Enhancing Visual Localization with Cross-Domain Image Generation
Authors: Yuanze Wang
Yichao Yan
Shiming Song
Songchang Jin
Yilan Huang
Xingdong Sheng
Dianxi Shi
Venue: ICML 2025 poster (2025)
Keywords: Cross-Domain Visual Localization; Image Generation; 3D Gaussian Splatting; Image Editing
Primary area: applications->computer_vision

Abstract: Visual localization aims to predict the absolute camera pose for a single query image. However, predominant methods focus on single-camera images and scenes with limited appearance variations, limiting their applicability to cross-domain scenes commonly encountered in real-world applications. Furthermore, the long-tail distribution of cross-domain datasets poses additional challenges for visual localization. In this work, we propose a novel cross-domain data generation method to enhance visual localization methods. To achieve this, we first construct a cross-domain 3DGS to accurately model photometric variations and mitigate the interference of dynamic objects in large-scale scenes. We introduce a text-guided image editing model to enhance data diversity for addressing the long-tail distribution problem and design an effective fine-tuning strategy for it. Then, we develop an anchor-based method to generate high-quality datasets for visual localization. Finally, we introduce positional attention to address data ambiguities in cross-camera images. Extensive experiments show that our method achieves state-of-the-art accuracy, outperforming existing cross-domain visual localization methods by an average of 59\% across all domains. Project page: https://yzwang-sjtu.github.io/CDG-Loc.

Enhancing
Visual Localization with Cross-Domain Image Generation
Yuanze Wang' Yichao Yan
Shiming Song? Songchang Jin"
Yilan Huang?
Xingdong Sheng?
Dianxi Shi
Abstract
Query Images
Visual localization aims to predict the absolute camera pose for single query image. However, predominant methods focus on single-camera im- ages and scenes with limited appearance varia- tions, limiting their applicability to cross-domain scenes commonly encountered in real-worle ap- plications. Furthermore, the long-tail distribu- tion of cross-domain datasets poses additional challenges for visual localization. In this work, we propose novel cross-domain data genera- tion method to enhance visual localization meth- ods. To achieve this, we first construct cross- domain 3DGS to accurately model photometric variations and mitigate the interference of dy- namic objects in large-scale scenes. We intro- duce a text-guided image editing model to en- hance data diversity for addressing the long-tai tail distribution problem and design an effective fine- tuning strategy for it. Then, we develop an anchor- based method to generate high-quality datasets for visual localization. Finally, we introduce positional attention to address data ambiguities in cross-camera images. Extensive experiments show that our method achieves state-of-the-ar accuracy, outperforming existing cross-domain visual localization methods by an average of 59% across all domains. Project page: https: //yzwang- sjtu. github. io/ CDG-CCGGOO
Photomerry same region vames ommabically
360 Images
Fishrye Images
Pinhole Images
Domain Consistent
Test
Render
Tran
Visual
Prodlct
localization
Cross- Domain 3D Caussians
Cress- Domala Images with Positional A Attetionn
Enbancing with Cross- Cross-Domaa Domain Images
Query
Figure I. Demonstration of the proposed method. Our method achieves state-of-the-ar cross-domain localization performance by rendering domain-consistent images with positional attention for data augmentation.
augmented reality. Successful visual localization methods have been developed, such feature matching- tech- niques (Zhou et al., 2022; Sarlin et al., 2021; 1 Zhou et al., 2025), scene coordinate regression methods (Wang et al., 2023; Brachmann et al., 2023; 2025; Wang et al., 2024a), and absolute pose regression methods (Chen et al., 2024b; Shavit & Keller, 2022: Chen et al., 2022a; Shavit al., 2021). Current methods focus single-domain visual lo- calization, typically applied to images from single camera (e.g.. pinhole cameras) and scenes with limited appearance variations. However, single-domain methods encounter two major challenges in real-world applications: 1) Query im- ages captured by pinhole cameras, 360 cameras, fisheye cameras, and other cameras exhibit significant differences due to variations in distortion and field of view. 2) The appearance of scenes undergoes substantial variations over time, influenced by factors such lighting conditions and dynamic objects. Due to the limited training data of single- domain methods, significant domain differences arise be- tween these methods and the real-world domain, lcading to a drop in localization performance. Recently, 360LoceHHangg
1. Introduction
Visual localization is the process of estimating the position and orientation query image in 3D space. is crucial in applications such as robotics, autonomous driving, and
'MoB Key Lab Artificial Intelligence, Al Institute, Shanghai Jiao Tong University, Shanghai, China 'Intelligent Game and De- cision Lab (IGDL). Beijing, China 'eenovo Research, Shanghai, China 'Department of Big Data Intelligence, Advanced Institute of Big Data, Beijing, 100195, China. Correspondence to: Dianxi Shi <dxshi mustii@ndd..cu..cn>>.....
Proceedings of the 4nnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Enhancing Visual Localization with Cross-Domain Image Generation
et al., 2024) pioneered the exploration of these challenges by mapping limited 360 images to cross-camera images for data augmentation, achieving state-of-the-art performance. However, there remains considerable room for improvement due to the limited diversity of the generated data.
duced by the editing model.
To ensure that cross-domain 3DGS generates high high-quality images for visual localization, we develop an anchor- based image generation method. This method guarantees that the generated images are correctly associated with photometric embeddings and uniformly distributed within the range of accurate reconstruction. We then generate cross-camera images by leveraging the camera models. These images exhibit different distortions and fields of view, leading to the misali misalignment ignment of appearance features across images corre- sponding to the same pose. This introduces data ambiguity, making it difficult for visual localization methods to learn ef- fectively. To address this, we propose an efficient positional attention mechanism to align appearance features, thereby eliminating data ambiguity.
In light of these challenges, our research focuses on devel- oping an advanced cross- dataset generation method to enhance visual localization performance, as illustrated in Fig. 1. This paper concentrates on data augmentation for absolute pose regression (APR) methods, which require image-pose pairs as training datasets. To achieve this, we adopt 3D Gaussian Splatting (3DGS) (Kerbl et al., 2023) for its capability to synthesize high-fidelity images from arbitrary views in real time, thus enhancing the diversity of data for visual localization. However, generating cross- domain datasets based on 3DGS faces two main challenges: 1) Photometric variations and dynamic objects disrupt the multi-view consistency required by 3DGS methods, making it difficult to reconstruct accurate and domain-consistent scene appearances. 2) Due to the high costs in time and la- bor, training datasets often exhibit a long-tail distribution, which prevents 3DGS from fully learning the scene repre- sentations across all domains. For instance, data collected under daytime conditions is abundant (primary domain), while data from nighttime conditions is extremely sparse (secondary domain) in our evaluated dataset.
Extensive experiments demonstrate that our method achieves state-of-the-art visual localization performance, with more than 59% average improvement across domains. Our main contributions are summarized as follows:
We developed a cross-domain 3DGS to generate real- domain consistent images by modeling photometric variations and mitigating the interference of dynamic objects in large-scale scenes. Furthermore, we de- signed a two-stage training strategy to reduce the inter- ference of hallucination noise.
To address these challenges, we propose a novel cross- domain 3D Gaussian Splatting to model large-scale scene photometric variations while mitigating the interference of dynamic objects. We introduce learnable photometric embeddings that encode image photometric histograms to represent scene photometry. These embeddings are inte- grated with the anchor-based Gaussian features proposed by Scaffold-GSSL GLLu et al., 2024) to model scene photomet- ric variations, achieving strong scene fitting and reduced Gaussian storage. Additionally, we introduce dynamic pho- tometric embeddings, Gaussian dynamic confidence, and Gaussian dynamic photometry to ignore dynamic objects and compensate for photometric losses. Unlike WildGaus- sians (Kulhanek et al., 2024), our method encodes pho- tometric histograms and is not constrained by the perfor- mance bottlenecks of pre-trained detectors (Oquab et al., 2023). Considering that sparse secondary domain images hinder 3DGS from accurately learning cross-domain scene representations, employ text-guided image editing model (Brooks et al., 2023) to augment secondary domain images by transforming primary domain images. Recog- nizing the domain inconsistencies between the pre-trained editing model and real-world domains, as well as the pres- ence of hallucination noise, we carefully design an efficient fine-tuning strategy to adapt the editing model to our task. Furthermore, we propose two-phase training strategy for cross-domain 3DGS to mitigate hallucination noise intro-
We introduce a text-guided image editing model to enhance data diversity for addressing the long-tail dis- tribution problem and design an efficient fine-tuning strategy to adapt the model to our task.
We develop anchor-based method to generate high- quality datasets for visual localization and introduce positional attention to eliminate data ambiguity.
2. Related Work
2.1. Visual Localization
Several studies have explored cross-domain visual localiza- tion under pinhole camera settings. To enhance localization performance, (Porav et al., 2018) uses invertible genera- tors to produce synthetic images, while (Anoosheh et al., 2019) converts nighttime images to a more discriminative daytime representation. Other methods focus on learning Hommiin-nvariian features to bridge the gap between varying environmental conditions (Hu al., 2019). Additionally, several works (Tang al., 2020: Chen et al., 2023) advo- cate for disentangling image representations into separate codes that isolate place-specific cues from appearance and occlusion factors, ensuring reliable place recognition. How- ever, these methods are limited to single-camera localization and rely on image retrieval. Compared to APR methods
2

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Enhancing Visual Localization with Cross-Domain Image Generation
(Clark al., 2017; Moreau et al., 2022; Chen et al., 2024a), the retrieval-based methods suffer from significantly higher computational costs and storage requirements due to the need to construct and maintain a retrieval database. This paper enhances APR-based cross-domain localization, in- cluding cross-camera scenarios, by proposing a novel cross- domain image generation method. Unlike the prior method (Huang et al., 2024) with limited image augmentation, our method facilitates diverse cross-domain image generation.
Diffusion (Rombach et al., 2022) enable complex trans- formations through realistic image generation from text prompts. In 3D scene editing, instruction-guider methods (Chen et al., 2024c; Haque et al., 2023) allow manipula- tion of scene elements, such as object location, shape, and lighting, using natural language instructions. The state-of- the-art techniques (Tschernezki et al., 2022) distill knowl- edge from generated multi-view images using pre-trained models. However, the images generated directly using the pre-trained model are misaligned with the target domain, which undermines localization accuracy. To address this, we carefully design an effective fine-tuning strategy to train the editing model to generate consisttn images.
2.2. Neural Rerendering for Unconstrained Scenes
Recent advances in neural rendering have enabled the 3D reconstruction from unconstrained scenes. Neural Rerender- ing in the Wild (Meshry et al., 2019) combines traditional 3D reconstruction with neural networks to handle uncon- strained scenes. Extensions to the Neural Radiance Field (NeRF) -Martin-Brualla et al., 2021; Chen et al., 2022b) further address challenges in unconstrained scenes by em- bedding appearance information and transient uncertainty. In addition, methods such Neural Scene Chronology (Lin al., 2023) focus on capturing temporal variations by employing modules for appearance hallucination and temporal step function encoding. However, their slow train- ing and rendering make large-scale scene modeling and data generation time-consuming. Additionally, the limited parameters of NeRF (Mildenhall et al., 2021) hinder its abil- ity to effectively represent large outdoor scenes. Recently, baGS-based methods have garnered attention due to their faster optimization and rendering efficiency compared to NeRF Among them, SpotLessSplats (Sabour et al., 2025), GS-W (Zhang et al., 2025), WE-GS (Wang et al., 2024b), and WildGaussians (Kulhanek et al., 2024) have shown potential in modeling appearance variations and dynamic objects in wild scenes. However, they cannot explicitly control photometric properties and are constrained by the performance bottlenecks of pre-trained detectors (Oquab et al., 2023). Existing methods are also not suitable for scenes with long-tail distribution problems. In this paper, we propose a method that models appearance variations by encoding photometric histograms, mitigates the impact of dynamic objects without relying on pre-trained detectors, and employs a fine-tuned image editing model to effectively address the long-tail distribution problem.
3. Preliminaries
Scaffold-GS (Lu et al., 2024) uses set of 3D anchor struc- tures to model the entire scene. Each anchor stores learnable Gaussian features fg. and employs multilayer perceptron to predict the properties of Gaussians inherited from the an- chor representation. Compared to the original 3D Gaussian structure (Kerbl et al., 2023), Scaffold-GS offers a more compact representation and stronger scene modeling capa- bilities, allowing for flexible modeling of dynamic scene ap- pearances. Similar to traditional 3DGS, it utilizes tile-based rasterization for efficient image rendering. The geometry of each 3D Gaussian G; is defined in world space:
Gx = e 1 (xx-p.)" 2, ((x-p.)
(I)
where x is an arbitrary position within the 3D scene, P; and I; denote the position and covariance matrix of 3D Gaus- sians, respectively. E, uses semi-definite parameterization to constrain itself to the space of valid covariance matrices:
Σ RSST
(2)
where R is rotation matrix and S is scaling matrix. Addi- tionally, each 3D Gaussian includes opacity a and color o; for image rendering. To render an image from given view, 3D Gaussians are first projected onto the image plane to obtain 2D Gaussians G² The 2D Gaussians are then processed using tile-based rasterization and alpha-blending to produce the final rendered image:
i-1 C(x) (ax)==xGoon C(a)((())) II - [[I-a,0((x')) ax))(x)), J=1
(3)
VEN
2.3. Image Editing
where x' and denote the queried pixel position and the number of 2D Gaussians associated with the queried pixel, respectively. All parameters of the 3D Gaussians are opti- mized end-to-end using a differentiable rasterizer.
Traditional methods focused on tasks like style transfer (Gatys, 2015; Liu al., 2024; Ojha et al., 2021), while re- cent approaches incorporate text-based guidance (Bao et al., 2023) (e.g., CLIP embeddings) to enhance editing. Recent progress in image editing (Zhang et al., 2023; Chefer et al., 2023; Li et al., 2024; Ceylan et al., 2023) leverages large pretrained models and generative techniques for targeted manipulations. Text-to-image diffusion models like Stable
4. Method
We aim to develop a visual localization method to accurately estimate the poses of dross-domain query images, given a
3

---

Enhancing Visual Localization with
Image Generation
Cross-domain 3D Gaussian Splatting
First Stage Training
Temporal Prompt
Encode
Render
Primary Domain
Fine-tuning Editor
A Augment
Fl P
Freezing Geometry
3D Gaussians
Second Stage Training
Photometric H
Embeddings
Rendered Images
Secondary Domain
Training for Visual L Localization
Online Mapping
Render
Combine
Enhance
Predict
APR
Anchor Map
360 Images
Cross-camers Images
Positional A Attentiona
Visual localization
Query Pose
Figure 2. Overview of our method. We first construct a cross-domain 3DGS to accurately model photometric variations and mitigate the interference of dynamic objects. Specifically, we construct learnable embeddings encode photometric histograms. These embeddings, along with Gaussian features, are fed into two separate MLPs to predict the static and dynamic attributes of the Gaussians. We fine-tune an image editing model to generate secondary domain images for augmenting the sparse secoudary domain dataset. Furthermore, we propose a two-stage training strategy for cross-domain 3DGS to mitigate the hallucination noise introduced by the editing model. Next, we develop an anchor-based generation method to create high-quality datasets for visual localization. Finally, we implement an online mapping mechanism reduce data storage costs and introduce positional attention resolve data ambiguities.
limited training set consisting of 360 image-pose pairs shown in Fig. 2. To achieve this, first construct cross- domain 3DGS to accurately model photometric variations and mitigate the interference of dynamic objects in large- scale scenes. Next, we introduce image editing models that transform images from the primary domain to augment sparse secondary domains, addressing the long-tail distribu- tion problem. Finally, we propose an anchor-based method generate high-quality datasets for visual localization and introduce positional attention to resolve data ambiguities.
priors that vary with position and time. We employ the multi- layer perceptron (MLP) to predict Gaussian attributes by combining Gaussian features fn= proposed by Scaffold-GS (Lu et al., 2024) with the photometric embeddings, thereby achieving strong scene modeling capabilities. Please refer to the supplementary materials for more details.
Dynamic Object Suppression. Dynamic objects intro- duce noise that hinders the accurate reconstruction of scenes. To suppress the influence of dynamic objects, we introduce dynamic confidence 3 for the Gaussian attributes and render dynamic confidence maps M using booha-blending:
4.1. Cross-Domain 3D Gaussian Splatting
To accurately model photometric variations and ignore the interference from dynamic objects in the large scenes, we carefully design an efficient cross-domain 3DGS. The main components of this module include: (a) photometric varia- tion modeling, (b) suppression of dynamic objects, and (c) a two-phase training strategy.
M(x)
Σ B, II EEN
(1-0,) j=1
(4)
Inspired by NeRF-W MMartin-Brualle et al., 2021), we as- sign lower weights to the training loss in the regions with high dynamic confidence to ignore the dynamic objects, However, varying photometry is partially treated dynamic objects and thus ignored, distorting the photometric render- ing of the scene. To address this, we introduce dynamic photometric embedding ea to incorporate photometric varia- tion priors, assisting in the prediction of dynamic confidence. We also add a dynamic photometric h to the Gaussians to compensate for photometric loss. To prevent entanglement, two separate MI.P networks M.P.s. M.PPs are used to
Photometric Variation Modeling. The photometry in images varies significantly with position and time in real- world scenes, leading to the failure of 3DGS that depends on multi-view consistency. To address this challenge, we construct L learnable photometric embeddings e uniformly distributed across the entire scene based on mapping images. These embeddings encode the photometric histogram H of the corresponding mapping images, introducing photometric

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Enhancing Visual Localization with Pross-Domain Image Generation
predict static and dynamic attributes independently:
Temporal Transformation Dataset
Primary Domai
Temporal
= (es. ha. 8 MLPales. fgs),
Prompt
(5)
Make it nightime
Finetune
Image Editor
where Gg and C₂2 represent static color and static photometric embeddings. We can obtain the rendered image C:
Scene Prior Dataset
i-1
Secondary Domain
C(x) = -cs + oajo, II (1-0g), 2=1
(6)
Add dynamic objects the scene
IEN
Figure Illustration of secondary domain augmentation.
Two-phase Training Strategy. Since the training dataset consists of only 360 images, we first convert them into pin- hole camera images for training the cross-domain 3DGS, then map the rendered pinhole images back to 360 im- ages. Due to the significant photometric differences in cross- domain images and hallucination noise in the augmented secondary domain images (Sec. 4.2), directly training cross- domain 3DGS on all domain images makes difficult to learn accurate scene representations. Therefore, we train cross-domain 3DGS in two stages. In the first stage, we only use images from the primary domain, learning the curate appearance of the primary domain and the global scene geometry. Inspired by NeRF-W (Martin-Bruall) et al., 2021), we use D-SSIM loss and the photometric loss with uncertainty to mitigate the interference of dynamic objects:
the image editing model using carefully constructed tem- poral transformation dataset. Specifically, we render the primary domain images I, corresponding the sparse sec- ondary domain images Is using the first-stage 3DGS. Then, the temporal transformation datasets pair the original im- ages La the edited images 4b, and the editing text T.. We further randomly rotate the 360 images Io by the latitude 6, and longitude @ using the 360-camera projection function vo for dataset augmentation:
i. = to (R(O,, oe) (
(8)
Considering that sparse temporal transformation datasets fail to cover the entire global scene, leading to hallucination phenomena when the model encounters unseen areas. We constructed a scene prior dataset, which also helps prevent dynamic objects in the fine-tuning dataset from introducing dynamic noise into the model. Specifically, the scene prior datasets pair the rendered static images and dynamic images from the primary domain with editing text, such as add dynamic objects to the scene.
L C= ||C C - + 2
log M ) + (1 (D.SSI..
2M
Where À is the loss weight, C and Ö represent the ground truth and the rendered result, respectively. In the second stage, we fine-tune the first-stage 3DGS using augmented secondary domain images. Specifically, we freeze parame- ters related to scene geometry and only train parameters re- lated to photometric variations. This prevents hallucination noise from damaging the already reconstructed scene geom- etry. Finally, we use (CC, C) and LPIPS losses (Haque et al., 2023) effectively reduce the hallucination noise.
4.3. Training for Visual Localization
A Anchor-based Image Generation Method. To generate high-quality datasets for training visual localization meth- ods, we need ensure that the generated images are cor- rectly associated with photometric embeddings and uni- formly distributed within the range of accurate reconstruc- tion. Therefore, we develop an machor-based image gener- ation method. Specifically, photometric embeddings uni- formly distributed across the scene are used as anchors, with each anchor associated with a pose Pa, where Pa denotes the pose of the image encoded by the photometric embed- ding. Then, we randomly generate m poses P, within 3D spherical range with radius L, centered at the anchor. The radius is empirically determined as the distance be- tween adjacent anchors to ensure that the generated images fall within the range of accurate reconstruction. The pose Pg is computed using random displacements and random rotation R:
4.2. Secondary Domain Augmentation.
The scarcity of secondary domain data due to the long- tailed distribution problem of cross-domain datasets limits the ability of cross-domain 3DGS to learn accurate scene representations. We introduce a text-guided image editing model to address the long-tail distribution problem and de- sign an efficient fine-tuning strategy to adapt the model to our task, as shown in Fig. 3. Inspired by the powerful In- structPix2Pix (Brooks et al., 2023) based diffusion, we define temporal prompt T, for temporal transformation (c.g., make it nighttime) and leverage the editing model to transform primary domain images into the secondary do- main. However, directly using the pre-trained model results in domain inconsistencies. To address this, we fine-tune
Pg RPPP +1,
|t- S L.
(9)
5

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Enhancing Visual Localization with Cross-Domain Image Generation
Pinhole
Fisheyel
Fisheye2
Risheye:
Table 1. The settings for the mapping and query datasets.
Mapping
Scene
Atrium
Concourse
Hall
Piatrium
Primary/Sconndry
581/10
491/10
540/0
632/20
(Frames)
Camera Model
360
360
360
360
Spatial Extent (m")
2340
1395
5460
6860
Query
Camera
Fisheye /illeyeee/iibbeee2/ssseyyy
Pinhole
360
Field of View
120" /150" /195*
85
Resolution
6144x3072
1280x 1024
1920x 1200
Figure 4. Visualization of positional attention mapping across dif- ferent camera domains the glohal coordinate space.
We obtain the normalized positional attention S by using the dimensions D of the global coordinate space:
where represents the position of the anchor. Finally, render the image using the pose Pa and the photometric embedding of its anchor. This ensures that the correct pho- tometric embedding is associated during image rendering, which is crucial for generating domain-consistent images.
25.
S
(12)
D
Finally, the positional attention maps concatenated with the images along the channel and fed into the visual local- ization method, guiding cross-camera image alignment.
Online Cooss-Camera Images Generation. The gener- ated images are currently in the 360 format, we need to gen- erate cross-camera images. Thanks to the full field of view of 360 images, any camera image can be directly mapped from the 360 images using the mapping +.;;
5. Experiments
5.1. Datasets and Baselines
T, (To) vie,, (15 (To)).
(10)
We conduct experiments using the large-scale dataset 360Loc (Huang al., 2024), which includes dynamic ob- jects and significant variations in lighting conditions. The dataset consists of five camera domains, covering both day- time (primary domain) and nighttime (secondary domain). We construct a long-tailed distribution dataset by selectively sparse secondary domain data based on scene size. The set- tings for mapping and query datasets are detailed the Tab. To facilitate training and evaluation, we use SAM2 (Ravi et al., 2024) remove the fixed base the bottom of the image. For comparison, we employ state-of-the-art base- lines provided by cross-domain visual localization bench- marks (Huang et al., 2024). To ensure a fair comparison, we reproduce the baselines using the same setup as our method. Please refer to the supplementary materials for more details.
where v.- denote the unprojection function of 360 camera and eic, is the projection function of camera domain cn- If there are j camera domains, this requires generating m x images for each anchor. However, storing images for all camera domains incurs high storage costs. To address this, store only the mapping relationships Φ,, rendering the cross-camera images online during the training of the visual localization method. Since the mapping relationships are precomputed offline and dominate the computational cost of cross-camera mapping, the additional overhead introduced by online mapping is acceptable.
Positional Attention. The appearance features of cross- camera images with the same pose are not aligned in the image coordinate space due to different distortions and fields of view. We define this problem as data ambiguity, which lowers the accuracy of visual localization methods. To address this, we propose position attention to guide the alignment of appearance features. First, we construct a global image coordinate space S based on 360 images, which can cover the field of view of any camera domain. Then, convert the image coordinates So, from different camera domains to the global coordinate space So as shown in Fig. 4, which are used as position attention:
5.2. Implementation Details
Our method is built upon the widely-used open-source Scaffold-GS (Lu et al., 2024) codebase and employs the MS-T (Shavit et al., 2021) for visual localization. During cross-domain 3DGS training, we scale images to 400x400 and train for 60,000 iterations in the first stage, followed by fine-tuning for 20,000 iterations in the second stage. For the editing model, we fine-tune it for 10,000 iterations at a reso- lution of 512x512. The number L of photometric embed- dings is set half the number of the mapping dataset and the parameter is set to 24. During visual localization training,
- (Sa...
(11)
6

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Enhancing Visual Localization with Cross-Domain Image Generation
Table 2. Comparison of median translation and orientation error (m, ?) on the primary domain dataset.
Seene
Atriam Day
Concourse. Day
Camera
Pinbole
Fisbeycl
Fisbeye2
Fisbaye3
360
Average
Pinholc
Fisheyel
Fisheye2
Fishoyc3
360
Average
PN (Kendall et 2015, 2015)
15.385.5
11 1187799
11,3004
10.1779.8
4,582266
10.8816
18.995.0
10.7788.7
10.1/87.7
7.7/85.9
2.0/990
9.9/79.3
MS-T (Shavit et al., 2021)
20.1/77.7
14.573.77
14.0771.7
12.5/65.6
13.2/54.7
14.9168.7
17.069.11
11/60.8
10.5/59.6
9.96634
4.9/446
10.7/57.5
PN- (Huang 2024)
11,3312
9.191
8.9/19.3
$.4/1811
7.7/17.1
9.1/21.0
7.0/18.9
4.7/11.5
4.41122
3.9/999
3.266
4.6/11.7
MS-T-VC2 (Huing et al., 2024)
10.046.7
6.336.3
6.2/66.5
4.9/28.1
5,2/47.6
6.5739.0
3.5/19.6
19112.
18/18
1./12.
16/13
2.1/14.3
8
22127
21/12.3
1./111.5
1.014.1
2.6/1455
2.7/9.7
1,51577
1.55.5
1,/554
T.V5.7
1,5/K4
Scene
Hall Day
Piatrium Day
Camern
Pinhole
Pisheyel
Fisheye2
Hisheye3
300
Average
Pinhole
Fisheyel
Fisheye2
Fisheye
360
Avenge
PN (Kendall et al., 2015)
19,8/0088
11.1/95.7
10.495,9
7.5/92.3
2.797.3
10.1/96.1
21.3/88.0
13.8/86.5
13.0/87.1
10.9/84.4
5.7/69
12.9/83.2
MS-T (Shavit et al., 2021)
21 8/66
14.1/44.0
13,592.7
11,2/91.
11,488..5
14.492.6
30.0788.5
23.4/1.3
22.7/72.7
19.3/72.
11.3/2.0
21.3/99.
PN- (Huang et 2024)
7.0/26.4
4.4/15.5
4.3/14.7
3.9/14.2
3.2/12.2
4.6/16.6
10.4/24.5
6.9/13.3
6.8/12.8
6./11.
5.09988
7.2/14,4
MS-T-VC2 (Huing et al., 2024)
4./36.9
2.2/5.8
2.1/25.2
1.8/26.2
1,3/26.9
2.4/28.2
8.1/32.7
4.024..5
3.9/23.6
3.8/4.11
3.3/2.11
4.6/25.4
2.V/7.44
1/08.6
0.988.4
0.7/7.5
4.4/1..0
2.5/1L3
2.4/10.5
2,4/10.1
2.3/10.1
Table 3. Comparison of median translation and orientation error (m, 9) on the secondary domain datasets.
Soone
Atrium Night
Concourse. Night
Camera
Pinhole
Fishevel
Fisheyc2
Fisheye3
360
Average
Pinhole
Fisheyel
Fisheye2
Fisheyee
360
Average
PN (Kendall al., 2015)
19.8/90.8
16.4/87.3
16.1/86.7
15,7/83.5
8.7/22.8
15.3774.2
19,896.2
13.3885.
13,08884
11,187.5
5.5/5.66
12.5/90.6
MS-T (Shavit et al., 2021)
24.8/84.7
17.8/80.5
17.0/80.1
15.1/80.0
5.0466.
15,9/74.3
22.1/74.7
17,7/69.5
17.0/63.3
17.2/72.7
8.4/57.3
16.5168.5
PN-VC2 (Husng al., 2024)
13.650.2
11,5/38.1
11665883
11.4/7788
9.744.77
11.642.0
10.7/554
8.3/ 16.4
8.415.77
8.1/44.7
5.5/13.5
8.2/17.1
MS-T-VC2 (Huang ol., 2024)
13.5/70.6
9.164.1
9.06338
8.66444
7.366.5
9.50449
5.5/28.7
2.0/
3.W703
3,5/20
34/2377
4.1/22.7
Ours
4.6/25.3
L8/110
1.7/10.1
16/10.1
1,77.3
2.2/12.8
3,3/114
1,776.6
16663
1,46.9
1.16.1
18775
Scene
Hall Night
Piatrium Night
Camera
Pinhole
Fisheyel
Picheye2
Fisheye3
360
Avenge
Finhole
Fisheyel
Fisheye2
Fisheye
360
Average
PN (Kendall et al., 2015)
22.995.6
1/93
17.99366
168N133
10. 70001
17,384.9
33.2889.7
29.389
28,89014
29,289.
30.291
MS-T (Shavit al., 2021)
12.1/96.1
29.9/44.2
29.7/93.6
31,8/3.1
22.3/444
29.284.3
37,79113
37.3/9.1
36,88900
35,7885.6
37,283.0
368897.6
PN PN-VC2 (Hunng et al., 2024)
13.1/
10.5443.7
10.1/43.0
9.740..
7,5/4477
10.2/43.6
15.8/51.9
13,2/34.9
12.8/34.8 34.8
12.5/326
10.6/ 30.9
13.0/37.0
MS-T-VC2 (Huang et al., 2024)
8.9/3.11
5.6/
5.3/
688/
3.8
5.7168.6
16.01
13.0/
12/4/53.7
11,9/,199
11,9//19.9
13.0/51.2
Ours
4.2/22.5
2.2/18.5
2.1/17.5
1.9/5.77
1.5/18.0
2.4/20.4
7,2/27.9
4.6/16.1
4.3/16.1
3.7/14.1
2.6/9.6
4.5/16.8
images from all domains are resized to 256x256. Training and evaluation are performed on an NVIDIA GeForce GTX 4090 GPU. Please refer to the supplementary materials for more details.
for visual localization and validates the effectiveness of our secondary domain augmentation method.
According to the cross-camera localization results, we ob- serve that visual localization performance decreases as the field of view (FOV) of the camera narrows. Notably, for pinhole images with a small FOV, localization performance is significantly worse than the average of the other four cam- eras with a wide FOV (5.2/21.8 vs. 1.9/12.7 in Atrium_Day), even when effective data augmentation methods are em- ployed. The poor performance of pinhole images signifi- cantly lowers the overall average localization accuracy of our method. This demonstrates that visual localization per- formance faces a bottleneck due to the inherent limitations of a narrow FOV. These findings suggest that selecting cam- eras with a wide FOV is crucial for achieving better perfor- mance in practical visual localization applications.
5.3. Quantitative and Qualitative Results
Quantitative Results. To evaluate the performance of cross-domain localization, we apply the widely-used me- dian translation (m) and rotation (o). The cross-domain localization performance is reported in Tab. 2 for daytime and Tab. for nighttime. Our method achieves state-of- the-art results across all cross-domain scenes, significantly outperforming the previously leading MS-T-VC2 by an av- erage of 51% during the day and 67% at night. Additionally, our method significantly outperforms the original MS-T without data augmentation, achieving an 85% improvement in domain average. This demonstrates that visual localiza- tion is highly data-hungry, particularly in more challenging large-scale cross cross-domain scenes. Although MS-T-VC2 also utilizes data augmentation methods, it relies on limited map- ping data, which restricts the diversity of the augmented data. Furthermore, the basclines fail to address the long-tail tail problem of cross-domain data, preventing visual localiza- tion methods from learning sufficient scene features and leading to poor performance in secondary domains. This emphasizes the importance of augmenting data diversity
Qualitative Results The Fig. 5 shows the cross-domain image generation results of our method, covering both di- verse appearance domains and camera domains. It is evi- dent that our method generates domain-consistent images across all target domains, even in highly sparse secondary domain scenes. Moreover, the generated images success- fully ignore dynamic interference to ensure accurate scene reconstruction, which is essential for achieving robust visual localization performance. These results demonstrate that
7

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Enhancing Visual Localization with Cross-Domas Domain Image Generation
Groun Truth
Cross Dourin Image Gansratinn
360
Fisheye3
Fisheye2
Fisheyel
Pinhole
Figure 5. Cross-domain image generation results. Our method can generate accurate and Nomain--onsistee images, even in sparse sec- ondary domain Furthermore, generated images effectively ignore dynamic interference to accurate reconstruction.
the proposed method can generate high-quality and diverse training data for cross-domain visual localization.
Table 4. Ablation study on cross-domain 3DGS with metrics "PSNR 7 /LPIPS 4". The symbol 4/" indicates an invalid result.
5.4. Ablation Study
Domain
day
night
Domain
day
night
Effectiveness of Cross-Domain 3DGS. To evaluate the effectiveness of cross-domain 3DGS, conducted quali- tative comparison with the Scaffold-GS in terms of dynamic interference removal, photometric variation modeling, and sparse secondary domain scene reconstruction, as shown in Fig. 6. Our method achieves accurate static appearance reconstruction by effectively suppressing dynamic interfer- ence while precisely reconstructing Nomain--onsisseen ap- pearances in regions with significant photometric variations. As shown in the bottom row of the figure, the baseline fails to model scenes in the sparse secondary domain, while our method successfully reconstructs domain-consisten scenes. We perform a quantitative comparison using PSNR and LPIPS metrics on the Atrium datasets. As shown in Tab. our method significantly outperforms the baseline. This demonstrates that cross-domain 3DGS can accurately recon- struct scene representations across all domains, even in the sparse secondary domain.
Scaffold-GS
22.38/0.38
Ours
24.64/0.23
26.07/0.22
Effectiveness of Fine-Tuning Strategy. We effectively fine- tune the image editing model to generate domain-consistent images while reducing hallucinations and dynamic noise. To better demonstrate the effectiveness of this strategy, we perform a qualitative comparison with pre-trained model, as shown in Fig. 7. The top row shows that the pre-trained editing model fails generate such as in photometric distribution and color. In contrast, images.
images,
fine-tuned model generates Furthermore, as illustrated in the middle and bottom rows, our fine-tuned model effectively prevents the introduction of hallucinations and dynamic noise. We also conducted quantitative experiments on the atrium scene. We employed image similarity metries SSIM and LPIPS evaluate the
8

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Enhancing Visual Localization with Image Generation
Ground Truth
Pretmined Model
Finelmed Model
Global Scene Prior Dataset
Finetuned Model
Figure 7. Ablation study on fine-tuning strategy. Top row: Our fine-tuned model generates conain-consiitent images compared the pre-trained model. Middle row: Our fine-tuned model reduces hallucination noise relative to pre-trained model. Bottom row: Our method introduces scene priors to the model without introduc- ing dynamic noise.
Ground Truth
Scaffold-GS
Owr
Figure 6. Ablation study on cross-domain 3DGS. The top two rows show qualitative comparisons scenes with dynamic object interference and significant photometric variations, respectively. bottom two rows show comparison results panoramic images. The red box indicates the zoomed-in region.
Table 6. Ablation study on positional attention on primary datasets.
Camera
Piabole
Fisheyel
Fisheye2
Fisheye3
300
Average
wio Positional Atlention
4.7/20.6
2/4/2299
2,3/12.6
2.3/266
2.1/14.4
288/4.66
Full Model
Table 5. Ablation study on fine-tuning strategy.
the query images can impair cross-domain localization per- formance. Future improvements could involve continual learning to adapt the model to evolving scene geometry.
Metries
SSIM
LPIPS
Pretrained Model
0.338
0.428
Finetuned Model
0.709
0.337
7. Conclusion
In conclusion, this work presents novel cross-domain data generation method designed to enhance visual localization performance. We develop a cross-domain 3DGS that accu- rately models photometric variations while effectively sup- pressing dynamic object interference in large-scale scenes. We introduce an image editing model to enhance data di- versity for addressing the long-tail distribution problem and design an effective tinettuing strategy for Furthermore, we propose anchor-based dataset generation method to produce high-quality training data for visual localization and incorporate positional attention to address data ambiguities in cross-camera images. Experimental results demonstrate that our method significantly improves the performance of visual localization methods, providing a promising direction for advancing cross-domain visual localization.
similarity between the images generated and the ground truth. As shown in the Tab. 5, the image similarity sig- nificantly improved after fine-tuning. It demonstrates that our fine-tuning strategy effectively reduces the domain in- consistencies between the pre-trained editing model and real-world domains.
Effectiveness of Positional Attention. Positional attention effectively resolves data ambiguity by aligning appearance features of cross-camera images in global coordinate space, thereby enhancing visual localization performance. To eval- uate the effectiveness of positional attention, we assess the visual localization performance without positional attention the primary domain scenes, as shown in Tab. 6. The posi- tional attention significantly improves overall localization performance (2.0/10.8 2.8/14.6), demonstrating that it effectively resolves data ambiguity.
Impact Statement
6. Limitation
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences our work, none which we feel must be specifically highlighted here.
In real-world scenarios, the geometric structure of the scene may change over time. Significant geometric changes within

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Enhancing Visual Localization with Cross-Domain Image Generation
References
the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12943- 12952, 2022b.
Anooshch, A., Sattler, T., Timofte, R., Pollefeys, M., and Van Gool, L. Night-to-day image translation for retrieval- based localization. In 2019 International conference on robotics and automation (ICRA), pp. 5958-5964, 2019.
Chen, Y., Chen, X., and Li, Y. Place recognition under occlusion and changing appearance via disentangled rep- resentations. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 1882-1888, 2023.
Bao, C.,, Zhang, Y., Yang, B., Fan, T., Yang, Z., Bao, H., Zhang, G., and Cui, Z. Sine: Semantic-driven image- based nerf editing with prior-guided editing field. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20919- 209299,, 2023.
Chen, Y., Chen, Z., Zhang, C., Wang, F., Yang, X., Wang, Y., Cai, Z., Yang. L., Liu, H., and Lin, G. Gaussianeditor: Swift and controllable 3d editing with gaussian splatting. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pp. 21476-21485, 2024c.
Brachmann, E., Cavallari, T., and Prisacariu, V. A. Ac- celerated coordinate encoding: Learning to relocalize in minutes using rgb and poses. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5044-5053, 2023.
Clark, R., Wang. S., Markham, A., Trigoni, N., and Wen, H. Vidloc: A deep spatio-temporal model for 6-dof video- clip relocalization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6856- 6864, 2017.
Brachmann, E.,, Wynn, J., Chen, S., Cavallari, T., Monszpart, Á. Turmukhambetov, D., and Prisacariu, V. A. Scene coordinate reconstruction: Posing of image collections via incremental learning of relocalizer. In European Conference on Computer Vision, pp. 421-440, 2025.
Gatys, L. A. A neural algorithm of artistic style. arXiv preprint Tt0.::5508.66511 2015.
Brooks, T., Holynski, A., and Efros, A. A. Instructpix2pix: Learning to follow image editing instructions. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13392-18402. 2023.
Haque, A., Tancik, M., Efros, A. A., Holynski, A., and
Kanazawa, A. neffneft Editing 3d scenes with instructions. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pp. 19740-19750, 2023.
Ceylan, D., Huang, C.-H. P., and Mitra, N. J. Pix2video: Video editing using image diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 23206-23217, 2023.
Hu, H., Wang, H., Liu, Z.., Yang, C., Chen, W., and Xie, L. Retrieval- based localization based feature learning under changing environments. In 2019 IEEE/RSJ international conference on intelligent robots 2019.
invariant
Chefer, H., Alaluf, Y., Vinker, Y., Wolf, L.. and Cohen-Or, D. Attend-and-excite: Attention-basee semantic guidance for text-to-image diffusion models. ACM Transactions on Graphics (TOG), 420 2023.
and systems (IROS), pp.
Huang, H., Liu, C., Zhu, Y., Cheng, H., Braud, T., and Yeung, S.-K. 360loc: A dataset and benchmark for omni- directional visual localization with cross-device queries. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Patlern Recognition, pp. 22314-22324, 2024.
Chen, S., Li, X., Wang. Z., and Prisacariu, V. A. Dfnet: En- hance absolute pose regression with direct feature match- ing. In European Conference Computer Vision, pp. 1-17, 2022a.
Chen, S., Bhalgat, Y., Li, X., Bian, J.-W., Li, K., Wang, Z.., and Prisacariu, V. A. Neural refinement for absolute pose regression with feature synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20987-20996, 2024a.
Kendall, A., Grimes, M., and Cipolla, R. Posenet: A convo- lutional network for sal-timee 6-dof camera relocalization. In Proceedings of the IEEE international conference on computer vision, pp. 2938-2946, 2015.
Chen, S., Cavallari, T., Prisacariu, A., and Brachmann, E. Map-relative pose regression for visual re-localization. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 22665, 2024b.
Kerbl, B., Kopanas, G., Leimkühher, T., and Drettakis, G. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139-1, 2.22.
Kulhanek, J., Peng, S., Kukelova, Z., Pollefeys, M., and Sattler, T. Wildgaussians: 3d gaussian splatting in the wild, arXiv preprint arXiv:2407.0m 004477 2024.
Chen, X., Zhang, Q., Li, X., Chen, Y., Feng. Y., Wang, X., and Wang, Hallucinated neural radiance fields in
10

---

Enhancing Visual Localization with Cross-Domain Image Generation
Li, D., Li, J., and Hoi, S. Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing, Advances in Neural Information Processing Systems, 36, 2024.
conference on robotics and automation (ICRA), pp. 1011 1018, 2018.
Ravi, N., Gabeur, V., Hu, Y.-T., Hu, R., Ryali, C., Ma, T., Khedr, H., Rädle, R., Rolland, C., Gustafson, L., et al. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.000 14, 2024.
Lin, H., Wang, Q. Cai, R., Peng, S., Averbuch-Elor H., Zhou, X., and Snavely, N. Neural scene chronology. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 20752-20761, 2023.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition, pp. 10684-10695, 2022.
Liu, K., Zhan, F., Xu, M., Theobalt, C., Shao, L., and Lu, S. Stylegaussian: Instant 3d style transfer with gaussian splatting, In SIGGRAPH Asia 2024 Technical Communi- cations, pp. 1-4. 2024.
Sabour, S., Goli, L., Kopanas, G., Matthews, M., Lagun, D., Guibas, L., Jacobson, A., Fleet, D., and Tagliasacchi, A. Spotlesssplats: Ignoring distractors in 3d gaussian splatting. ACM Transactions on Graphics, 44(2):1-11, 2025.
Lu, T., Yu, M., Xu, I.., Xiangli, Y., Wang, I.., Lin, D., and Dai, B. Scaffold-gss gs: Structured 3d gaussians for view- adaptive rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20654-206644 2024.
Sarlin, P.-E., Unagar, A., Larsson, M., Germain, H., Toft, C., Larsson, V., Pollefeys, M., Lepetit, V., Hammarstrand, L., Kahl, F,, et al. Back the feature: Learning robust camera localization from pixels to pose. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, pp. 3247-3257, 2021.
Martin-Brunn Brualla, R., Radwan, N., Sajjadi, M. S., Barron, J. T., Dosovitskiy, A., and Duckworth, D. Nerf in the wild: Neural radiance fields for unconstrained photo collections. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7210-7219, 2021.
Meshry, M., Goldman, D. B., Khamis, S., Hoppe, H., Pandey, R., Snavely, N., and Martin-Brualla, R. Neural rerendering in the wild. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 6878 6887,
Shavit, and Keller, Y. Camera pose auto-encoders for improving pose regression. In European Conference on Computer Vision, pp. 140-157, 2022.
Shavit, Y., Ferens, R., and Keller, Y. Learning multi-scene absolute pose regression with transformers. In Proceed- ings of the IEEE/CVF International Conference on Com- puter Vision, pp. 2733-2742, 2021.
Mildenhall, B., Srinivasan, P., Tancik, M., Barron, J. T., Ramamoorthi, R., and Ng. R. Nerf: Representing scenes as neural radiance fields for view synthesis. Communica- tions of the ACM, 65(1):99-44)) 1096 2021.
Tang, L., Wang, Y., Luo, Q.. Ding, X., and Xiong, R. Adversarial feature disentanglement for place recogni- tion across changing appearance. In 2020 IEEE Interna- tional Conference on Robotics and Automation (ICRA), pp. 1301-1307, 2020.
Moreau, A., Piasco, N., Tsishkou, D., Stanciulescu, B., and de La Fortelle, A. Lens: Localization enhanced by nerf synthesis. In Conference on Robot Learning, pp. 1347- 1356, 2022.
Ojha, U., Li, Y., Lu, J., Efros, A. A., Lee, Y. J., Shechtman, E., and Zhang, R. Few-shot image generation via cross- domain correspondence. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10752, 2021.
Tschernezki, V., Laina, I., Larlus, D., and Vedaldi, Neural feature fusion fields: 3d distillation of self-supervised 2d image representations. In 2022 International Conference on 3D Vision (3DV), pp. 443-453, 2022.
Wang, F., Jiang, X., Galliani, S., Vogel, C., and Pollefeys, M. Glace: Global local accelerated coordinate encoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21562-21571, 2024a.
Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El- Nouby, A., et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023.
Wang. Y., Yan, Y., Shi, D., Zhu, W., Xia, J., Jeff, T., Jin, S., Gao, K., Li, X., and Yang, X. Nerf-ibvs: visual servo based on nerf for visual localization and navigation. Advances in Neural Information Processing Systems, 36: 2023.
Porav, H., Maddern, W., and Newman, P. Adversarial train- ing for adverse conditions: Robust metric localisation using appearance transfer. In 2018 IEEE international

---

Enhancing Visual Localization with Cross-Domain Image Generation
Wang, Y., Wang, J., and Qi Y. We-gs: An in-the-wild efñi
cient 3d gaussian representation for unconstrained photo collections, arXiv preprint arXiv:2406.02407, 2024b.
Zhang, D., Wang. C., Wang, W., Li, P., Qin, M., and Wang, H. Gaussian the wild: 3d gaussian splatting for uncon- strained image collections. In European Conference Computer Vision, pp. 341-359, 2024.
Zhang, D., Wang. C., Wang, W., Li, P., Qin, M., and Wang, H. Gaussian the wild: 3d gaussian splatting for uncon- strained image collections. In European Conference Computer Vision, pp. 341-359, 2025.
Zhang, L.., Rao, A., and Agrawala, M. Adding conditional control text-to-image difftusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3836-3844 2023.
Zhou, Q. Agostinho, S., Ošep, A., and Leal-Taixé, Is geometry enough for matching in visual localization? In European Conference on Computer Vision, pp. 407-425, 2022.
Zhou, Q. Maximov, M., Litany, O., and Leal-Taixé, The nerfect match: Exploring nerf features for visual localiza- tion. In European Conference on Computer Vision, pp. 108 127, 2025.
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Enhancing Visual Localization with Cross-Domain Image Generation
A. Implementation Details
Cross-Domain 3D Gaussian Splatting. We use static photometric embeddings with a dimension of x and dynamic photometric embeddings with dimension of L 2, while the photometric histogram has a dimension of 10. The photometric embeddings encoding the histogram are concatenated with the Gaussian features and fed into two separate MLPs predict the static and dynamic attributes of the Gaussians. Each MLP has depth of 2 and hidden layer dimensions of 64. To avoid division by zero in the training loss, we add bias of 0.1 to the dynamic confidence map. For the first stage of cross-domain 3DGS, we set the loss weight À = 0.8. The loss weight of the second stage is set to I for both components. We utilize even-indexed images for training the cross-domain 3DGS, while using odd-indexed images for evaluation purposes. We map panoramic images to pinhole images using cubemap projections to train the 3DGS.
For data augmentation, we rotate the temporal transformation dataset by randomly sampled angles. During fine-tuning of the instruct-pix2pix editing model, we the random flip, mixed precision, initial learning rate of X 10-3 and batch size of 4. When converting primary domain data to secondary domain data, set the image-guides and text-guided weights to 1.5 and 7, respectively, and configure 50 iterations for inference.
Training for Visual Localization. The original mapping dataset (Huang et al., 2024) does not contain any secondary domain (nighttime) scenes. Therefore, we construct long-tailed distribution mapping dataset by selecting secondary domain images from the query set based on scene size. The specific sources of the selected images are shown in Tab. 7. To prevent data leakage, the selected mapping images are removed from the query images. For fair comparison, we reproduce all baselines on the long-tailed distribution data constructed in this paper. Additionally, all images from all domains resized to 256x256. We set an initial learning rate of À 10 and a batch size of 32 for 300 epochs for both our method and the baselines.
Relationship between Rendering Quality and Localization Performance. To evaluate the impact of rendering quality, we conduct a quantitative experiment on the Atrium scene. Specifically, we compare the original Scaffold-GS (which produces lower-quality renderings compared to our cross-domain 3DGS) combined with our proposed data generation and positional attention mechanism. Localization performance drops (2.4/13.65 vs. 5.95/27.85 785) when using the lower-quality images generated by Scaffold-GS, This result demonstrates a positive correlation between rendering quality and localization accuracy.
Table 7. The specific sourees sparse secondary domain images.
atrium/query 360/nighttime
360
24
58
114
221
226
283
353
394
527
576
.36.0
2
53
132
155
232
257
321
366
410
461
_360_
54
|
80
110
136
162
188
214
240
266
292
318
348
374
403
426
452
555
582
607
Niatrium/qerry3660mmg
360.0
2
26
50
74
98
122
146
170
194
218
254
266
362
386
410
434
458
482
663
674
B. More Results
Effectiveness of Cross-Domain 3DGS. We have included comparative experiments with the advanced GS- W (Zhang et al., 2024), which is designed for unconstrained scenes. Since GS-W does not address the long-tail data distribution issue, we conduct quantitative experiments on the Atrium Day scene, which contains sufficient data. Additionally, we extend its training iterations to 140,000 for the large-scale evaluation scene. As shown in the Tab. 8, our method significantly outperforms GS-W W while using fewer iterations (60,000). This demonstrates that our method has stronger scene modeling capabilities for large-scale scenes.
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Enhancing Visual Localization with Cross-Domain Image Generation
Table 8. Ablation study on fine-tuning strategy.
Metries
PSNR1
SSIM f LPIPS
GS- W (Zhang et al., 2024)
18.85
0.635
0.594
Cross-domain 3DGS (Ours)
24.64
0.758
0.231
Effectiveness of Anchor-Based Image Generation. To ensure that randomly generated images for visual localization are correctly associated with photometric embeddings and uniformly distributed within the range of accurate reconstruction, we develop an anchor-based image generation method. To validate the effectiveness of anchor-basee approach, we compare qualitatively with the method that randomly associates photometric embeddings. As shown in Fig. 8, is evident that random association of photometric embeddings often leads inconsistency with the photometric distribution of the current pose, resulting in domain- intossiiiiconsstteett images. Since first render pinhole images and then map them 360 images, this process exacerbates the aforementioned photometric inconsistencies, leading to issues such the formation of dark bright spots. In contrast, method is associated with accurate photometric embeddings, generating domain comain-consistent and globally photometrically consistent images. This demonstrates the effectiveness of our proposed anchor-based image generation method.
Effectiveness of Fine- ToinegTTnnnggg Strategy. We designed an effective fine-tuning strategy to train the image editing model (Brooks et al., 2023) in order to transfer the pre-trained domain to the real domain. To demonstrate the effectiveness the proposed fine-tuning strategy, we compare it qualitatively with the pre-trained model, as shown in Fig. the pre-trained model only generates images that are inconsistent with the real domain in terms of photometric and color distribution but also suffers from hallucination effects due the lack of scene prior knowledge. In contrast, our method generates domain- consitett images and reduces hallucination noise by incorporating scene priors.
evident that
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Enhancing Visual Localization with Image Generation
HE
Ground Truth
Random Embedding
O urs
Figure Ablation study on anchor- hased image generation, we observe that using random embeddings to generate images results in photometric inconsistencies with the current pose, leading to global photometric artifacts such as hright and dark spots. In contrast, our method accurately associates the generated images with precise photometric embeddings, ensuring globally photometrically consistent images. The experimental results validate the effectiveness of the Anchor- Based Image Generation method.
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Enhancing Visual Localization with Image Generation
TB
Ground Truth
Pretrained Model
Finetumed Model
Figure 9. Ablation study the fine-tuning strategy, we observe that the pre-trained model not only generates images that inconsistent with the real domain but also suffers from hallucination effects due to the lack of scene information. In contrast, our fine-tuned model generates domain-consistee images and reduces hallucination noise by incorporating scene prior knowledge.
16


Reviews:
[{"review_id": "PWPblzaBbc", "reviewer": "Reviewer_zXjH", "summary": "This paper proposes a novel cross-domain data generation framework to enhance visual localization in scenarios with significant appearance variations (e.g., lighting conditions, camera types). The key contributions include a cross-domain 3D Gaussian Splatting framework, a text-guided image editing model, an anchor-based dataset generation method, and a positional attention mechanism. Experiments on the 360Loc benchmark demonstrate state-of-the-art performance.\n\n## update after rebuttal\nAfter carefully reading the reviews from other reviewers and the authors' rebuttal, I have decided to maintain my original rating (Weak Accept).", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**1. What is the APR method used in the proposed approach—PN, MS-T, or other methods?**\n\nSorry for the unclear details.  Our proposed approach employs the MS-T method for absolute pose regression (APR).\n\n**2. The ablations are mainly evaluated based on rendering quality. It would be beneficial to compare localization performance to demonstrate the relationship between rendering quality and localization performance.**\n\nWe sincerely appreciate your insightful suggestion. To evaluate the impact of rendering quality,  we conduct a quantitative experiment on the Atrium scene. Specifically, we compare the original Scaffold-GS (which produces lower-quality renderings compared to our cross-domain 3DGS) combined with our proposed data generation and positional attention mechanism.\n| Method                  | Atrium_Day | Atrium_Night | Average     |\n|-------------------------|------------|--------------|-------------|\n| Scaffold-GS             | 3.5/18.2   | 8.4/37.5     | 5.95/27.85  |\n| **Cross-domain 3DGS（Ours）** | **2.6/14.5**   | **2.2/12.8**     | **2.4/13.65**   |\n\n As shown in the table, localization performance drops when using the lower-quality images generated by Scaffold-GS. This result demonstrates a positive correlation between rendering quality and localization accuracy.", "claims_and_evidence": "The claims are generally well-supported.", "methods_and_evaluation_criteria": "1. The integration of 3DGS with photometric embeddings and dynamic suppression is novel and appropriate for modeling cross-domain variations.\n\n2. The 360Loc dataset is suitable for evaluation.", "theoretical_claims": "N/A.", "experimental_designs_or_analyses": "1. What is the APR method used in the proposed approach—PN, MS-T, or other methods?\n\n2. The ablations are mainly evaluated based on rendering quality. It would be beneficial to compare localization performance to demonstrate the relationship between rendering quality and localization performance.", "relation_to_broader_scientific_literature": "This work builds on 3D Gaussian Splatting, image editing and visual localization.", "essential_references_not_discussed": "None.", "other_strengths_and_weaknesses": "Strengths:\n1. Novel framework for cross-domain data generation.\n2. SOTA performance on the 360Loc benchmark.", "other_comments_or_suggestions": "None.", "questions_for_authors": "Please refer to \"Experimental Designs Or Analyses\".", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9kFcokiq4W", "reviewer": "Reviewer_nrtL", "summary": "This paper addresses cross-domain visual localization challenges by proposing a novel data generation framework based on 3D Gaussian Splatting (3DGS). The key contributions include: (1) a cross-domain 3DGS that models photometric variations and mitigates dynamic object interference, (2) a text-guided image editing model for addressing long-tail distribution problems, (3) an anchor-based method for high-quality dataset generation, and (4) a positional attention mechanism for cross-camera data ambiguities. Experiments show improvement across domains compared to baselines.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**1. Limited discussion of related work**\n\nThank you for your valuable suggestion. We have expanded the related work section in the revised manuscript by incorporating the recommended references to better highlight the novelty of our proposed method. The revised portion of the related work is shown below:\n\nRecent advances in neural rendering have enabled the 3D reconstruction from unconstrained photo collections. Neural Rerendering in the Wild [1] combines traditional 3D reconstruction with neural networks to handle unconstrained scenes. Extensions to the NeRF [2,3] further address challenges in uncontrolled scenes by embedding appearance information and transient uncertainty. In addition, methods such as Ha-NeRF [4] and Neural Scene Chronology [5] focus on capturing temporal variations by employing modules for appearance hallucination and temporal step function encoding. However, their slow training and rendering make large-scale scene modeling and data generation time-consuming. Additionally, the limited parameters of NeRF hinder its ability to effectively represent large outdoor scenes. Recently, 3DGS-based methods have garnered attention due to their faster optimization and rendering efficiency compared to NeRF. Among them, SpotLessSplats [6], GS-W, WE-GS, and WildGaussians have shown potential in modeling appearance variations and dynamic objects in wild scenes. However, they cannot explicitly control photometric properties and are constrained by the performance bottlenecks of pre-trained detectors. Existing methods are also not suitable for scenes with long-tail distribution problems. In this paper, we propose a method that models appearance variations by explicitly encoding photometric histograms, mitigates the impact of dynamic objects without relying on pre-trained detectors, and employs a fine-tuned image editing model to effectively address the long-tail distribution problem.\n\nSeveral studies have explored cross-domain visual localization under pinhole camera settings. To enhance localization performance, [7] uses invertible generators to produce synthetic images, while [8] converts nighttime images to a more discriminative daytime representation. Other methods focus on learning domain-invariant features to bridge the gap between varying environmental conditions [9]. Additionally, several works [10,11] advocate for disentangling image representations into separate codes that isolate place-specific cues from appearance and occlusion factors, ensuring reliable place recognition. However, these methods are limited to single-camera localization and rely on image retrieval-based localization. Compared to APR methods, such approaches suffer from significantly higher computational costs and storage requirements due to the need to construct and maintain a retrieval database. This paper enhances APR-based cross-domain localization, including cross-camera scenarios, by proposing a novel cross-domain image generation method. Unlike the prior method with limited image augmentation, our method facilitates diverse cross-domain image generation.\n\n[1]Neural Rerendering in the Wild [CVPR'19] \n\n[2]NeRF in the Wild [CVPR'21] \n\n[3]Ha-NeRF [CVPR'22] \n\n[4]Neural Scene Chronology [CVPR'23] \n\n[5]NeRF On-the-go [CVPR'24] \n\n[6]SpotLessSplats [TOG'25] \n\n[7]Adversarial training for adverse conditions: Robust metric localisation using appearance transfer [ICRA'18] \n\n[8]Night-to-Day Image Translation for Retrieval-based Localization [ICRA'19] \n\n[9]Retrieval-based localization based on domain-invariant feature learning under changing environments [IROS'19] \n\n[10]Adversarial feature disentanglement for place recognition across changing appearance [ICRA'20] \n\n[11]Place Recognition under Occlusion and Changing Appearance via Disentangled Representations [ICRA'23] \n\n**2. Figure 5 layout should be improved with a horizontal arrangement: GT, 360, fisheye3, fisheye2, fisheye1, pinhole.**\n\nThank you for your valuable suggestion. We have revised the layout of Figure 5 to present the images in a horizontal arrangement as suggested. The manuscript has been updated accordingly.", "claims_and_evidence": "This paper clearly identify limitations of single-domain methods and provide a convincing rationale for the method. Experimental results on the 360Loc dataset with different domains demonstrate improvements.", "methods_and_evaluation_criteria": "Using 3DGS for high-fidelity image synthesis and introducing learnable photometric embeddings are effective to handling variations. The evaluation on the 360Loc dataset with different camera types and lighting conditions provides a realistic and diverse experiment.", "theoretical_claims": "The theoretical aspects are sound.", "experimental_designs_or_analyses": "The use of a dataset that includes multiple camera domains and varying lighting conditions and construction of a long-tailed distribution dataset effectively simulates real-world scenarios. The reported  improvement demonstrates the effectiveness.", "relation_to_broader_scientific_literature": "Building upon advances in neural rerendering for unconstrained photo collections, this paper makes a contribution by leveraging these techniques to enhance visual localization through cross-domain image generation.", "essential_references_not_discussed": "The paper would benefit from discussing several relevant works in the areas of neural rerendering for unconstrained photo collections and cross-domain visual localization:\n\n1. Neural Rerendering for Unconstrained Photo Collections:\n   - Neural Rerendering in the Wild [CVPR'19]\n   - NeRF in the Wild [CVPR'21]\n   - Ha-NeRF [CVPR'22]\n   - Neural Scene Chronology [CVPR'23]\n   - NeRF On-the-go [CVPR'24]\n   - SpotLessSplats [TOG'25]\n\n2. Cross-Domain Visual Localization:\n   - Adversarial training for adverse conditions: Robust metric localisation using appearance transfer [ICRA'18]\n   - Night-to-Day Image Translation for Retrieval-based Localization [ICRA'19]\n   - Retrieval-based localization based on domain-invariant feature learning under changing environments [IROS'19]\n   - Adversarial feature disentanglement for place recognition across changing appearance [ICRA'20]\n   - Place Recognition under Occlusion and Changing Appearance via Disentangled Representations [ICRA'23]\n\nIncluding these references would strengthen the paper by providing a more comprehensive context for the proposed methods and highlighting the novelty of the approach in relation to existing work.", "other_strengths_and_weaknesses": "**Strengths:**\n- Addresses a relevant real-world problem\n- Innovative cross-domain data generation framework\n- Creative use of text-guided image editing for data augmentation\n- Effective positional attention mechanism for cross-camera data\n\n**Weaknesses:**\n- Limited discussion of related work", "other_comments_or_suggestions": "Figure 5 layout should be improved with a horizontal arrangement: GT, 360, fisheye3, fisheye2, fisheye1, pinhole.", "questions_for_authors": "No", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "evsuWzoQWL", "reviewer": "Reviewer_yDcV", "summary": "The paper focuses on improving visual localization accuracy with cross-domain image generation by three contributions. First,  a crossdomain is developed 3DGS to to generate realdomain consistent images. Second, a text-guided image editing model is presented to enhance data diversity for addressing the long-tail distribution problem. Third, an anchor-based method is developed to generate highquality datasets for visual localization. Extensive experiments demonstrate that the method improve visual localization performance on Loc360 dataset.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**1. Limitation results on 360Loc.** \n\nThank you for your valuable suggestion. Our work targets the challenging task of cross-domain visual localization, which requires datasets containing query images captured by **various types of cameras**. To the best of our knowledge, 360Loc is the only existing benchmark with cross-camera query images. Therefore, we have exclusively selected 360Loc as our evaluation benchmark.\n\n**2. Why not use the cross-domain image generation for SCR methods? Is the proposed method can enhance localization accuracy of SCR methods?**\n\nThank you for your valuable question. We would like to clarify the following points: 1) The baseline methods we evaluated follow the 360Loc benchmark, which does not include SCR methods for comparison. 2) In theory, our method could enhance the accuracy of SCR by using cross-domain 3DGS to render paired RGB and depth images. 3) Adapting SCR to cross-domain tasks is more complex, as SCR relies on **camera intrinsics and distortion parameters**, such as scene coordinate calculations, reprojection, and pose solving via PnP. However, existing SCR methods are designed for **pinhole cameras**. Adapting SCR to 360° and fisheye cameras requires deriving tailored methods. These enhancements to SCR are beyond the scope of our work, but it is a promising direction for future research.\n\n**3. More quantitative results to validate the effectiveness of the fine-tuning strategy.**\n\n Thank you for your valuable suggestion. To validate the effectiveness of our fine-tuning strategy, we conducted quantitative experiments on the atrium scene. We employed image similarity metrics SSIM and LPIPS to evaluate the similarity between the images generated and the ground truth.\n| | SSIM ↑ | LPIPS ↓ |\n|------------|--------|---------|\n| Pretrained | 0.338  | 0.428   |\n| Finetuned  | **0.709**  | **0.337**   |\n\nAs shown in the table, the image similarity significantly improved after fine-tuning, which demonstrates the effectiveness of the proposed fine-tuning strategy.\n\n**4. The proposed Cross-Domain 3DGS is engineered.**\n\nOur method builds upon Scaffold-GS but introduces key innovations to address challenges like appearance variations, dynamic object interference, and long-tail data distributions. We propose a photometric modeling scheme combining histogram-based embeddings and compensation to handle appearance changes, incorporate photometric priors for dynamic object uncertainty, and fine-tune a text-guided image editing model to augment sparse datasets in long-tail distributions. These improvements form the core innovation of our Cross-Domain 3DGS.\n\n**5. The proposed text-guided image editing model seems an application of InstructPix2Pix. The original contribution of the paper is not clear.**\n\nTo address the long-tail distribution issue in the dataset, we use InstructPix2Pix to transform daytime images into sparse nighttime images for data augmentation. However, directly using the pre-trained model leads to domain inconsistencies and hallucinations, as shown in Figure 7. To overcome this, we propose a fine-tuning strategy. We use 3DGS to render daytime images corresponding to sparse nighttime images, creating a domain shift dataset that enables InstructPix2Pix to learn domain-consistent transformations. Additionally, to mitigate hallucinations due to the sparse domain shift dataset, we generate a scene prior dataset by rendering daytime images that cover the entire scene, enriching the model with more scene priors.\n\n**6. It is suggested to rewrite the Introduction to clearly express the challenge and contributions, provide an overview of the entire method, and modify Fig. 2.** \n1. Thank you for your constructive suggestions. We have revised the Introduction to better highlight the core challenges of cross-domain visual localization and our contributions. We begin by emphasizing the challenges in cross-domain localization, followed by the motivation for introducing 3DGS to address these issues. We then discuss the technical challenges in designing cross-domain 3DGS and the solutions we developed. Finally, we discuss the issues encountered when training visual localization methods with data generated based on 3DGS and how our method is designed to overcome these training issues.\n2. We have rewritten a more structured overview of our method and modified Figure 2 to more clearly express the design of cross-domain 3DGS and the training of the visual localization method. We have also updated the paper to include the Impact Statement.\n\n**7. In Tables 1,2, which method does the paper use, and how many generated images are used?** \n1. Sorry for the unclear details. The method used in the paper is MS-T. \n2. We provide the detailed counts used for each scene (including self-rotated images and ground truth).\n| Atrium | Concourse | Hall  | Piatrium |\n|--------|-----------|-------|----------|\n| 17,430  | 14,730     | 16,200 | 18,960   |\n\nWe have updated the paper to include these details.", "claims_and_evidence": "Insufficiently. The authors claim that the proposed text-guided image editing model enhances data diversity for addressing the long-tail distribution problem. In my opinion, the validation is not sufficient. To validate the effectiveness of fine-tuning strategy, the paper provides some visualization results, such as Fig. 7 and Fig. 2 in the Supplementary Material. However, the quantitative results are missing. How the strategy improve actual localization accuracy is not discussed.", "methods_and_evaluation_criteria": "Yes, the proposed methods can be used for enhancing visual localization performance.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "The experiment is somewhat insufficient at the following aspects. \n1. Limitation results on 360 image. In Tables 1,2, the localization improvements on 360Loc are presented. It is suggested to add the results of the common localization dataset. \n2. Limitation results on APR methods. The experiments results demonstrate the improvement of APR methods. Additionally, SCR methods always show more accurate performance than APR methods. Is the proposed method can enhance localization accuracy of SCR methods? \n3. More quantitative results of ablation studies are preferred. To validate the effectiveness of fine-tuning strategy, the paper presents visualization results. However, the quantitative result is missing.", "relation_to_broader_scientific_literature": "The main contribution lie in the usage of cross-Domain image generation with 3D GS and image editing model for enhancing visual localization.", "essential_references_not_discussed": "Essential References are discussed.", "other_strengths_and_weaknesses": "Strength.\n\n1.A cross-domain 3D GS to generate real-domain consistent images.\n\n2.A text-guided image editing model to enhance data diversity.\n\n3.An anchor-based method to generate high-quality datasets for visual localization. \n\nWeakness.\n\n1.The contributions seem incremental. \n\n(1)The proposed Cross-Domain 3D Gaussian Splatting is based on Scaffold-GS with dynamic object suppression and training strategy, which is engineered.\n\n(2)The proposed text-guided image editing model seems a application of structPix2Pix. The original contribution of the paper is not clear. \n\n2.The writing needs further improvements. \n\n(1)The of Introduction is somewhat confused, which is hard to read. It is suggested to rewrite it to express the challenge and contributions more clearly.\n\n(2)In Section Method, it is suggested to present the overview of the whole method, especially where the paper contributions. Meanwhile, Fig.2 is also suggested to be modified.\n\n3.The motivation for APR method. The pipeline of localization enhancement is designed for APR method. However, as far as current works cover, APR methods are always less accurate than SCR methods. Why not use the cross-domain image generation for SCR methods.\n\n4.The experiments are somewhat insufficient. Please see the section “Experimental Designs Or Analyses” for details.", "other_comments_or_suggestions": "The comments and suggestions are listed in the Strengths And Weaknesses part.", "questions_for_authors": "In Tables 1,2, the paper presents the localization accuracy with cross-domain image generation. There are two questions. Which method does the paper use and How many generated images are used? Please present more details.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "szlOnXgL9t", "reviewer": "Reviewer_MirD", "summary": "Summary\nThis paper proposes a novel cross-domain data generation framework to enhance visual localization in scenarios with significant domain variations. The main results look solid and impressive. The contributions include 1) A modified 3D Gaussian Splatting framework that models photometric variations via learnable embeddings, suppresses dynamic objects using confidence maps, and employs a two-stage training strategy to mitigate hallucination noise. 2) A text-guided image editing model (fine-tuned with scene priors) to augment sparse secondary domains (e.g., nighttime data). 3) A method to synthesize pose-consistent training data and positional attention to resolve cross-camera feature misalignment.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**1 Limited discussion of failure cases.** \n\nThank you for your valuable suggestions. \nWe observe two main failure cases in our current method. First, in scenes like Piatrium_Night that contain extremely dark regions, the fine-tuned image editing model sometimes fails to produce realistic textures. This highlights the limitation of fine-tuned InstructPix2Pix in handling low-light appearances. Second, when significant static geometry changes are present in the query images (such as structural modifications), our cross-domain localization performance may degrade. In the future, this issue could be addressed by incorporating static geometry editing into the cross-domain image generation process, thereby enhancing data diversity and improving robustness in such scenarios. We have updated the manuscript to include the discussion of failure cases.\n\n**2 No analysis of computational costs.** \n\nWe apologize for the missing details. All experiments were conducted on a single NVIDIA RTX 4090 GPU. Fine-tuning the image editing model for each scene takes approximately 10 hours on average. Cross-domain 3DGS training and image generation require at most 4 hours per scene. Visual localization training takes roughly 16 hours per scene. During inference, the localization method MS-T with the proposed positional attention mechanism runs at 32.51 ms per frame. We have updated the manuscript to include the computational costs of the experiments.\n\n**3 Is there any possibility to include more comparison methods?**\n1. For the cross-domain visual localization task, our work primarily focuses on Absolute Pose Regression (APR) methods, and the baselines we compare against are the state-of-the-art APR methods provided by the 360Loc benchmark. To the best of our knowledge, there are currently no other advanced methods targeting this task.\n\n2. For the image generation task, we have included comparative experiments with the advanced GS-W [1], which is designed for unconstrained scenes. Since GS-W does not address the long-tail data distribution issue, we conduct quantitative experiments on the Atrium_Day scene, which contains sufficient data. Additionally, we extend its training iterations to **140,000** for the large-scale evaluation scene.\n\n|                         | PSNR ↑ | SSIM ↑  | LPIPS ↓ |\n|-------------------------|--------|---------|---------|\n| GS-W                    | 18.85  | 0.635   | 0.594   |\n| **Cross-domain 3DGS（Ours）** | **24.64**  | **0.758**   | **0.231**   |\n\nAs shown in the table, our method significantly outperforms GS-W while using fewer iterations (**60,000**). This demonstrates that our method has stronger scene modeling capabilities for large-scale scenes.\n\n[1] Gaussian in the Wild: 3D Gaussian Splatting for  Unconstrained Image Collections [ECCV 2024]", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "No Theoretical Claims.", "experimental_designs_or_analyses": "The experimental designs are sound, including comparisons on dataset 360Loc (Atrium Day, Concourse Day, Hall Day, Piatrium Day, Atrium Night, Concourse Night, Hall Night, Piatrium Night). \nThe ablation is also conducted on Effectiveness of Cross-Domain 3DGS, Effectiveness of Fine-Tuning Strategy, and Effectiveness of Positional Attention.", "relation_to_broader_scientific_literature": "1) 3DGS: Extends Scaffold-GS with photometric modeling by addressing limitations in dynamic scenes.\n2) ​NeRF-W: Adopts uncertainty-aware rendering for dynamic object suppression.\n​3) InstructPix2Pix: Leverages diffusion models for domain transfer and adds scene-specific fine-tuning.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths: 1) The practical focus on real-world challenges including cross-camera and long-tail data. \n\nWeakness: 1) Limited discussion of failure cases. \n                   2) No analysis of computational costs.\n                   3) Is there any possibility to include more comparison methods?", "other_comments_or_suggestions": "None.", "questions_for_authors": "None.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The ratings for this paper are one accept (Reviewer nrtL) and three weak accepts. Both Reviewer zXjH and Reviewer nrtL agree that the proposed method is novel and appreciate the good performance and the proposed novel cross-domain data generation framework. Both Reviewer nrtL and Reviewer MirD acknowledge that the paper is addressing a real-world problem. The major concerns of Reviewer yDcV are about its insufficient experiments and incremental contributions. The rebuttal satisfactorily addressed most of these concerns, and Reviewer yDcV raised the rating to weak accept. Therefore, the ACs recommend acceptance.
Decision: Accept (poster)