. Each satellite patch spans 70 70m² and is labeled positive if the ground camera is within its central 1/4 region; otherwise, it is semi-positive. The dataset also has Same-Area and Cross-Area splits: Same-Area uses training and testing data from the same region, while Cross-Area splits training and testing between two separate city groups. We use only positive satellite images for all experiments, following [[].
Evaluation Metrics. For the KITTI dataset [11, we evaluated localization and orientation errors by calculating mean and median errors in meters and degrees, respectively. We also compute recall at thresholds of m and m for longitudinal (along the driving direction) and lateral (orthogonal the driving direction) localization errors, as well as 1 andd 3 for orientation errors. A localization is considered successful if the estimated position falls within the threshold the ground truth, and an orientation is accurate if its error is within the angle threshold. For the VIGOR dataset (15), which does not provide driving direction information, we report mean and median errors as outlined in [
Visualization. We provide visualizations of the query images and localization results in Fig For better clarity, we show the synthesized BEV image generated from our estimated Gaussian primitives at the bottom left of each example (though the model uses BEV feature maps for localization). Further qualitative results, including an analysis of failure cases, are provided in the supplementary material.
Implementation Details. For 3D point cloud generation from ground images, we employ specific depth estimation models: DepthAnything
(53) for the pinhole camera images of the KITTI dataset [51] and UniK3ID (54) for the panoramic images the VIGOR dataset [15]. Our feature extractor for both ground and satellite images is a DINOv2 backbone [49, initialized with FiT weights (48), which is subsequently fine-tuned using an attached DPT module (50). This extractor yields satellite feature maps with dimensions (C, H, W) (32, 128 128). For ground images, initial feature maps of (32, 64, 256) are extracted; these are then projected into BEV using our feature Gaussians projection, resulting in final ground BEV features also of dimensions (32, 128, 128). Our model is trained using the Adam W optimizer [55] (weight decay 10 10-3) and OneCycleLR scheduler (56) with cosine annealing strategy, where the learning rate peaks 6.25 x 10-5, We use a batch size of 8 on a single 4090 GPU, training for 8 epochs on KITTI and 14cf epochs on VIGOR.
4.1 Comparison with State-of-the-Art Methods
We compare our method with the latest state-of -ftt---rttttttttt (SOTA) approaches, including supervised methods such as Boosting 4 VFA (20), CCVPE (57), HC-Net (6, and DenseFlow [18], all of which rely on ground-truth camera poses for supervision. We also compare with G2Sweakly (]. which uses only satellite image and corresponding ground image input, similar to our setup.

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Table I: Comparison with the most recent state-of-the-ar (SOTA) KITTI (# denotes supervised learning algorithms), Our weakly supervised approach slightly outperforms SOTA supervised methods in Cross-Area evaluations.
localization
Lateni
Longitadinal
Azimuth
Algorithms
Test Area
mean(m)] median(m))
d=Imrt
d=3mn
d=Imf
d=3m?
190
3"1 meann" [) mediant '
Boosting 8
12.08
11:42
76.44
96.34
23.54
50.57
99.10
100.00
20
10.74
10.51
51.17
5.19
49.85
96.98
CCVPE 57
1.22
0.62
97.35
98.65
77.13
96.08
77.39
99:47
0.67
0.54
HC-Net 6
0.80
0.50
99.01
92.20
91.35
99.84
0.45
0.33
Same-Area
DenseFlow [8
1.48
0.47
95.47
87.89
89:40
0.49
0.30
G2SWeakly
12.03
8.10
85.74
31.94
99.99
100.00
Ours
5.82
2,85
60.04
91.54
24.06
56.82
99.99
100.00
0.33
0.28
G2SWeakly D
6.81
3.39
66.07
94.22
16:51
49,96
99.99
100.00
0.33
0.28
Ours
2.87
2.06
52.90
94.24
35.62
76.57
99.99
100.00
0.33
0.28
Boosting +
12.58
12.11
57.72
86.77
14.15
34.59
98.98
100.00
VFA P0
11.12
10.95
27.82
5.75
18.42
71.00
3.95
3.03
CCVPE SY]
9.16
3,33
44.06
81.72
23.08
52.85
57.72
92.34
1.55
0.84
HC-Net 0
8.47
4.57
75.00
58.93
33.58
83.78
3.22
Cross-Arca
DenseFlow 18
7.97
3.52
54.19
23.10
43.44
2.17
1.21
G2SWeakly 1
13.87
10.24
62,73
86.53
9.98
29.67
99,99
100.00
0.33
0.28
Ours
7.05
3,22
58.15
92.62
23.08
51.61
99.99
100.00
0.33
0.28
G2SWeakly
12.15
7.16
64.74
34.77
100.00
Ours
6.20
2.51
51.45
95.17
27.41
60.45
99.99
100.00
0.33
0.28
The comparison results on the KITTI dataset [51 are summarized in Table 1 Since our rotation estimator is inherited from G2Sweakly [1. the rotation estimation performance identical between the two methods. However, our method significantly outperforms G2Sweakly []] in terms of location estimation across almost all evaluation metrics, yielding substantial improvements in both longitudinal pose accuracy and the corresponding mean and median errors. This improvement can be attributed to the limitations of the IPM projection method used in G2Sweakly (.. which suffers from distortions in scenes that are far from the camera and fails to capture the details of objects above the ground plane.
KITTI.
Our feature- -ased Gaussian splatting for BEV synthesis effectively addresses these issues, leading to a notable enhancement in localization accuracy. Fig. [ad Fig. 4 visualize the difference between the IPM projection and our proposed BEV synthesis method, clearly demonstrating that our projection technique resolves challenges such as occlusions caused by tall objects (e.g.. buildings, trees, vehicles) and geometric distortions from curved roads. Furthermore, in cross-area evaluations, our method even surpasses supervised approaches (Boosting 44. VFA 20,, CCVPE (57) HC-Net (6, and DenseFlow (18) in terms of mean and median errors, showcasing the strong generalization ability of our approach and highlighting the potential of weakly supervised methods.
It is worth noting that the experimental results for VFA (20 are taken from the PIDI occ
paper. that aligns
22
This is because the authors of VFA have adopted setting their prior works 58 ground-truth poses to the satellite image center, which risks overfitting by biasing predictions towards the center. Since the official code for VFA 20 is not available, we attempted reproduce their results and found that we could only achieve the reported performance by using this same setting. However, when we use the standard setting adopted by our method and other comparable works 4
59
17 6. [8] 1.
our reproduced results are consistent with the VFA results reported in PIDLoc [2 Therefore, for fair comparison, we use the VFA (20 results from PIDLoc [22].
VIGOR. The comparison results on the VIGOR dataset [15] are presented in Table 2 Our method demonstrates significant reduction in both mean and median errors compared the baseline weakly supervised approach, G2SWeakly [I]. across all evaluation scenarios. Furthermore, even when benchmarked against state-of arr fully supervised methods, our method maintains comparable performance in same-area evaluations while achieving notable improvements across most metrics in cross-area evaluations. This reduces the gap between weakly supervised and fully supervised methods, indicating that our approach generalizes effectively to diverse localization tasks, including both same-area and cross-area scenarios, as well as cases where the query images are either panoramic or captured using pinhole cameras.
7

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Table 2: Comparison with the most recent state-of-the-ar (SOTA) on VIGOR ( denotes supervised learning algorithms). Our weakly supervised approach achieves performance comparable to SOTA supervised methods in Same-Areh evaluations and comprchensively surpasses them in Cross-Are Area evaluations.
Same-Area
Cross-Area
Method
A
Aligned-orientation
Unknown-orientatiion
Aligned-orientation
Unknown-orientation
Mean(m)) Median(m)) Mean(m)) Median(m))
Mean(m)) Median(m))
Mean(m) median(m).
Boosting 4
4.12
1.34
5.16
1.40
CCVPE S7
3.60
1.36
3.74
1.42
4.97
1.68
5.41
1.89
HC-Net 6
2.65
1.17
3.35
1.59
DenseFlow [8 x
3.03
0.97
4.97
1.90
5.01
2.42
7.67
3.67
G2SWeakly [
0
5.22
1.97
5.33
2.09
5.37
1.93
5.37
1.93
Ours
3.15
1.45
3.18
1.49
3.03
1.41
3.05
1.43
G2SWeakly [
4.19
1.68
4.18
1.66
4.70
1.68
4.52
1.65
Ours
2.87
1.58
2.91
1.60
2.84
1.36
2.89
1.38
NVIDIA Computation comparison. All evaluations of GPU memory usage performed
RTX 4090. Our model, which features DINOv2 44 backbone fine-tuned with our lightweight DPT network (50) (composed of few CNN layers), requires considerably less memory during the training phase compared to G2S Weekey [T. On the KITTI [5T] dataset (batch size 8), our training phase uses only 9.2 GB of GPU memory, substantially less than the 22.7 GB required by G2SWeakly []] During inference, our model consumes 7.7 GB of GPU memory, while G2SWeakly [ requires 7.2 GB as shown in Table 5 This increase 0.5 GB for method is attributable to the larger DINOv2 backbone 449 representing a trade-off for its enhanced feature representation capabilities.
Table BEV synthesis comparison the KITTI dataset.
Table Ablation stady backhone module the KITTI dataset.
Rendering Method
Same Area
Cross Area
Methods
A
Same Area
Cross Area
Median (m)]
Median (m))
Mean(m)
Median(án) Mean(m) Median(m)
(m)
(m)]
Direct Tran
17.74
15.61
6.35
LoRA 61
0
16.29
14.48
17.05
14.7
Lin Si00-Spaat-hhoe Shoot 60
16.14
13.94
17.74
14.51
DPT
5.82
2.85
7.05
3.22
OrienterNet S
15.59
13.68
16.15
13.80
Direct Train
1
14.32
12.43
17.28
15.14
Direct Projection
7.59
4.25
8.93
5.81
61]
13.58
11.79
16.81
BevSplat (wlo OPT)
5.74
DPT
2.87
2.06
6.20
2.51
BevSplat OPT)
5.82
2.85
7.05
3.22
Table Comparison of resource consumption.
IPM Lift-Splat-Shoot 60
6.68
3.71
8.60
4.84
7.89
4.30
11.63
5.31
Training Memory Inference Memory Inference
OrienterNet
5.07
OrienterNet S
32 .4
10.8
71
Direct Projection
7.59
4.25
8.93
5.81
LSS
8.3
85
BesSplat (wla OPT)
4.37
3,21
7,86
4.57
G2SWeakly
1
22.7
7.2
31
BevSplat (w/ OPT)
2.87
2.06
6.20
2.51
Ours
7.7
44
4.2 Ablation Study
Different BEV synthesis approaches. To val- idate the effectiveness of our BevSplat method, we compared it against two common BEV generation techniques: the Inverse Perspective Mapping (IPM) approach utilized in [1, and direct projection of 3D point clouds. For a fair comparison, both baseline methods and our BevSplat employed the same DINOv2 back- bone for feature extraction.
Table Ablation study module on KITTI
BackBone A1
Same
Cross
Mean(mj. Median(m)) Mean(m), Median(m))
VGG
0
8.77
4.53
9.91
5.80
DINOvI
7.68
4.01
9.16
4.67
DINOv2
0
7.04
3.45
8.37
4.21
DINOV2(FTI)
5.82
2,85
7.05
3.22
VGG
6.49
2.72
8.02
4.29
DINOvI
2.98
7.12
3.37
DINOv2
4.21
2.73
7.01
3,18
The IPM projection method. This technique assumes all pixels in the ground-level image correspond to flat plane at a real-world height of meters. Consequently, while IPM can accurately represent flat road surfaces elevations. These objects are typically stretched along the line of sight in the BEV. For instance, in the first row depicted in Fig. 4b), vehicles' features appear elongated into regions not corresponding to their actual ground footprint. Similarly, in the second row Fig. bb) buildings' features are distorted and erroneously projected. further limitation that IPM typically projects only the lower portion of the ground image to BEV, discarding valuable information from the upper half. In contrast,
2INO
2.87
2.06
6.20
2.51
BEV, it introduces significant distortions for any objects with non-zero

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Ground
GerSylat Tonn
Ground
(b) IPM Yeaturt
(c) D.P Feature (d) Bevcsplai Peatues
Figure 4: Visualization of query ground images (a), the corresponding BEV feature maps synthesized by IPM (b), by direct projection (c), and by the proposed BevSplat (d). The top two examples are from the KITTI dataset, while the bottom two are from the VIGOR dataset.
our BevSplat method effectively.
is designed to leverage geometric information from the entire image more
Direct point cloud projection to BEV. While this approach can address some of the aforementioned IPM limitations, it introduces new challenges stemming from point cloud sparsity (visualized in Fig. [c). This sparsity leads to BEV voids and discontinuous features, exacerbated by the lack of control over individual point attributes such opacity, scale, and shape. Furthermore, unlike 3DGS 77 which utilizes a-blending, the simple top-down projection inherent in this method causes severe occlusion, leading to the loss of underlying feature information an issue also evident in Fig. 4c). Our ablation study (Table 3 confirms this inherent characteristic: BevSplat(w// OPT) configured with non-optimizable Gaussian parameters (c.g., fixed opacity=1.0,, scalc=0.1, offsett==0, and no learned adjustments) performs comparably to direct point cloud projection, underscoring that point clouds be formulation significantly improves upon this baseline by optimizing Gaussian opacity, scale, shape, and position. Guided by satellite imagery, this optimization process effectively mitigates the issues of point cloud projection, yielding coherent, feature-rich BEV representations and thereby enabling superior localization accuracy. As detailed in Table BevSplat subsequently outperforms both IPM and Direct Projection methods.
degenerate form of 3DGS (7]. However, our full BevSplat(w/ OPT)
Other depth-basee re-sampling methods. We further compare method with other BEV projection techniques that are also based on depth prediction, such as those used in Lift-Splat-Shen Shoott [60] and OrienterNet [3] detailed in Table by adapting their projection modules into framework. Although these approaches can generate a denser BEV and, like our method, leverage the full vertical information from the ground-view image-allowing them perform well in same-area settings when guided by GPS labels they fail to generalize to unseen environments. They tend to make erroneous guesses to fill in occluded regions, which explains their performance degradation in cross-area evaluations. Furthermore, they employ a simple weighted averaging for BEV projection, which is less accurate for handling vertical occlusions compared to BevSplat's principled alpha blending. Finally, both methods utilize complex h x w X d depth representation to perform an attention based sum over ground features. This implicit, high-dimensional process incurs substantial computational and memory overhead to produce a denser BEV as shown in Table 5
Foundation model backbone. To validate the effectiveness of fine-tuning a foundation model for extracting ground and satellite image features, we conducted ablation studies on the impact of different foundation models with their praneed weights, well as the influence of various fine-tuning methods on the experimental results.
Impact of Different Foundation Models and Weights. Prioritizing robust outdoor generalization and effective 3D-relevant feature extraction for our foundation model, we selected DINOv2 [49 fine-tuned with the FiT method [48). This model, which we term 2FiTv) utilizes its dinov2 base bineeffiee pre-trained weights renowned for these capabilities. To validate this choice and compare its efficacy against alternatives, our ablation study also evaluated VGG [62], DINOv1 (63.. and the original DINOv2. All DINO-based backbones in this study (DINOv1, DINOv2, and DINOx2(FiT)) were subsequently further fine-tuned by using DiT-like module (50). The ablation results (Tablelo) validated our selection, as (FF) after our DPT-like fine-tuning, demonstrated superior performance among the evaluated backbones.
Impact of Different Fine-tuning Methods. Using DPT-like models is common practice for 3D vision tasks; for example, VGGT 664 utilizes DPT [50) for point cloud reconstruction, depth estimation, and

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feature matching. Although we are the first to apply DPT-DINO for feature extraction in the specific sub-field of cross-view localization, our motivation is to similarly obtain features that are rich in 3D information. This is analogous to human navigation, where in addition to semantic information, an understanding of the real 3D scene is also crucial for localization. However, obtaining such 3D-aware features to bridge the significant ground-satellnn
domain gap is non-trivial. Simpler fine-tuning methods like direct end-to-end training or LoRA [61] fail, as they either lose crucial texture details or are not powerful enough to adapt the foundation model. We use a DPT-like module because its multi-scale feature fusion architecture is uniquely suited for this challenge. successfully adapts the backbone by preserving both the low-level texture and high-level semantic information required for matching across these different domains as shown in Table
Number of Gaussian primitives per pixel (Np)). The number of Gaussian primitives per pixel, Np, also affects the resulting BEV feature quality. While an excessive Np can complicate training and cause inter-primitive occlusions, an insufficient count leads to sparse and inadequate BEV representations. Our ablation study (Fig. ()) determined Np = 3 to be optimal, offering the best balance between feature richness and model tractability,
Metrics Al
Metrics Np (for
Metrics vs. Sequerice (for 0)
Metrics Sequueice (for
Error (m)) lower is better)
(1 Love 16 Semmon
Metic Type
Meskc Type
Samea Arms Mewn
Same Area Mees
Nezz Type
Matc: Type
Surie Acea Modian
Scmne Arza Medion
Sane Arda- Moan
Arza
Cross loes Meon
Cross Area Mese
Cress Median
Area Medikn
Enoo (m0)
Primitives Pixel
Primitives per (Np)
Sequence
Sequence
Figure 5: Ablation on primitives per pixel (N₂) Figure 6: Location error with increasing sequence The error is minimized when N,- = 3 on KITTI length on KITTI dataset. dataset.
4.3 Multi-Frame Localization
Beyond processing single ground-level images, our method extends to leveraging multiple frames from video sequences to enhance localization robustness, particularly in dynamic environments. Similar to CVLNett [65] given known inter-frame relative poses, our BevSplat technique projects features from these frames into a unified BEV. These multi-frame BEV features are then fused by a Transformer employing self-attention. We investigated this multi-frame capability using query video sequences comprising to frames. The results, presented in Fig. (6 demonstrate that localization performance consistently improves with an increasing number of frames in the sequence. This underscores the efficacy of our approach in leveraging temporal information from video data for enhanced localization robustness.
Conclusion
This paper has introduced a new approach for weakly supervised cross-view localization by leveraging feature-based 3D Gaussian primitives to address the challenge of height ambiguity. Unlike traditional methods that assume a flat ground plane or rely computationally expensive models such as cross- view transformers, our method synthesizes a Bird's- s-ye View (BEV) feature map using feature- based Gaussian splatting, enabling more accurate alignment between ground-level and satellite images. We have validated our approach on the KITTI and VIGOR datasets, demonstrating that our model achieves superior localization accuracy.
However, the inference speed of our method is currently constrained by the reconstruction and rendering overhead inherent to existing 3D Gaussian Splatting (3DGS) techniques. Future work will focus on developing faster reconstruction algorithms and more compact 3D Gaussian representations to enhance computational efficiency. Despite this current limitation, we believe that our approach provides a promising direction for scalable and accurate cross-view localization, paving the way for real-world applications in autonomous navigation, geospatial analysis, and beyond.
10

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Acknowledge
The authors are grateful for the valuable comments and suggestions by the reviewers and ACs. This work was supported by NSFC (02406194), Shanghai Frontiers Science Center of Human- cenan-centered Artificial Intelligence (ShangHAI), MoE Key Laboratory Intelligent Perception, HPC Platform of ShanghaiTech University and Sluman-Maching Collaboration SLIP-HHMaaCo. A part of the experiments of this work were supported by the core facility Platform of Computer Science and Communication, SIST, ShanghaiTech University.
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A Carefule Explanation of the Weakly Supervised Setup
We focus weakly-supervise setting because precise GPS data is often unavailable in the real world. To do this, we adopt the weakly-supervised setup from G2S []] which defines two scenarios:
>=0: the error of the location labels for ground images in the training dataset is the same as the error that the model aims refine during deployment. For example, the error of location labels for ground images the training data set +/- 20m. During testing, the model is also given a location of query images with error up to 20m and aim to reduce this enror.
>=1: relatively more accurate location labels for ground images in the training data are available than the poses we aim to refine during employment. For example, the model was trained with images whose location labels have an error of +/- 5m. During testing, the query images have an initial location estimate with errors up to 20m, and the model aim to reduce this error.
B Concepts of "Height Ambiguity".
We use the term "height ambiguity" to describe the challenge of projecting 2D ground-level image to B-ye View (BEV). Because single 2D pixel could represent points at various depths and heights, its true 3D position is ambiguous.
Methods like IPM resolve this ambiguity by assuming a flat ground plane. This introduces significant emoos for any object with height, causing the characteristic distortions and smearing we aim solve. We will clarify this definition in our revision
C Clarification on Occlusion Handling in the Differentiable Blending Process
Forward Pass: Following Equation sort all contributing Gaussians by depth and render them from front to back.
The process is fully differentiable, allowing the loss to guide how the scene should be structured. For the feature vector (fb): The gradient for the b-th Gaussian computed as:
Backward Pass:
aLa 0f 3F
OLa
Tn at
The learning signal is scaled by transmittance (7i) and opacity (as). This means the features of the most visible (least occluded) and most solid Gaussians are prioritized for updates.
For the opacity (at): The gradient of the b-th Gaussian is computed as:
ÖL
ŌLa
To (Jo - -
dog
OF
The gradient depends on the difference between the current Gaussian's feature (f)) and the accumu- lated features behind it (fficcum)) This trains the model to make a Gaussian opaque if it is needed to hide conflicting background, effectively learning to form solid, occluding surfaces.
In short, this mechanism is directly analogous to how the original 3DGS handles RGB colors, and we have repurposed it to optimize feature representations for localization.
D Robustness to Localization Errors
We evaluate the robustness of our method to varying levels of initial localization error. As shown in Table 7 localization performance improves significantly as the initialization error decreases.
E
Ablation Study on Gaussian Primitive Offset and Scale
This ablation study investigates our method's sensitivity to the maximum offset and maximum scale of Gaussian Primitives. For each parameter, evaluate values from the set (0.3, 0.5, 1.0). The results,
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Table Performance comparison under different location error settings on KITTI dataset.
Location Error (m²)
Same Area
Cross Area
A1
Mean(m) + Median(m) 4
Mean(m) 4 Median(m) +
5.82
2.85
7.05
3.22
56 56
2.87
2.06
6.20
2.51
3.27
2.28
3.60
2.47
28 x 28
2.43
1.94
3.31
2.21
presented Table g demonstrate relatively stable performance across these configurations. Optimal performance is observed when both the maximum offset and scale set to 0.5; consequently, these are adopted as their default values.
Table 8: Ablation study on max max_offiet and max_ KITTI dataset.
Same Area
Cross Area
Max_ (f)_Offfst((m)
Max_Scale(m)
A1
Mean(m) 4 Median(m)
Mean(m) 4 Median(m)
0.3
0.3
6.16
2.89
7.36
3.20
0.5
0.5
0
5,82
2.85
7.05
3.22
1.0
1
0
6.00
2.95
7.06
3.16
0.3
0.3
1
3,42
2.28
6.83
2.53
0.5
0.5
2.87
2.06
6.20
2.51
1.0
1
3.28
2.30
6.52
2.57
Ablation Study on the Number of Gaussian Primitives Per Pixel(NV,)
We conducted ablation study on the number of Gaussian primitives per pixel (N₂) across both the KITTI and VIGOR datasets to validate our design choice. The results for KITTI are presented in Figure 5 of our main paper, and the new results for the VIGOR dataset are provided below Table 9
Table 9: Ablation study on the number of sampled points, N,
Same Area
Cross Area
Np
A
Mean(m) | Median(m) |
Mean(m) & Median(m)
0 3.05
1.71
2.97
1.71
0
2.98
1.67
2.94
1.65
3
0
2.96
1.62
2.90
1.65
0
3.03
1.67
2.91
1.68
I
2.62
1.47
2.71
1.42
2
1
2.59
1.41
2.67
1.40
3
1
2.57
1.40
2.63
2.54
2.59
1.42
2.65
1.38
The results on VIGOR are consistent with our findings on KITTI: performance optimal when =3. As we discuss in our paper:
Using too few primitives can limit the model's ability to fill gaps in sparse regions.
Using too many primitives can make training more difficult.
This finding is also consistent with prior work. Our design was inspired by PixelSplat 666 which similarly found Np=3 to be a robust and effective setting across multiple datasets. Therefore, we conclude that Np=3 is well-justified hyperparameter that should be generally applicable.
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G Applicability to Multi-Frame Localization Tasks
Input Sequence of Ground Images (Frames 1 to 4)
Fuse Feature
Satellite Feature
Location Map
Figure 7: Visualization of multi-frame localization results on the KITTI dataset, achieved using our BevSplat-based approach. This demonstrates the aggregation of information over time and the filtering of dynamic elements.
As detailed in the main paper, our quantitative analysis of CVL.Net-based multi-frame fusion method (65] demonstrated progressively improved performance with increasing number of frames, confirming its efficacy for temporal sequence tasks. To complement these findings, Figure provides qualitative illustration.
This visualization highlights how our fusion strategy effectively aggregates richer contextual infor- mation from multiple video frames. Notably, the approach adeptly filters dynamic objects while prioritizing the preservation of static scene elements, which are crucial for robust cross-view lo- calization. These qualitative insights further substantiate the effectiveness and generalizability of our proposed method handling dynamic environments and leveraging temporal information for improved localization.
H Multi-frame Fusion Comparison: BevSplat vs. IPM and Direct Point Cloud Projection
To demonstrate our method's consistency and fusion capabilities, we have conducted a new multi- frame comparison against both IPM and direct point cloud projection baselines. The Table TO below present the performance on the KITTI dataset using a sequence six frames. The values in parentheses show the percentage improvement from fusing six frames over the single-frame results:
Our BevSplat framework demonstrates superior multi-frame fusion capabilities for the following reasons:
Inverse Perspective Mapping (IPM): BEV representations generated via IPM are prone to significant artifacts and distortions, which fundamentally hinder effective temporal fusion.
Direct Point Cloud:
Projections of raw point clouds result in sparse representations that handle occlusions poorly, often causing background features to bleed through foreground objects. This issue is not resolved well by aggregating multiple frames.

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Table 10: Mutil-frama fusion comparison.
Same Area
Cross Area
Methods
Seq
A1
Mean(m)
Median(m)
Mean(m)
Median(m)
G2SWeakly
8.65(L 4.1%)
5.22(1 5.7%)
9.41(J 4.6%)
6.01(1 5.3%)
Direct Projection
7. 7.05() 7.1%)
3.86(. 9.2%)
8.15(.. 8.7%)
5.31 8.6%)
Ours
6
0
5.01() 13.9%)
2,27(1 20.4%)
6.09(L 13.6%)
2.71() 15.8%)
G2SWeakly
6
6.25() 6.4%)
3.38() 8.9%)
8.18(1 4.9%)
4.32() 10.7%)
Direct Projection
6
1
3.85() 13.1%)
2I91 11.6%)
7.18(1 9.6%%
3,96() 14.7%)
Ours
6 2. 30.0%)
1.77(L 14.1%)
5.23(L 15.6%)
1.94() 22.7%)
Our Method: In contrast, BevSplat utilizes adaptive Gaussian primitives to create a dense, coherent BEV that faithfully represents the road topology. This provides robust foundation for multi-frame fusion, as shown in Fig. 9 of our supplement.
I Sensitivity to Depth Prediction Quality and Failure Cases
We evaluate our framework's 's performance with three different depth foundation models: DepthAny- thingy1 [67) ZoeDepth [68], and DepthAnythingy2 [69 in Table
Table 11: Ablation study different depth estimation methods.
Same Area
Cross Area
Method
\1
Mean(m) | Median(m) 1 Mean(m) |
Median(m) |
DepthAnythingVV
[67
0
5.91
2.84
7.21
3.25
ZoeDepth
(68
0
5.84
2.86
7.14
3.22
DepthAnything
669
0
5.82
2.85
7.05
3.22
DepthAnythingVi
[67
1
2.97
2.11
6.28
2.52
ZoeDepth [68]
I
2.91
2.03
6.21
2.54
DepthAnything
669
1
2.87
2.06
6.20
2.51
The results demonstrate that while more accurate depth model improves performance, the overall system not highly sensitive to the choice different depth estimators. This robustness stems from our end-to-end differentiable design, which optimizes the initial 3D Gaussian positions during training, compensating for minor discrepancies between different depth priors. Our framework can seamlessly leverage future advancements in monocular depth estimation. We will include this analysis in our paper.
Robustness in Adverse Environmental Conditions
To test our method's robustness, generated synthetic Rain, Fog, and Night data for KITTI (following the methodology of Robust-Depth (70).. This allows for a controlled comparison against the G2SWeakly baseline under challenging conditions.
The results in Table T2 that while both methods are affected by adverse conditions, our approach demonstrates greater relative robustness. The reason lies in how each method handles corrupted input:
IPM-based methods like G2SWeakly project visual artifacts (e.g., rains, fog) directly onto the BEV, creating severe geometric distortions that corrupt the final representation.
contrast, our method, while starting with a less accurate depth map in these conditions, still preserves stable underlying 3D structure. It avoids the stretching errors of IPM and is better able to ignore atmospheric noise, leading more graceful degradation in performance.
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Table 12: Performance comparison under different weather conditions.
Same Area
Cross Area
Method
Weather
A
Mean(m)
Median(m)
Mean(m) &
Median(m)
Origin
0
9.02
5.54
13.97
10.24
Rain
0
16.45
13.29
18.44
16.52
G2SWeakly
Fog
0
12.82
9.8
15.47
12.03
Night
14.42 0
11.31
17.25
14.66
Origin
0
5.82
2.85
7.05
3.22
Rain
0
8.61
4.78
10.03
5.69
Ours
Fog
0
6.60
3,23
8.27
4.29
Night
7.59 0
4.11
10.77
6.16
Origin
6.68
3.71
12.15
7.16
Rain
I
16.82
13.48
19.45
17.42
G2SWeakly
Fog
10.19
5.48
15.67
12.53
Night
11.56
7.43
17.72
16.53
Origin
2.87
2.06
6.20
2.51
Rain
8.64
3.94
11.12
6.34
Ours
Fog
4.21
2.50
8.21
3.43
Night
7.95
3.32
10.39
7.09
K
Coordinate System
Projecting Ground Image
Projecting Tree
Camera
Actual Then
Actual Time
Real-world (Metric) Scale
World (Height Megisttee
Projectod
Wer Dhoight Up ReppettV
Werlss (might)
'ourdd (Deoth))
(Desdh
Figure 8: BevSplat Geometry Projection Overview. Our method is two-stage geometry projection. Left panel (Stage 1): We reconstruct the 3D scene from ground-level images using their associated depth information, illustrated by converting a tree from ground-level image its 3D representation. Right panel (Stage 2): The reconstructed 3D scene is then projected into the Bird's Bird's-Eye View (BEV).
Our methodology employs world coordinate system consistent with the OpenCV convention 71], depicted in Figure This right-handed system where, from the camera's 'ss viewpoint, the +X axis extends to its right, the +Y axis points downwards, and the +7. axis aligns with its forward viewing direction. Consequently, the upward direction corresponds to the -Y axis.
In the 3D reconstruction stage, a point cloud is generated from the input images. This is achieved by back-projecting pixels, using their depth information, along the initial camera's 's viewing direction (defined as the +Z axis of this coordinate system).
Subsequently, for Bird's-Eye View (BEV) projection, an aerial perspective is simulated. A virtual camera is conceptually positioned at a nadir viewpoint-looking directly downwards-abon the reconstructed 3D scene. Given our coordinate system where the +Y axis points downwards, this BEV camera located a Y-coordinate that is numerically smaller than those of the scene's primary content (thus representing a higher altitude). It views along the +Y direction (downwards). The BEV
26

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is then formed by orthographically projecting the 3D point cloud onto the world's XZ- XZ-plane (which effectively serves as the ground plane) along this +Y viewing axis.
L Qualitative Results
Robust Performance in Complex Scenarios: As illustrated in the first two rows of images in Figure and
and Figure [0] our method demonstrates robust localization performance across a variety of challenging scenarios, such road intersections, curved road sections, and areas with significant occlusions from roadside trees, as validated on the KITTI and VIGOR datasets. This proficiency is primarily attributed our approach's enhanced capabílities in: (1) effectively handling visual occlusions caused by buildings; (2) establishing and leveraging more accurate geometric relationships within the scene; and (3) optimally fusing features pertinent to the vertical spatial arrangement of elements, such as trees and road surfaces, between ground-level and aerial (e.g., satellite) views. Consequently, our method achieves promising localization results in these complex environments, underscoring its effectiveness tackling real-world complexities.
Limitations in Feature-Scarce Environments: Conversely, as illustrated in the last two rows of images in Figure and Figure TO in specific scenarios such as long, straight road segments that lack distinctive visual features, our method exhibits comparative reduction in localization accuracy. The primary reason for this limitation is that the absence of salient visual landmarks, the deep learning network, when attempting to match the ground-level view to the satellite imagery, may assign similar matching probabilities or confidence scores to multiple plausible locations within the satellite map. This multi-modal matching outcome leads to localization ambiguity, making it difficult for the network to make a unique,
positioning decision.
M On the Benefits in Supervised vs. Weakly-Supervised Settings
Although our paper focused on the weakty-supervised setting, our framework also demonstrates strong performance with full supervision. To illustrate this, we trained our model and the G2SWeakly baseline in fully supervised setting on the KITTI dataset. The results are in Table 13
Table 13: Supervised vs. weakly-superrised settings.
Same Area
Cross Area
Method
Al
Mean(m) + Median(m) 1
Mean(m) +
Median(m) $
6.32
3.15
12.2
8.33
Ours(Supervieed)
2.07
1.12
6.75
3.03
Ours(Weakly Supervised)
0
5.82
2.85
7.05
3.22
MurslWaklly Supervised) 1 2.61
2.06
6.20
2.51
As the results show, while switching to a fully supervised setting, our model highly competitive, in the challenging cross-area task. However, we found that our weakly-supervised model (A==1) achieves even better cross-area performance than our own fully-supervised version. This suggests that precise supervision may cause overfitting to the training domain's 's biases, which is contrary to our goal of building a more generalizable system.
Therefore, our paper's focus on the weakly-supervise setting is twofold. First, it addresses the practical challenge that high-quality GPS data is often unavailable in the real world. Second, it the setting where our method paradoxically achieves its best and most robust generalization performance.
N Limitations and Future Works
As discussed in the main paper's conclusion, a current limitation of our BevSplat method, which renders Bird's-Eye View (BEV) perspectives based on 3D Gaussian Splatting 77. is its computational speed compared to Inverse Perspective Mapping (IPM). For instance, on an NVIDIA RTX 4090 GPU, BevSplat requires 14 ms to generate single BEV image. In contrast, IPM, which utilizes
27

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Louadnn PrOMIOI
OVt
Lecetor pnoottt
Query Ground Image
BevSplat Image
BevSplat Feature
Satellite Feature
Location Map
Figure 9: Qualitative results for BevSplat-based single-image localization on KITTI. Top two rows:
successful examples; bottom two rows: failure examples.
Olrr OF
Loutber
Query Ground Image
BevSplat Image BevSplat Feature
Satellite Peature
Location Map
Figure 10: Qualitative results for BeySplat-based single-image localization on VIGOR. Top two rows:
successful examples; bottom rows: failure examples.
28

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direct linear interpolation, can achieve this in 4ms. This performance disparity affects the overall inference speed of model. Therefore, a significant direction for our future work the exploration of faster and more compact Gaussian representations to address this bottleneck and enhance real-time applicability.
Broader Impacts
Our work, BevSplat, addresses the critical demand for robust and accessible localization systems for mobile robots, such as drones and autonomous vehicles, particularly scenarios where high-precision GPS is either unavailable impractical due cost signal dependeney. By leveraging computer vision, BevSplat delivers real-time, high-precision localization using only a monocular camera, camera augmented with low-cost, low-precision GPS. This significantly extends localization capabilities to GPS-denied or unreliable environments, a crucial step for the widespread adoption of autonomous systems.
To foster further research and collaboration within the community, we are committed to open-sourcing complete codebase, training datasets, and pre-traine se-irained model weights GitHub. This efficient implementation, which operates on a single NVIDIA RTX 4090 GPU, is provided as resource for the research community. We encourage researchers to explore, build upon, and collaborate with us to advance this promising research direction, ultimately contributing to safer and more versatile autonomous navigation.
29


Reviews:
[{"review_id": "Ee0Zi4GK9T", "reviewer": "Reviewer_gAUg", "summary": "This paper focuses on weakly-supervised cross-view localization to find a ground camera's pose corresponding to a satellite map using training data with noisy location labels.\nThe authors propose BevSplat inspired by 3D Gaussian Splatting. For each pixel in the ground image, it predicts a set of 3D Gaussian primitives, each with its own position, shape, etc. After rendering, the method creates a high-quality BEV feature map that avoids distortions. The BEV map is matched against a feature map from the satellite image to determine the final location. Extensive experiments on the KITTI and VIGOR datasets have shown remarkable performance against existing work.", "questions": "1. As mentioned in \"Weaknesses\", how does this paper handle potential information loss from occlusion when projecting multiple 3D Gaussians to 2D location?\n2. Are there any failure cases/discussions on how sensitive the localization accuracy is to the quality of this depth prediction?\n3. Is $N_p$ sensitive to specific dataset?", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The idea of using feature-based 3D Gaussian primitives for BEV synthesis is straightforward and effective.  The proposed approach directly models the 3D scene geometry from a single image to resolve distortions, which is a more principled approach than other methods like IPM. Visualizations in Fig.1 and 4 clearly show that BevSplat handles challenging cases like buildings and curved roads better than prior work.\n2. Experiments are comprehensive, and results are remarkable. Evaluations on two standard benchmarks are conducted, showing a substantial leap in performance. Ablation studies on rendering method, other backbones against DINOv2, and the number of primitives per pixel are included to prove the model's effectiveness.\n3. The paper is well-written and easy to follow. The overall structure is clear.\n\nWeaknesses:\n1. From Sec 3.1, the proposed method relies on a pre-trained depth estimation model, while the quality of the initial 3D geometry is heavily dependent on this. The paper does not analyze how sensitive the localization accuracy is to the quality of this depth prediction. \nPoor performance of the depth model in several environmental conditions (e.g., night, rain, etc.) would likely lead to significant failures, which is expected to be discussed in the main paper.\n2.  A direct comparison of computational cost and complexity (e.g., interference time, FPS) is suggested in the main paper for a better understanding of method's practical viability.\n3. Sec 4.2 shows the impact of $N_p$. This might suggest that the model is sensitive to this hyperparameter, which may not generalize perfectly to other datasets without re-tuning. This paper didn't explain this in detail, leaving it more like an empirical finding. More explanations/examples on this are suggested. \n4. From my understanding, when rendering the BEV feature map (Sec 3.2.1), multiple 3D Gaussians can be projected to the same 2D pixel location. How does the differentiable blending process (Eq. 3) handle this potential information loss from occlusion? More explanations on this are suggested.", "was_revised": true, "final_justification": "The authors have thoroughly addressed my concerns and questions during the rebuttal, including aspects such as alternative depth models, challenging environmental conditions, computational cost and complexity analysis, and hyperparameter selection. \n\nI believe these clarifications and proposed changes will strengthen the work and enhance its overall persuasiveness. I recommend that these revisions be incorporated into the final version of the paper.\n\nBased on the response, I have raised my score from 4 to 5. I appreciate the authors’ thoughtful and comprehensive replies.", "rebuttal": "We thank Reviewer gAUg for the constructive feedback and insightful suggestions. We address the raised points below.\n## 1. Sensitivity to Depth Prediction Quality and Failure Cases.\n\nWe thank the reviewer for this suggestion. \nWe evaluate our framework's performance with three different depth foundation models:  DepthAnythingv1, ZoeDepth, and DepthAnythingv2:\n\n| Depth Model     | λ1 | Same Area mean(m) ↓ | Same Area median(m) ↓ | Cross Area mean(m) ↓ | Cross Area median(m) ↓ |\n|:----------------|:--:|:-------------------:|:---------------------:|:--------------------:|:----------------------:|\n| DepthAnythingv1 | 0  |         5.91        |          2.84         |         7.21         |          3.25          |\n| ZoeDepth        | 0  |         5.84        |          2.86         |         7.14         |          3.22          |\n| DepthAnythingv2 | 0  |         5.82        |          2.85         |         7.05         |          3.22          |\n| DepthAnythingv1 | 1  |         2.97        |          2.11         |         6.28         |          2.52          |\n| ZoeDepth        | 1  |         2.91        |          2.03         |         6.21         |          2.54          |\n| DepthAnythingv2 | 1  |         2.87        |          2.06         |         6.2          |          2.51          |\n\nThe results demonstrate that while a more accurate depth model improves performance, the overall system is not highly sensitive to the choice of different depth estimators. This robustness stems from our end-to-end differentiable design, which optimizes the initial 3D Gaussian positions during training, compensating for minor discrepancies between different depth priors. Our framework can seamlessly leverage future advancements in monocular depth estimation. We will include this analysis in our paper. \n\n\n## 2. Robustness in Adverse Environmental Conditions\nWe thank the reviewer for this crucial point. To test our method's robustness, we generated synthetic Rain, Fog, and Night data for KITTI (following the methodology of \"Robust-Depth\"[61], ICCV 2023). This allows for a controlled comparison against the G2SWeakly baseline under challenging conditions.\n\n| Methods   | Weather | λ1 | Same Area mean(m) ↓ | Same Area median(m) ↓ | Cross Area mean(m) ↓ | Cross Area median(m) ↓ |\n|:----------|:--------|:--:|:-------------------:|:---------------------:|:--------------------:|:----------------------:|\n| G2SWeakly | Origin  | 0  |         9.02        |          5.54         |        13.97         |         10.24          |\n| G2SWeakly | Rain    | 0  |        16.45        |         13.29         |        18.44         |         16.52          |\n| G2SWeakly | Fog     | 0  |        12.82        |          9.80         |        15.47         |         12.03          |\n| G2SWeakly | Night   | 0  |        14.42        |         11.31         |        17.25         |         14.66          |\n| Ours      | Origin  | 0  |         5.82        |          2.85         |         7.05         |          3.22          |\n| Ours      | Rain    | 0  |         8.61        |          4.78         |        10.03         |          5.69          |\n| Ours      | Fog     | 0  |         6.60        |          3.23         |         8.27         |          4.29          |\n| Ours      | Night   | 0  |         7.59        |          4.11         |        10.77         |          6.16          |\n| G2SWeakly | Origin  | 1  |         6.68        |          3.71         |        12.15         |          7.16          |\n| G2SWeakly | Rain    | 1  |        16.82        |         13.48         |        19.45         |         17.42          |\n| G2SWeakly | Fog     | 1  |        10.19        |          5.48         |        15.67         |         12.53          |\n| G2SWeakly | Night   | 1  |        11.56        |          7.43         |        17.72         |         16.53          |\n| Ours      | Origin  | 1  |         2.87        |          2.06         |         6.20         |          2.51          |\n| Ours      | Rain    | 1  |         8.64        |          3.94         |        11.12         |          6.34          |\n| Ours      | Fog     | 1  |         4.20        |          2.50         |         8.20         |          3.43          |\n| Ours      | Night   | 1  |         7.95        |          3.32         |        10.39         |          7.09          |\n\n\nThe results show that while both methods are affected by adverse conditions, our approach demonstrates greater relative robustness. The reason lies in how each method handles corrupted input:\n\n* **IPM-based methods** like G2SWeakly project visual artifacts (e.g., rains, fog) directly onto the BEV, creating severe geometric distortions that corrupt the final representation.\n* In contrast, **our method**, while starting with a less accurate depth map in these conditions, still preserves a stable underlying 3D structure. It avoids the fundamental stretching errors of IPM and is better able to ignore atmospheric noise, leading to a more graceful degradation in performance.\n\nWe will add this analysis to our paper. We also believe that as depth estimation foundation models continue to improve, the robustness of our method will increase further. Thank you for prompting this important investigation.\n\n## 3. Is Np Sensitive to Specific Dataset?\nWe conducted an ablation study on the number of Gaussian primitives per pixel ($N_{p}$) across both the KITTI and VIGOR datasets to validate our design choice. The results for KITTI are presented in Figure 5 of our main paper, and the new results for the VIGOR dataset are provided below.\n\n| Np | λ1 | Same Area mean(m) ↓ | Same Area median(m) ↓ | Cross Area mean(m) ↓ | Cross Area median(m) ↓ |\n|:--:|:--:|:-------------------:|:---------------------:|:--------------------:|:----------------------:|\n| 1  | 0  |        3.05         |         1.71          |         2.97         |          1.71          |\n| 2  | 0  |        2.97         |         1.67          |         2.94         |          1.65          |\n| 3  | 0  |        2.96         |         1.62          |         2.90         |          1.65          |\n| 4  | 0  |        3.03         |         1.67          |         2.91         |          1.68          |\n| 1  | 1  |        2.62         |         1.47          |         2.71         |          1.42          |\n| 2  | 1  |        2.59         |         1.41          |         2.67         |          1.40          |\n| 3  | 1  |        2.57         |         1.40          |         2.63         |          1.38          |\n| 4  | 1  |        2.59         |         1.42          |         2.65         |          1.38          |\n\nThe results on VIGOR are consistent with our findings on KITTI: performance is optimal when $N_{p}$=3. As we discuss in our paper:\n* Using too few primitives can limit the model's ability to fill gaps in sparse regions.\n* Using too many primitives can make training more difficult.\n\nThis finding is also consistent with prior work. Our design was inspired by PixelSplat(CVPR 2024), which similarly found Np=3 to be a robust and effective setting across multiple datasets. Therefore, we conclude that Np=3 is a well-justified hyperparameter that should be generally applicable.\n\n\n## 4. Runtime and Memory Analysis.\n| Method      | Training Memory | Inference Memory | Inference Time |\n|:------------|:---------------:|:----------------:|:--------------:|\n| OrienterNet[5] |      32.4GB     |       10.8GB       |      71ms      |\n| DenseFlow[18]   |      34.8GB     |       27.8GB       |      74ms      |\n| G2SWeakly[1]   |      22.7GB     |      **7.2GB** |     **31ms** |\n| Ours        |     **9.2GB** |        7.7GB       |      44ms      |\n\nWe apologize for not providing this direct comparison in the original manuscript. As we discussed in our conclusion and supplementary material (lines 53-61), we acknowledge that our 3D reconstruction incurs a modest computational overhead, resulting in slightly higher inference costs than the simpler G2SWeakly [1] baseline. We consider this a necessary trade-off for the significant gains in localization accuracy. However, our method remains highly efficient compared to more complex approaches like the RAFT-based DenseFlow [18] and the transformer-based OrienterNet [5]. We will add this direct comparison to our revised manuscript for a clearer context.\n\n\n### 5. Clarification on Occlusion Handling in the Differentiable Blending Process\n\n**Forward Pass**: Following Equation (3), we sort all contributing Gaussians by depth and render them from front to back\n\n**Backward Pass**: The process is fully differentiable, allowing the loss to guide how the scene should be structured.\nFor the feature vector ($f_{b}$): The gradient for the feature of the b-th Gaussian is computed as:\n\n$$\\frac{\\partial L_{all}}{\\partial f_{b}} = \\frac{\\partial L_{all}}{\\partial F_{BEV}} * T_{b} * \\alpha_{b}$$\n\nThe learning signal is scaled by transmittance ($T_{b}$) and opacity ($α_{b}$). This means the features of the most visible (least occluded) and most solid Gaussians are prioritized for updates.\n\nFor the opacity ($α_{b}$): The gradient of the b-th Gaussian is computed as:\n\n$$\\frac{\\partial L_{all}}{\\partial \\alpha_{b}} = \\frac{\\partial L_{all}}{\\partial F_{BEV}} * T_{b} * (f_{b} - f_{b}^{accum})$$\n\nThe gradient depends on the difference between the current Gaussian's feature ($f_{b}$) and the accumulated features behind it ($f_{b}^{accum}$). This trains the model to make a Gaussian opaque if it is needed to hide a conflicting background, effectively learning to form solid, occluding surfaces.\n\nIn short, this mechanism is directly analogous to how the original 3DGS handles RGB colors, and we have repurposed it to optimize feature representations for localization.\n\n\n[61]Saunders, Kieran, George Vogiatzis, and Luis J. Manso. \"Self-supervised monocular depth estimation: Let's talk about the weather.\" ICCV 2023."}, {"review_id": "du4QGnl1VQ", "reviewer": "Reviewer_aKDE", "summary": "This work presents a novel weakly supervised cross-view localization method that utilizes 3D Gaussians to address height ambiguity and complex cross-view occlusions, which are limitations of previous IPM-based approaches. By leveraging a pre-trained depth network, the proposed method trains 3D Gaussian parameters from ResNet features. It employs a DINOv2-DPT backbone on both ground and satellite images to match similarities and predict the ground image’s position in the satellite image. The proposed method demonstrates strong performance on both the KITTI and VIGOR datasets. Several experiments support its effectiveness, although limitations remain in inference efficiency.", "questions": "- What motivates the use of fine-tuning with a DPT-like module [48]? Is this a common practice in this task, or is there a specific reason for introducing it here? In Table 4, all entries appear to use a fine-tuned DPT, making it hard to understand the motivation.\n\n- A more careful explanation of the weakly supervised setup would be helpful, including the differences between λ₀ and λ₁. Are noisy annotations the only form of supervision in this task? Considering that fully supervised methods exist, it is difficult to understand why the proposed method is particularly beneficial in the weakly supervised setting.\n\n- In Table 1, the azimuth values exactly match those of G2SWeakly [1]. Is there a particular reason for these identical results?\n\n- What exactly does “same area” mean in Table 1? It is mentioned that the region is identical, but it is unclear whether this refers to the same satellite images with different ground queries or literally the same image pairs used at training time. If it is the latter, what is the rationale for evaluating on identical data?", "limitations": "Yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "### Strengths\n- Novel framework: This work retains the weak-supervision advantages of G2SWeakly while overcoming its cross-view localization limitations by leveraging 3D Gaussian representations to boost performance. In particular, the use of 3D Gaussians effectively resolves height ambiguity caused by the planar assumption in previous IPM-based methods and mitigates matching failures in occluded regions.\n\n- Extensive experiments: The authors conduct thorough evaluations of the proposed method against various recent approaches from multiple perspectives. Despite being a weakly supervised method, it demonstrates competitive performance compared to supervised methods. Especially, the results presented in Figure 4 and Table 3 effectively highlight the distinctive features and advantages of the proposed approach.\n\n### Weaknesses\n- Lack of convincing evidence regarding efficiency: Although the training memory is modest, there appears to be no meaningful efficiency gain at test time. The paper points out the high computational cost of transformer-based models, but it is difficult to be convinced of the proposed method’s efficiency given its inference-time overhead, which is mentioned in the conclusion.\n\n- Key factors of performance improvement: Table 3 shows performance gains attributable to the 3D Gaussian module, but much of the improvement stems from the choice of foundation model. A deeper analysis of how different foundation models impact overall results in this cross-view localization task would strengthen the paper.", "was_revised": true, "final_justification": "Thank you for your effort in providing detailed responses and conducting additional experiments. \nThe comparison with supervised learning and recent work effectively highlights the strength of the proposed method in a weakly supervised setting. \nWhile the supervised approach performs better in the same-area setting, it shows inferior generalization in the cross-area case, where the proposed weakly supervised method excels. Similarly, FG² [21], despite its strong performance in the same area, shows a notable drop in cross-area accuracy. Given that FG² is one of the most recent methods, I understand that fully addressing this comparison may be beyond the scope of this paper.\nRegarding efficiency, I still find the contribution in this aspect to be somewhat limited, particularly when compared to G2SWeakly [1].\nOn the other hand, the ablation study using DPT confirms that the proposed approach is indeed effective when integrated with strong backbones.\n\nOverall, I find the paper promising for its contributions to weakly supervised localization, and I believe it could have a meaningful impact in this domain.", "rebuttal": "We thank Reviewer aKDE for the positive evaluation and insightful comments. We have corrected the typos (In Figure 1 and on line 77) in the revised manuscript. Our responses are below.\n\n\n## 1. Carefule Explanation of the Weakly Supervised Setup\nThank you for the question. We focus on a weakly-supervised setting because precise GPS data is often unavailable in the real world. To do this, we adopt the weakly-supervised setup from G2SWeakly [1], which defines two scenarios:\n\n**λ=0**: the error of the location labels for ground images in the training dataset is the same as the error that the model aims to refine during deployment. \nFor example, the error of location labels for ground images in the training data set is +/- 20m. During testing, the model is also given a location of query images with error up to 20m and aim to reduce this error. \n\n**λ=1**: relatively more accurate location labels for ground images in the\ntraining data are available than the poses we aim to refine during employment. \nFor example, the model was trained with images whose location labels have an error of +/- 5m. During testing, the query images have an initial location estimate with errors up to 20m, and the model aim to reduce this error. \n \n \n## 2. On \"Same Area\" vs. \"Cross Area\"\nWe apologize for the lack of clarity.\n\n* **Same-Area**: The test query images are from the same geographical regions as the training set, but are not the same images.\n* **Cross-Area**: The test images are from entirely new geographical regions not seen during training, testing the model's generalization. \n \n## 3. On the Benefits in Supervised vs. Weakly-Supervised Settings\nWe thank the reviewer for this excellent question. Although our paper focused on the weakly-supervised setting, our framework also demonstrates strong performance with full supervision. To illustrate this, we trained our model and the G2SWeakly baseline in a fully supervised setting on the KITTI dataset. The results are as follows:\n\n| Method                       | λ1 | Same Area mean(m) ↓ | Same Area median(m) ↓ | Cross Area mean(m) ↓ | Cross Area median(m) ↓ |\n|:-----------------------------|:--:|:-------------------:|:---------------------:|:--------------------:|:----------------------:|\n| FG²[21]                 | -  |         0.75        |          0.52         |         7.45         |          4.03          |\n| G2SWeakly(Supervised)        | -  |         6.32        |          3.15         |        12.20         |          8.33          |\n| Ours(Supervised)             | -  |         2.07        |          1.12         |         6.75         |          3.03          |\n| Ours(Weakly Supervised)      | 0  |         5.82        |          2.85         |         7.05         |          3.22          |\n| Ours(Weakly Supervised)      | 1  |         2.61        |          2.06         |         6.20         |          2.51          |\n\n\nAs the results show, while switching to a fully supervised setting, our model is highly competitive, even outperforming the most recent SOTA, FG² (CVPR 2025)[21], in the challenging **cross-area** task. However, we found that our weakly-supervised model (λ1=1) achieves even better cross-area performance than our own fully-supervised version. This suggests that precise supervision may cause overfitting to the training domain's biases, which is contrary to our goal of building a more generalizable system.\n\nTherefore, our paper's focus on the weakly-supervised setting is twofold. First, it addresses the practical challenge that high-quality GPS data is often unavailable in the real world. Second, it is the setting where our method paradoxically achieves its best and most robust generalization performance.\n\n\n\n\n## 4. Convincing Evidence Regarding Efficiency\nThank you for this insightful comment regarding the efficiency of our 3D representation. We would like to clarify why our model, despite its cost, is essential for the task.\n\nA core premise of our work is that explicitly modeling height is necessary to obtain a high-fidelity BEV representation. Methods that ignore 3D information, like Inverse Perspective Mapping (IPM), create distorted BEVs by \"smearing\" tall objects across the ground plane (as shown in our Fig. 1 & 4). This geometric corruption makes precise localization exceptionally challenging. Our method uses Gaussian primitives specifically to model this essential 3D structure and produce a clean, distortion-free BEV.\n\nWhile our approach requires slightly longer inference time and larger GPU memory than traditional IPM, however, when compared to other approaches—such as the transformer-based LSS and OrienterNet, or the RAFT-optimized DenseFlow—our method is significantly more efficient. The results on KITTI dataset are shown bellow:\n\n| Method | Training Memory | Inference Memory | Inference Time |\n| :--- | :---: | :---: | :---: |\n| OrienterNet[5] | 32.4GB | 10.8GB | 71ms |\n| LSS[60] | 26.1GB | 8.3GB | 85ms |\n| DenseFlow[18]   |      34.8GB     |       27.8GB       |      74ms      |\n| G2SWeakly[1] | 22.7GB | **7.2GB** | **31ms** |\n| Ours | **9.2GB** | 7.7GB | 44ms |\n\n## 5. Key Factors of Performance Improvement\nWhile the strong FiT3D DINO backbone contributes to a significant improvement, our method still demonstrates a clear improvement over both IPM and direct point cloud projection using the same backbone, as shown in Table 4. This indicates that our proposed BEV synthesis module is crucial to the performance gains. \n\n## 6. What Motivates the Use of Fine-tuning with a DPT-like Module [48]?\nThank you for pointing out this question. Using DPT-like models is a common practice for 3D vision tasks; for example, VGGT (CVPR 2025) utilizes DPT for point cloud reconstruction, depth estimation, and feature matching. Although we are the first to apply DPT-DINO for feature extraction in the specific sub-field of cross-view localization, our motivation is to similarly obtain features that are rich in 3D information. This is analogous to human navigation, where in addition to semantic information, an understanding of the real 3D scene is also crucial for localization.\n\nHowever, obtaining such 3D-aware features to bridge the significant ground-satellite domain gap is non-trivial. Simpler fine-tuning methods like direct end-to-end training or LoRA[62] fail, as they either lose crucial texture details or are not powerful enough to adapt the foundation model. We use a DPT-like module because its multi-scale feature fusion architecture is uniquely suited for this challenge. It successfully adapts the backbone by preserving both the low-level texture and high-level semantic information required for matching across these different domains.\n\nTo clarify the motivation that was missing from our original Table 4, we provide the following ablation study on the KITTI dataset which was previously omitted:\n\n| Method          | λ1 | Same Area mean(m) ↓ | Same Area median(m) ↓ | Cross Area mean(m) ↓ | Cross Area median(m) ↓ |\n|:----------------|:--:|:-------------------:|:---------------------:|:--------------------:|:----------------------:|\n| Direct Training | 0  |        17.74        |         15.61         |        17.59         |         15.71          |\n| LoRA[62]            | 0  |        16.29        |         14.48         |        17.05         |          14.7          |\n| DPT             | 0  |         5.82        |          2.85         |         7.05         |          3.22          |\n| Direct Training | 1  |        14.32        |         12.43         |        17.28         |         15.14          |\n| LoRA[62]            | 1  |        13.58        |         11.79         |        16.81         |         14.63          |\n| DPT             | 1  |         2.87        |          2.06         |         6.2          |          2.51          |\n\n\n## 7. Azimuth Values Exactly Match Those of G2SWeakly\nThank you for your observation. The azimuth results are identical because our work focuses on improving translational localization, and we directly adopt the rotation estimator from our baseline, G2SWeakly [1], without any changes.\n\nWe did mention this on lines 212-214, but we agree this could be made much clearer. We will explicitly state this in our method description and add a note to Table 1 in the revision.\n\n### References\n\n[60] Philion, Jonah, and Sanja Fidler. \"Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d.\" European conference on computer vision. Cham: Springer International Publishing, 2020.\n\n[62] Hu, Edward J., et al. \"Lora: Low-rank adaptation of large language models.\" ICLR 1.2 (2022): 3."}, {"review_id": "6rIt5PZC8h", "reviewer": "Reviewer_arEj", "summary": "The authors propose a cross-view localization method that utilizes 3D Gaussians to address ambiguities in height information inherent in existing approaches. By estimating effective 3D information, the method enhances localization accuracy. Evaluations on the KITTI and VIGOR datasets demonstrate competitive performance compared to G2SWeakly and other methods.", "questions": "* How does the proposed method compare to simpler 2D BEV representations, such as those used in Lift-Splat-Shoot[1], which require significantly less computation than 3D Gaussians?\n* Could the authors provide additional comparison results for multi-frame localization to demonstrate the method’s consistency across frames compared to IPM and direct point cloud approaches?\n* How does the method address the challenge of using relative depth from DepthAnythingV2 for the KITTI dataset, particularly in ensuring metric localization?\n\n[1] Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D, ECCV 2020", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n* Cross-view localization relies heavily on accurate 3D representations for estimating the relative pose between ground-based cameras and satellite imagery. Leveraging 3D Gaussians is a logical approach to improve this process.\n* The proposed method achieves significant improvements in localization metrics compared to the baseline and the G2SWeakly method.\n* The ablation study provides robust evidence supporting the effectiveness of the proposed approach.\n\nWeaknesses\n* The method synthesizes 3D local map using Gaussians to a satellite image to estimate relative pose. However, the reliance on detailed 3D information appears excessive, as highlighted by the limitation of high computational cost and usage of only BEV representation, which does not necessarily require height information.\n* Unlike Inverse Perspective Mapping (IPM), which does not require metric scale depth, the proposed method uses depth estimation. The paper notes that DepthAnythingV2, which provides only relative depth, was employed for the KITTI dataset, unlike UniK3D, which supports metric scale depth.", "was_revised": true, "final_justification": "Most of my concerns have been addressed in the rebuttal, so I'm raising my review to borderline accept.", "rebuttal": "We sincerely thank Reviewer arEj for the thorough review and constructive feedback. We address the major concerns below.\n\n## 1. Comparison with Lift-Splat-Shoot[60].\nWe thank the reviewer for this excellent question and for the suggestion to compare our method with an important baseline like Lift-Splat-Shoot (LSS) [60]. To provide a direct and fair comparison, we adapted the official LSS open-source code for our cross-view localization task. The key modifications were changing the input to a single ground camera, adding a satellite feature branch, and training the model with our loss function.\n\nThe performance and efficiency results on the KITTI dataset are presented below.\n\n### Performance Comparision.\n| Methods          | λ1 | Same Area mean(m) ↓ | Same Area median(m) ↓ | Cross Area mean(m) ↓ | Cross Area median(m) ↓ |\n|:-----------------|:--:|:-------------------:|:---------------------:|:--------------------:|:----------------------:|\n| LSS[60] | 0  |        16.14        |         13.94         |        17.74         |         14.51          |\n| Ours             | 0  |         **5.82**        |          **2.85**         |         **7.05**         |          **3.22**          |\n| LSS[60] | 1  |         7.89        |          4.3          |        11.63         |          5.31          |\n| Ours             | 1  |         **2.87**        |          **2.06**         |         **6.2**          |          **2.51**          |\n\n### Runtime and Memory Analysis.\n| Methods          | Training Mempry    | Inference Memory   | Inference Time   |\n|:-----------------|:------------------:|:------------------:|:----------------:|\n| LSS[60] | 26.1GB             | 8.3GB              |85ms              |\n| Ours             | 9.2GB              | 7.7GB              |44ms              |\n\n### Analysis of Experimental Results.\nRegarding the reviewer's point on **computational cost**, our analysis shows that our approach is actually more efficient than our adapted LSS baseline, particularly in **training memory** and **inference time**. We attribute this to the different 3D representations: LSS constructs a dense H x W x D point grid, which creates a larger computational bottleneck during the splatting stage compared to our sparser H x W set of Gaussian primitives.\n\nIn terms of **localization accuracy**, we believe the performance difference stems from two key design choices. First, our method's use of **alpha blending** allows for a more principled handling of occlusions compared to LSS's pillar-based pooling. Second, LSS's design generates a dense BEV, which forces the network to \"hallucinate\" features for occluded areas. This is challenging in a weakly-supervised setting that lacks a BEV ground truth. Our method avoids this by generating a sparse but more faithful representation of only the visible scene.\n\nWe believe these design choices make our approach particularly well-suited for the high-precision, weakly-supervised localization task. We will add this detailed comparison to our paper and thank you for prompting this valuable analysis.\n\n\n## 2. Multi-frame Fusion Comparison: BevSplat vs. IPM and Direct Point Cloud Projection.\n\nWe thank the reviewer for this valuable suggestion. To demonstrate our method's consistency and fusion capabilities, we have conducted a new multi-frame comparison against both IPM and direct point cloud projection baselines.\n\nThe tables below present the performance on the KITTI dataset using a sequence of six frames. The values in parentheses show the percentage improvement from fusing six frames over the single-frame results\n\n### Results for λ1 = 0：\n| Methods           | Seq | Same Area mean(m) ↓ | Same Area median(m) ↓ | Cross Area mean(m) ↓ | Cross Area median(m) ↓ |\n|:------------------|:--------:|:-------------------:|:---------------------:|:--------------------:|:----------------------:|\n| G2SWeakly         | 6        | 8.65(↓ 4.1%)        | 5.22(↓ 5.7%)          | 9.41(↓ 4.6%)         | 6.01(↓ 5.3%)           |\n| Direct Projection | 6        | 7.05(↓ 7.1%)        | 3.86(↓ 9.2%)          | 8.15(↓ 8.7%)         | 5.31(↓ 8.6%)           |\n| Ours              | 6        | 5.01(↓ **13.9%**)       | 2.27(↓ **20.4%**)         | 6.09(↓ **13.6%**)        | 2.71(↓ **15.8%**)          |\n\n### Results for λ1 = 1：\n| Methods           | Seq | Same Area mean(m) ↓ | Same Area median(m) ↓ | Cross Area mean(m) ↓ | Cross Area median(m) ↓ |\n|:------------------|:--------:|:-------------------:|:---------------------:|:--------------------:|:----------------------:|\n| G2SWeakly         | 6        | 6.25(↓ 6.4%)        | 3.38(↓ 8.9%)          | 8.18(↓ 4.9%)         | 4.32(↓ 10.7%)          |\n| Direct Projection | 6        | 3.85(↓ 13.1%)       | 2.91(↓ 11.6%)         | 7.18(↓ 9.6%)         | 3.96(↓ 14.7%)          |\n| Ours              | 6        | 2.01(↓ **30.0%**)       | 1.77(↓ **14.1%**)         | 5.23(↓ **15.6%**)        | 1.94(↓ **22.7%**)          |\n\n\nOur BevSplat framework demonstrates superior multi-frame fusion capabilities for the following reasons:\n\n* **Inverse Perspective Mapping (IPM)**: BEV representations generated via IPM are prone to significant artifacts and distortions, which fundamentally hinder effective temporal fusion.\n* **Direct Point Cloud**: Projections of raw point clouds result in sparse representations that handle occlusions poorly, often causing background features to bleed through foreground objects. This issue is not resolved well by aggregating multiple frames.\n* **Our Method**: In contrast, BevSplat utilizes adaptive Gaussian primitives to create a dense, coherent BEV that faithfully represents the road topology. This provides a robust foundation for multi-frame fusion, as shown in Fig. 9 of our supplement.\n\nWe will incorporate these visualizations into our revised manuscript.\n\n## 3. Metric Depth of DepthAnythingV2.\nWe appreciate the opportunity to clarify this important implementation detail, and we apologize our manuscript was not sufficiently clear on this point.\n\nThe DepthAnythingV2 project provides models that support both relative depth estimation (on arbitrary images) and metric depth estimation (when trained on datasets with ground-truth metric depth). We utilize the latter for our experiments. By using a model that directly outputs depth in meters, the scale ambiguity challenge is addressed at the input stage, providing our localization framework with the necessary metric information.\n\n## 4. High Computational Cost on Representation.\n\nThank you for this insightful comment regarding the necessity of our 3D representation. We would like to clarify why this component, despite its cost, is essential for the task.\n\nA core premise of our work is that explicitly modeling height is necessary to obtain a high-fidelity BEV representation. Methods that ignore 3D information, like Inverse Perspective Mapping (IPM), create distorted BEVs by \"smearing\" tall objects across the ground plane (as shown in our Fig. 1 & 4). This geometric corruption makes precise localization exceptionally challenging. Our method uses Gaussian primitives specifically to model this essential 3D structure and produce a clean, distortion-free BEV.\n\nWhile our approach requires slightly longer inference time and larger GPU memory than traditional IPM, however, when compared to other approaches—such as the transformer-based LSS[60] and OrienterNet[5], or the RAFT-optimized DenseFlow[18]—our method is significantly more efficient. The results on KITTI dataset are as shown below:\n\n| Method | Training Memory | Inference Memory | Inference Time |\n| :--- | :---: | :---: | :---: |\n| OrienterNet[5] | 32.4GB | 10.8GB | 71ms |\n| LSS[60] | 26.1GB | 8.3GB | 85ms |\n| DenseFlow[18]   |      34.8GB     |       27.8GB       |      74ms      |\n| G2SWeakly[1] | 22.7GB | **7.2GB** | **31ms** |\n| Ours | **9.2GB** | 7.7GB | 44ms |\n\n[60] Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D, ECCV 2020"}, {"review_id": "rouHauNHkZ", "reviewer": "Reviewer_WBEg", "summary": "The draft describes a system to localize a ground image relative to a satellite image. Building on previous work, G2SWeakly [1], the method can similarly be trained in a weakly supervised fashion, only requiring ground-satellite pairs with coarse GPS labels for the ground images. The main contribution on top of G2SWeakly is to substitute Inverse Perspective Mapping (IPM) with feed-forward prediction and birds-eye-view (BEV) re-rendering of Gaussian Splats. The splats encode high dimensional neural features, such that the BEV re-render is a neural representation of the ground image, which can be matched against a neural map extracted from the satellite image. Experiments are conducted on two datasets, Kitti and VIGOR, and show that the method a) consistently outperforms the baseline G2SWeakly, b) performs on par or even better than supervised competitors.", "questions": "- Please discuss the results of [20]. Could these numbers be included in Table 1, or is the comparison unfair for some reason? If these numbers were included, which claims would still hold? If sufficient reason were provided that [20] would not work in a weakly supervised fashion (e.g. by coupling [1] with [20]) I would consider accepting it's superior performance as being entirely limited to the strongly supervised domain, and raise my score.\n\n- Within your framework, would it be possible to adapt the BEV projection approaches of [5] or [20]? If those two baselines would be included in the draft, and the proposed BEV splats projection still shows benefits, I am willing to increase my rating.", "limitations": "yes (As a minor comment I would suggest to actually provide the \"inference speed\" of the method rather than only saying it is constrained.)", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Neutral: The writing is of mixed quality**. Initially, I had trouble understanding the setup and motivation of the paper, since the writing assumes that the reader is familiar with previous works on this problem. \n* Concepts like \"height ambiguity\" are repeatedly mentioned without being explained. \n* The limitations of Inverse Perspective Mapping (IPM) are assumed to be obvious throughout the text, and IPM is only explained on page 8. \n* The related work section mentions \"one-to-many cross-view localization\" or \"pixel-level localization\" without explaining what it is. \n* The technical contributions are not listed clearly, e.g. using a list of bullet points after the introduction. Only after reading the entire draft it becomes apparent that the main contribution is improving on the IPM step in G2SWeakly.\n* Parts of the pipeline, stemming from G2SWeakly, are omitted. The experimental section mentions that the proposed method re-uses the rotation estimation step of G2SWeakly. I do not think the method description ever touches on this aspect, I was not even aware that the rotation needs to be estimated in a designated processing step. I imagined rotation is just solved when matching the BEV features to the satellite features. Instead, there seem to be extra parts of the pipeline that are inherited from G2SWeakly but never discussed.\n* On the other hand, I found the technical description - of how the Gaussian Splats are predicted and used - well written and easy to follow.\n* The figures, e.g. the system overview in Fig. 2., are well done and informative.\n* Overall, despite the issues above, after reading the entire text, I had the impression that I understood the method and which problems it solves.\n\n**Strengths:**\n* The method advances capabilities of weakly-supervised cross-view localization which is a significant family of methods due to their ability to scale (precise ground-to-satellite pose labels are difficult to get).\n* The experimental sections shows consistent and notable improvements over the baseline, G2SWeakly. The method also compares well with fully supervised approaches. \n* The draft includes a multi-frame variation of the method which shows how accuracy can be improved if the user provides a short image sequence rather than a single image. Such a setup is of practical value.\n\n**Weaknesses:**\n* The draft cites [20] (\"View from above\", CVPR24) but does not discuss it. That method, while being fully supervised, seems to achieve superior results on the Kitti dataset, affecting some claims of the submission. I.e. claims like \"in cross-area evaluations, our method even surpasses supervised approaches\" (line 224) do not hold if the results of [20] were included in Table 1. [20] would lead by a large margin. This is made worse because [20] technically seems to address the same problems as the draft: namely incorporating features above the ground plane in ground-to-satellite matching. This needs to be discussed in detail.\n* The baselines for BEV projection are weak, and alternative approaches from the literature to do the BEV projection are ignored. The only alternative to naive IPM considered, is a re-rendering of the ground depth map from BEV. This clearly leads to a very sparse BEV representation that is difficult to match with the neural map, as discussed in the draft. But better strategies exist, e.g. the depth-based re-sampling of OrienterNet [5] which leads to a dense BEV or the aforementioned approach of [20].  \n\n**Conclusion:**\nThe main contribution of the draft is to present an alternative to Inverse Perspective Mapping (IPM), but previous strategies, like [5, 20] are not discussed nor compared to. Therefore, important baselines are missing.", "was_revised": true, "final_justification": "The authors have addressed my main concerns in the rebuttal. They provide the missing comparison to the BEV projection of [5] which performs worse than the proposed approach. They also provided a possible explanation of why [5] performs worse. \n\nRegarding the missing discussion of the results of [20], the authors explain that those results might be an artifact of a particular way to evaluate, that seems to re-appear throughout works of the authors of [20]. They substantiate this claim with experiments using a custom implementation, and with pointers to recent related work that similarly call the results of [20] into question.\n\nI encourage the authors to add this discussion and the various experiments to the submission / the supplement. With those changes, the submission represents a solid advance in weakly supervised cross-view relocalization, carried by strong results. The other reviews echo this assessment. Therefore, I recommend to accept the draft.", "rebuttal": "We thank Reviewer WBEg for the detailed review and constructive suggestions. We address the major concerns below.\n## 1. Writing\nWe sincerely thank you for your careful review and valuable feedback. We would like to provide explanations for the points you raised.\n\n\n### More Explanation of IPM.\nIPM was defined on page 1, line 32 and visualized in Figure 1 to motivate our work. \nIt assumes that all points on the ground plane, and exploit the ground plane Homography for the ground-to-satellite projection.\nWe will highlight this more clearly in the revision.\n\n### Concepts of \"Height Ambiguity\". \n\nThank you for the question. We use the term \"height ambiguity\" to describe the challenge of projecting a 2D ground-level image to a Bird's-Eye View (BEV). Because a single 2D pixel could represent points at various depths and heights, its true 3D position is ambiguous.\n\nMethods like IPM resolve this ambiguity by assuming a flat ground plane. This introduces significant errors for any object with height, causing the characteristic distortions and smearing we aim to solve. We will clarify this definition in our revision\n\n\n### Different Localization Task Definitions in Related Works\nWe will clarify these terms in the paper.\n* One-to-many localization: this task aims to determine the coarse location of a ground camera by retriving its similar satellite image counterparts from the database. The similar satellite image counterparts can be several, and thus is named as one-to-many localization. \n* Pixel-level localization: once the coarse location of a ground camera is given, the pixel-level localization aims to determine which pixel of the satellite image corresponds to the precise ground camera pose. This paper solves for the pixel-level localization. \n\n\n### Rework the presentation of the paper's contributions.\nThank you for the suggestion. Our contribution is re-worked below:\n\n* We introduce BevSplat, a new framework that synthesizes a Bird's-Eye View (BEV) representation by modeling the ground scene as a collection of feature-based 3D Gaussian primitives.\n* Our approach explicitly models 3D geometry to resolve the critical issue of height ambiguity, a limitation of traditional projection methods like IPM that leads to severe BEV distortions.\n* We demonstrate that BeVSplat achieves new state-of-the-art performance on the challenging KITTI and VIGOR datasets, significantly outperforming prior methods within the practical weakly-supervised localization paradigm.\n\n\n### Rotation Estimation\nYes, our rotation estimation module is adopted directly from G2SWeakly. We will make this explicit in our revised pipeline description.\n\n## 2. Why not compare with \"View from Above\", will claims like \"in cross-area evaluations, our method even surpasses supervised approaches\" (line 224) still hold?\n\nThe primary reason we did not include a direct comparison with \"View from Above (VFA)\" [20] was the unavailability of its official source code and pre-trained models, which prevents a direct and fair reproducible comparison. We note that even the most recent state-of-the-art supervised method, FG<sup>2</sup> [21] (CVPR'25), also omits a comparison to VFA for the same reason.\n\nTo address your crucial question about whether our claim on line 224 still holds, we have benchmarked our weakly-supervised method against this new fully-supervised SOTA method, FG²[21]. The results are presented below:\n\n\n| Methods      | λ1 | Same Area mean(m) ↓ | Same Area median(m) ↓ | Cross Area mean(m) ↓ | Cross Area median(m) ↓ |\n|:-------------|:--:|:-------------------:|:---------------------:|:--------------------:|:----------------------:|\n| FG^2[21] | -  |         **0.75**        |          **0.52**         |         7.45         |          4.03          |\n| Ours         | 0  |         5.82        |          2.85         |         7.05         |          3.22          |\n| Ours         | 1  |         2.87        |          2.06         |         **6.20**          |          **2.51**          |\n\n\nIt can be seen that, although it outperforms our methos on same-area evaluation, our method shows superior cross-area evaluation results, which are consistent in the conclusions in our submission.\n\n\n## 3. Comparison with other BEV projection Baselines, e.g. [5] and [20].\n\nWe thank the reviewer for this suggestion. To provide a comprehensive analysis, we have tried our best during this rebuttal period to re-implement \"View from Above\" [20] and adapt OrienterNet [5] to our weakly-supervised framework. The following sections present a direct comparison of performance, efficiency, and methodology.\n\n### Performance Comparision.\n| Method | λ1 | Same Area mean(m) ↓ | Same Area median(m) ↓ | Cross Area mean(m) ↓ | Cross Area median(m) ↓ |\n| :--- | :---: | :---: | :---: | :---: | :---: |\n| View from Above[20] | 0 | 19.95 | 18.42 | 20.98 | 19.43 |\n| OrienterNet[5] | 0 | 15.59 | 13.68 | 16.15 | 13.8 |\n| G2SWeakly[1] | 0 | 12.03 | 8.10 | 13.87 | 10.24 |\n| Ours | 0 | **5.82** | **2.85** | **7.05** | **3.22** |\n| View from Above[20] | 1 | 5.93 | 3.82 | 13.54 | 9.85 |\n| OrienterNet[5] | 1 | 5.71 | 3.20 | 10.02 | 5.07 |\n| G2SWeakly[1] | 1 | 6.81 | 3.39 | 12.15 | 3.16 |\n| Ours | 1 | **2.87** | **2.06** | **6.20** | **2.51** |\n\n\n### Runtime and Memory Analysis.\n\n| | View from Above | OrienterNet | G2SWeakly | Ours |\n|:---|:---:|:---:|:---:|:---:|\n| Training Memory | 62.0GB | 32.3GB | 22.7GB | **9.2GB** |\n| Inference Memory | 21.8GB | 10.8GB | **7.2GB** | 7.7GB |\n| Inference Time | 209ms | 71ms | **31ms** | 44ms |\n\n\n### Implementation Details.\nFor a rigorous comparison, we adapted the baselines to our weakly-supervised setting.\n\n* **View from Above [20]**: Due to the unavailability of public code, we re-implemented the method based on the paper's descriptions.\n* **OrienterNet [5]**: We used the official code, modifying only its satellite feature extractor to match ours for a controlled comparison.\n* **Loss Adaptation**: Both baselines, originally designed for full supervision, were adapted to our weakly-supervised tasks (λ1=0: No GPS, λ1=1: Noisy GPS) using our loss framework (Eq. 7) to ensure a meaningful evaluation.\n\n### Analysis of Experimental Results.\n\n#### View from Above [20].\n* **Impractical Horizon Assumption**: Relies on a fixed horizon height τ, which is not robust to real-world camera poses and leads to incorrect feature separation.\n* **Requires Strong Supervision**: Its keypoint selection mechanism needs precise camera poses for supervision, making it unsuited for our weakly-supervised task. This explains its severe performance drop, especially with No GPS (λ1=0).\n* **Inefficient**: Its iterative refinement design results in excessive computational costs. Our re-implementation required 62GB VRAM for training and 209ms for inference (the original paper reported 68GB and 222ms).\n\n#### OrienterNet [5].\n* **Poor Generalization for Occluded Regions**: OrienterNet produces a dense BEV by \"hallucinating\" or \"in-painting\" features for occluded regions. While this can work when guided by an accurate GPS label in same-area, it fails to generalize well to unseen environments, explaining its poor performance in cross-area evaluations.\n* **Ineffective Occlusion Handling**: It uses simple weighted averaging for BEV projection, which is less accurate for handling vertical occlusions than BeVSplat's principled alpha blending.\n* **Computationally Heavy**: OrienterNet uses a complex h x w x d polar depth representation to perform an attention-based sum over ground features. This implicit, high-dimensional process incurs substantial computational and memory overhead to produce a denser BEV.\n\n\n### Consistent with Broader Findings.\nOur findings align with broader trends for complex models in this domain. For instance, our baseline G2SWeakly[1] also benchmarked against a transformer-based counterpart[4] (see Table 2 in their paper). They similarly found that while the transformer had an advantage with Noisy GPS (λ1=1), its performance degraded significantly in the No GPS (λ1=0) setting. This supports our conclusion that complex architectures often struggle to generalize without strong supervisory signals.\n\n\n## 4. Conclusion.\nIn summary, BevSplat outperforms these strong baselines because it avoids their simplifying assumptions and computationally expensive designs. Our explicit 3D Gaussian representation is more robust, generalizes better, and is inherently suited for weak supervision. We acknowledge a minor speed trade-off compared to the simpler G2SWeakly, which we identify as a direction for future work.\n\n### References\n[4] Shi, Yujiao, et al. \"Boosting 3-dof ground-to-satellite camera localization accuracy via geometry-guided cross-view transformer.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.\n[21] Xia, Zimin, and Alexandre Alahi. \"FG^2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025."}]

Author rebuttal:

Meta-review: This paper introduces BevSplat, a weakly-supervised method that uses 3D Gaussian Splatting to create a high-quality Bird's-Eye-View (BEV) feature map from a ground image, which is then matched against a satellite map for accurate localization. Its novel use of 3D Gaussians effectively solves key distortion issues in prior methods, leading to state-of-the-art performance on standard benchmarks despite requiring only weak supervision.

After rebuttal, the final rates are 2 Borderline accepts and 2 Accepts. The authors solved reviewers' concerns on the robustness, supervision settings,  comparison of multi-frame fusion and more baselines. The reviewers give positive comments and recognition. Given these positive reviews, the AC recommends acceptance.
Decision: Accept (spotlight)