L2RSI: Cross-view LiDAR-based Place Recognition for Large-scale Urban Scenes via Remote Sensing Imagery
Authors: Ziwei Shi
Xiaoran Zhang
Wenjing Xu
Yan Xia
Yu Zang
Siqi Shen
Cheng Wang
Venue: NeurIPS 2025 poster ()
Keywords: Cross-view LiDAR-based Place Recognition
Point cloud
Remote sensing imagery
Primary area: applications
TL;DR: This work is the first to address the challenge of large-scale (over 100km^2) urban cross-view LiDAR-based place recognition with high-resolution remote sensing imagery.

Abstract: We tackle the challenge of LiDAR-based place recognition, which traditionally depends on costly and time-consuming prior 3D maps. 
To overcome this, we first construct LiRSI-XA dataset, which encompasses approximately $110,000$ remote sensing submaps and $13,000$ LiDAR point cloud submaps captured in urban scenes, and propose a novel method, L2RSI, for cross-view LiDAR place recognition using high-resolution Remote Sensing Imagery. This approach enables large-scale localization capabilities at a reduced cost by leveraging readily available overhead images as map proxies. L2RSI addresses the dual challenges of cross-view and cross-modal place recognition by learning feature alignment between point cloud submaps and remote sensing submaps in the semantic domain. Additionally, we introduce a novel probability propagation method based on particle estimation to refine position predictions, effectively leveraging temporal and spatial information. This approach enables large-scale retrieval and cross-scene generalization without fine-tuning. Extensive experiments on LiRSI-XA demonstrate that, within a $100km^2$ retrieval range, L2RSI accurately localizes $83.27\%$ of point cloud submaps within a $30m$ radius for top-$1$ retrieved location. Our project page is publicly available at https://shizw695.github.io/L2RSI/.

L2RSI: Cross-view LiDAR-based Place Recognition for Large-scale Urban Scenes via Remote Sensing Imagery
Ziwei Shi
Xiaoran Zhang 1.2 Wenjing Xul.2 Yan Xia
Yu Zang Siqi Shen Cheng Wang
Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China
Key Laboratory of Multimedia Trusted Perception and Efficient Computing.
Ministry of Education of China, Xiamen University, China
3 University of Science and Technology of China, China
shizw 1995 @ edu..c zangyu7 @ xmu.edu.cn
point sobmap
Remote seesing inagge
Bus retriesal
Hetrinal
Semandic qurr
20000
60:00
8600
16tel
8500
103kee
Nuster oftop eet levals
Figure (Left) We propose L2RSI for cross-view LiDAR-based place recognition without 3D pre-built map. Given a point cloud submap representing the surroundings of the robot and the absolute orientation provided by a LiDAR and a magnetometer, 1.2RSI provides the most probable location in large-scale city using high-resolution remote sensing imagery. (Right) Place recognition performance in different retrieval ranges. Notably, 1.2SSI achieved recall (<30m) of over 80% at Top-1 retrieval within range of 100km²?
Abstract
We tackle the challenge of LiDAR- based place recognition, which traditionally depends on costly and consuminn prior 3D maps. To overcome this, we first construct LiRSI-XA dataset, which encompasses approximately 110, 000 remote sensing submaps and 13, 000 LiDAR point cloud submaps captured urban scenes, and propose novel method, L2RSI, for cross-view LiDAR place recognition using high-resolution Remote Sensing Imagery. This approach en- ables large-scale localization capabilities available overhead images as map proxies. L2RSI addresses the dual challenges of cross-view and cross-modal place recognition by learning feature alignment between point cloud submaps and remote sensing submaps in the semantic domain. Additionally, we introduce novel probability propagation method based on parti- cle estimation to refine position predictions, effectively leveraging temporal and spatial information. This approach enables large-scale retrieval and coss-scenee generalization without fine-tuning. Extensive experiments on LiRSI-XA demon- strate that, within 100km retrieval range, L.RSII accurately localizes 83.27% of point cloud submaps within a 30m radius for top- retrieved location. Our project page is publicly available https://shizww95. http://ww.....iu..../.........
reduced cost by leveraging readily
*Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Place recognition aims retrieve the closest match and its location from a pre-built database within the global coordinate system when GPS is weak or even denied, serving as an essential task for autonomous driving and robotic navigation. LiDAR-based place recognition is now increasingly attractive since 3D point clouds from LiDAR are invariant to lighting and shadows [1]. Existing LiDAR- LIDAR-based place recognition methods face significant limitation: they depend on up-to-date prior 3D maps, the acquisition and maintenance of which are time-consuming and costly. Recent research has sought to address this issue by exploring alternative approaches. Tang al. [2. 3] unified overhead imagery and LiDAR into a pseudo point cloud modality, achieving retrieval-baa
Table I: List of conditions and tasks.
Conditions
GPS
place recognition. However, their approach only works on the assumption of known route, which confines the retrieval process to a very limited range. Cho et al. [4] conducted LiDAR- place recognition using OpenStreetMap (OSM).
IMU
Query
LiDAR+MAG
Reference
RSI
Due to the dual challenges of cross- view and cross tross-modality, existing methods typically restrict the retrieval area small range. The most commonly used 3D datasets include the Oxford Radar RobotCar [5],
Range
100km
Database
>60000
Distance
<30m
Recoltx1
>80%
KITTI [6], and KITTI-360 [7]. The former consists of multiple repeated trajectories, covering an area of less than 2km? while the latter two datasets are composed of several independent trajectories, with the largest covering range approximately 1kmm².
As shown in Table 1, our task is to utilize high-resolution remote sensing imagery (RSI) as the database (due the advantages of global coverage, cost-effectiveness, and timeliness). to achieve place recognition (LPR) in the range of over 100km² 2 urban scenes, more than 60000 RSI and 30m required distance threshold, based on the only sensors of LiDAR and magnetometer (MAG).
In fact, using high-resolution remote sensing as the reference map will lead to a large gap between the query and databases in terms of domain and view, which will cause two major problems: (1) Most of the current retrieval solutions can hardly learn the correspondence between the query point cloud and the remote sensing image retrieval database directly. (2) Single query can be ambiguous and unstable, which limit the practicality of the system, not to mention the retrieving range of over 100km².
To address the first problem, we observe that the content and style between LiDAR point clouds and remote sensing imagery, even from the same location, can differ significantly. However, they exhibit strong correlations in the semantic domain, illustrated in the bottom-left comner of Fig. 1. Inspired by this observation, we propose a framework, named L2RSI, which includes a semantic contrastive learning network align the global descriptors of LIDAR point cloud submaps and remote sensing submaps in the semantic space. The point cloud submaps are obtained through the registration of short-range LiDAR scans, overcoming the sparsity of single-frame point clouds. With the direction provided by an additional magnetometer (MAG), these submaps are transformed into a bird's s-eee view (BEV) representation through rotation and projection, enabling the network to effectively exploit the shared semantic features visible in both modalities.
Specific the second problem, we propose the Spatial-Tempont Particle Estimation (STPE) to aggregate
information of continuous queries, thus to eliminate the ambiguity between geometrically distant but semantically similar positions. Specifically, the retrieval results in each query are considered as particles. Instead of filtering out the incorrect particles, our idea is to aggregate these particles and estimate a more accurate probability density distribution for the current position. Distribution of the particles of a single query can be modeled as the mixture of multiple gaussian models (GMMs). Then, the aggregation spread over desired time window through an estimated inter-frame correspondence of the point clouds, estimating the probability density of the current query and refining the retrieval results.
Our contributions are summarized as follows:
To the best of our knowledge, the proposed framework is the first to address the challenge of large-scale (over 100km²) urban cross-view LiDAR-based place recognition with high- resolution remote sensing imagery, demonstrating promising performance with significant practical applicability.

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We propose a particle estimation algorithm that utilizes the mixture of multiple gaussian models to aggregate aopatio-tempora information and infer the probability density of the current position. significantly improving the retrieval performance.
The proposed method exhibits remarkable generalization ability, achieves Recal/@1 (< 30m) of 83.27% within a retrieval range of 100km², and demonstrates promising potential on cross scene generalization without fine-tuning,
2 Related work
Uni-modal 3D place recognition. The early work by Uy et al. proposed PointNetVLAD [8]. which utilized PointNet [9] as a point cloud feature extractor, followed by NetVLAD layer [10] for global feature aggregation. LPD-Net [11] additionally introduced local information of points and aggregated global descriptors through GNN. SOE-Net [I] incorporated orientation encoding into the feature embedding process to capture spatial context and used a self-attention mechanism to aggregate global information. MinkLoc3D [12] applied sparse convolution and Generalized- Mean pooling [13] to extract discriminative features from sparse voxel representations of point clouds, and its extension, MinkLoc++ [14], further incorporated monocular camera images. SVT--Net [15] extended the transformer network to sparse voxels for capturing long-range contextual features. Zhou et al. [16] encoded geometric information by representing point clouds as normal distribution transformation cells. CASSPR [17] leveraged a dual-bramch hicrarchical cross-attention mechanism to integrate the advantages of point-based and voxel-based features. Crossloc3d [18] proposed ground LiDAR place recognition method using acrial LiDAR as the database. However, such methods require an available and reliable prior 3D map, which is demanding requirement in practice.
Cross-modal 3D place recognition. Cross-modal methods enhance usability by replacing costly prior 3D maps with data from alternative modalities. The work by Lee et al. [19] was the first to achieve LiDAR-based place recognition within a street-view image database. They created depth images from short sequence images using MantDepth [20] and established consistent data representation with LiDAR range images through contrastive learning. Lip-Loc [21] aligned LiDAR point clouds with RGB camera images through range image projection. C2L-PR [22] achieved modality alignment by 2D to 3D mapping via depth estimation and semantic segmentation, and mitigates the FOV discrepancy through orientation voting. VXP [23] incorporated similarity constraints on local descriptors and established a voxel-pixel shared feature space through a two-stage training process. Text2Pos [24] and Text2Loe [25] determined the most probable location using textual descriptions. UniLoc [26] designed a universal place recognition working with any single query modality. Some researchers focused publicly available maps, such as OpenStreetMap (OSM). Ruchti et al. [27] relied exclusively on road information extracted from LiDAR point clouds, associating it with the OSM road network compute weights for Monte Carlo localization. Cho et al. [4] designed hand-crafted rotation-invariant descriptor to retrieve the most similar location from the OSM database based on the relative position of LiDAR and buildings. The works most relevant to our research are a series of studies based on overhead images. Tang et al. [2] utilized ray-tracing to generate escudo-point clouds from LiDAR scans and overhead images that are more similar each other. In subsequent work [3]. they introduced CycleGAN [28] to generate synthetic LiDAR images from satellite images and selected aseudo-pair, from sequential data to learn a shared embedding space. The place recognition range of the aforementioned methods is limited to known or predefined routes within urban scenes. In contrast, our L2RSI can be easily extended unknown areas spanning tens to hundreds of square kilometers, offering broader application potential.
Garg al. [29] and Malone et al. [30] introduced tempo- ral information by training a sequence-matching network to enhance place recognition performance. However, remote sensing imagery is inherently static and lacks temporal continuity or directional cues, and such methods are not applicable. Prticle filtering [27, 31] is widely used in localization and tracking tasks, where it recursively refines the particles based on the motion model of the system, gradually converging them towards the position of the robot. The main difference from the proposed STPE is that our approach is particle aggregation rather than filtering. This is due the inherent content discrepancies between remote sensing imagery and LiDAR data, where the observation information from a single query is often highly unreliable. Such toxic queries can lead to the filtering out of most, if not all, trustworthy particles. In contrast, the proposed method ensures the smooth transmission of spatio-temporal information and is more suitable for extremely challenging place recognition.
Cequence-Constrain
Place Recognition.
3

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Preprocesing
Cintrastive
Global Place Recognition
Eunake
leage af
STPE
Xeear rookig segoy
Palriceed sabeuy Semurti DEV
Salewack
Vegnales
TeCCutupe
Protekliikk propieh
Flaal Togel raboay
Figure 2: Overview of the proposed L2RSI. It consists of three modules: the data preprocessing module (left), the training framework for Global Descriptor Extraction network (middle) and the inference framework (right).
Cross-view 2D place recognition. Since our method unifies cross-modal data into semantic images, some image- based place recognition methods can also be applied. Early works such as CVUSA [32], CVACT [33], and VIGOR [34] established the main benchmarks image place recognition between street view and satellite view. GeoDTR [35], used CNNs to extract global features and enforced the introduction of a Transformer to extract additional information through counterfactual loss. Sample4Geo [36] introduced the off-the-shelf ConvNeXt-B
feature extractor and designed dynamic similarity sampling strategy to construct training batches. Recent GRGeo [37] proposes a feature reorganization module to effectively enhance feature aggregation for BEV images. In this work, we find that directly applying these methods can not work well in our task due to the domain gaps.
Problem statement
We begin by defining the large-scale remote sensing imagery Mres= (m; ) to be a collection of square submaps m,. Each submap is sampled at equal intervals with partial overlap and includes the UTM coordinates of the center point. Let Q be a query point cloud submap obtained by aggregating a short sequence LiDAR scans. Our task is defined as determining the coarse position of the center point in the query point cloud submap, which is a cross-view, cross-modal retrieval problem:
m
= orgmax (im(Fa(Q,0)) (me)( m.CMref
(1)
where (-).) and (-) denote the deep learning networks used to encode the point cloud submap Q and remote sensing submaps m; into a shared feature space. Sim((:)) represents a measure of similarity, egg., the dot product. represents the orientation of the intermediate frame in Q. We obtain a noisy measurement from magnetometer.
Methodology
Fig. 2 shows the overview of the proposed I.2RST. In the Data preprocessing module (Sec. 4.1), on one hand, we perform semantic segmentation on remote sensing imagery, which are then evenly divided into submaps to serve as the semantic database. On the other hand, we conduct semantic segmentation on point cloud submaps, which are subsequently compressed into BEV images. During training, we learn representations of BEV images and remote sensing submaps within a shared semantic space to overcome the modality gap (Sec. 4.2). During inference, we extract the global descriptor of the given query point cloud submap and retrieve set of potential candidates from the semantic database. Then we apply STPE to estimate the probability density of the current query position and refine these candidates (Sec. 4.3).
4.1 Data preprocessing
Remote sensing imagery. Satellite and LiDAR data exhibit discrepancies in content and style due to differences in viewpoints, imaging principles, and acquisition times. In practice, humans typically assess whether the point cloud and remote sensing imagery represent the same location by identifying high-level semantic information. Inspired by this, we employ AIE-SEG developed by Alibaba', which is well-established universal segmentation model for remote sensing interpretation, to perform semantic segmentation on remote sensing imagery. Intuitively, we select roads, sidewalks,

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vegetation, and buildings as the semantics of interest. Subsequently, a square window with side length slides uniformly across the semantic image, extracting submaps to form the semantic database, as shown in the top-left comer of Fig. 2. Notably, considering that robots travel along the road, we filter out submaps that are located far from the road label.
Point cloud. For the acquired short sequences of LiDAR scans, we build the point cloud submap using FastGICP [38]. Then, we introduce SphereFormer [39] perform segmentation based on the same semantics as remote sensing imagery. To adapt to data collected by different LiDAR systems, we remove the intensity feature and retrain the model on SemanticKITTI [40]. While various unsupervised domain adaptation methods can potentially yield better semantic segmentation results, our approach is simple yet effective.
To obtain the same perspective as the remote sensing imagery, we then compress the point cloud into bird's-eye s-eye view (BEV) image, covering area of approximately d d. The topmost points retained to preserve the features that are commonly visible in the remote sensing imagery. Based on the orientation measured by the magnetometer, we rotate the BEV image align the north direction upwards before feeding it into the global descriptor extraction network.
4.2 Semantic contrastive learning
Recently, several works focused on building cross-modal representations have emerged prominently across various zero-shot computer vision tasks, such as classification and retrieval [45, 46]. Inspired by their work, we design network that aligns cross-modal semantic features in unified feature space through contrastive learning.
[41], recognition [42, 43, 44]
Network architecture. As shown the middle of Fig. 2, we employ dual-branch network to extract global descriptor for pairs of semantic images { from point clouds and { from remote sensing imagery, where N is the batch size. The distance between the centers of the paired images constrained be less than d/2. Each branch consists of semantic encoder, a generalized mean (GeM) pooling, and a fully connected (FCN) layer in a sequential configuration. The semantic encoder supports initialization with various pre-trained foundation models. We use MAE [47] here. Then, GeM pooling and the FCN layer generate global descriptors. Since both branches operate within the semantic domain, we fully share their weights. this work, we set d = 60m.
J.N
Loss function. During training, we ensure that image pairs within batch serve as negatives for each other and use the symmetric InfoNCE loss as the contrastive learning objective [36], formulated as follows:
L -log explf? (FA) EjENeoplE" 1511) EjeNerp'st 1,91)
explt -1-M)
log(
(2)
where 1o and J.P represent the global descriptors of Q; and P., respectively. The temperature coefficient 7 is set to 0.1. Compared to the traditional triplet loss [8, 2, 3], the advantage is that increasing the number of negatives in a training batch greatly accelerates the training process.
4.3 Spatial-Temporal Particle Estimation
During inference, we first extract the global descriptor of the query point cloud submap Qe at time step f. We then calculate its similarity distance to the image submaps in the remote sensing database for determining the Top-C retrieval results. ( seee the top-right corner of Fig. 2). Note that the global descriptors are extracted offline to improve retrieval efficiency.
To alleviate the unreliability of individual query retrieval results, we consider the query sequence (Q,)ı441 from time step t-L+1 to t. The retrieval results of Q2 are treated as particles, where the state of each particle, denoted as [(( with & E 1, 2,.,, K, refers to the 2D coordinates of candidate submaps the remote sensing database. The state of the k-th particle of Q2 transitioned from time step t 1 to t through the following function:
Hunll Mi- (x/ Barll 14.31
where M,-1 is the motion model from time step / 1 to and v [+J] noise term. Unlike traditional particle filtering, we propagate particles from multiple past time steps to current time step using the motion model, and then construct probability density function at time step / based on the combined particle distribution.
5

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In practice, we obtain the relative positions between LiDAR scans from FastGICP [38], and correct the direction in the world coordinate system every 20 meters using the magnetometer, which subsequently enables the determination time step tend to cluster around some locations with similar features, which can be formulated as multiple Gaussian distributions:
particle states at each time step. We observe that these particles each
Σ Am exp ( (I- Hum
(y (V-Mym)
P(a v)
(4)
20
203m
where M represents the number of Gaussian distributions and we use DBSCAN clustering top-K candidates (K C) to determine it with a radius of r 30m. Let a and y denote the coordinates of the particles. Since they are assumed to be independent, their covariance is O. Am indicates the contribution of the m-th Gaussian distribution to the overall distribution, which is computed as follows:
Nm Li= N
Am
(5)
where N₂ is the number of candidates in the i-th cluster. The candidates in m-th cluster are represented as ((x7", ), Nm The center of Gaussian distribution jaom and Mym is the geometric
center of m-th cluster:
Nm
Nm
(Nom: Mym)
(6)
N
i=1
i=1
The standard deviations am and ym estimated follows:
Nm (o
Nm L
Mamp Jym
N 6/ mymm
Therefore, we estimate the probability density function of the query Qi by propagating the retrieval result distribution of the query sequence (Qj) (+11 from time step f [++1 to l. Assuming the relative position between Q; and Q2 is (Aæj, Auj). The distribution of the retrieval results of Q; after propagation according to (4) is:
(1 8) Σ Am exp (- p (V- (U-Mim) 8
(8)
20
20
where 1mmm- - Mxm + Ary and 1 - Mym + Dyy This implies that the particle states at the current time step are derived from their estimated values at the previous step. rather than from the positions associated with the retrieved remote sensing images. The probability density function of the position at time step can be formulated Gaussian Mixture Model:
1 Σ Pz(,,))
Pa(a, = j=t-1+1
(9)
where the parameters of (z. (I.y) y) is updated as the vehicle moves. Then, we assign probability value to each remote sensing submaps in the database:
1 Pr v) S
Po(x, dy da.
(10)
where (u, is the center coordinate of the remote sensing submap. We take the average value within a rectangle with an area of 4- as the probability value for this remote sensing submap. To avoid cumulative errors, only consider the query sequence of 250m, and re-rank the retrieval results of
Qe based on probability values.
Experiment
5.1 Benchmark dataset
Due to the lack of publicly available datasets that provide correspondences between large-scale outdoor LiDAR scans and remote sensing imagery, we construct the LiRSI-XA and LiRSI-Oxford datasets for training and evaluation.
LiRSI-XA. We purchase high-resolution remote sensing imagery covering Xiang an District from AIRSAT Technology Group, with resolution of 0.5m. We extract submaps of 60m 60m from

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Table 2: Summary statisties for dataset.
LiRSI-XA
LiRSI-OXIORi
Submap Num.
Training Set
Test Set-S
Test Set-M
Test Set-L
Test Set-G
11-14-02-26
14-12-05-52
PC
12T94
862
862
862
862
1697
1671
RSI
47913
3094
6479
12119
63047
2103
2103
Area (km²)
65
16
100
2.8
2.8
the original remote sensing imagery at intervals of 20m, resulting in a two-third overlap between adjacent submaps. And the submaps lacking road semantics in their 30m x 30m central region are filtered out.
We utilize a Ouster OS1-64 LiDAR acquire point cloud data in the Xiang' an District of Xiamen, covering trajectory of approximately 100km. We record directional information through mag- netometer with an average error of less than 10 During point cloud preprocessing, we construct point cloud submap every 5m and compress it into a semantic BEV image with a side length of 60m. There three-year temporal gap between LiDAR data and remote sensing imagery.
The vehicle trajectory is shown in Fig. 3a. We select approximately 5km of the trajectory as the test set, with the remaining portion used for training. For the training set, we manually exclude data with inherent misalignment between ground and aerial semantic information to avoid confusion during training. This includes the areas where the sky is obstructed, such as the road segments beneath overpasses and dense street trees, as well as changes in buildings, vegetation, and roads due to the temporal gap. For the test set, we established databases of 4km² (S), 9km2 (M), 16km2 (L) and 100km² (G) to evaluate the retrieval performance of the proposed method, see Table 2. Notably, the point cloud at the boundary between the test and training sets are discarded, ensuring that the network is never exposed to the test set data during training.
LiRSI-Oxford. Oxford Radar RobotCar is famous urban scene dataset [5] for localization.
The data is collected with a Velodyne HDL-32E LiDAR. The dataset includes multiple repeated traversals, each cov- ering about 10km, as shown in Fig. 3b. Based on this, we supplement the database with the corresponding remote sensing imagery, covering an area of ap- proximately 2. 2.8km? And we simulate the magnetometer by adding random noise within +15 the ground truth of orientation, as the official dataset does not include a magnetometer. The source and resolution of remote sensing im- agery, as well as the data preprocess- ing process, are the same as those of the LIRSI-XA. The data 11-14-02-26, 14-12-05-52 used as the test set to evaluate the generalization ability of the proposed method without fine-tuning, see Table
(a) LiRSI-XA
(b) LiRSI-Oxford
Figure 3: Dataset configuration. LiRSI-XA a, different colored rectangles highlight test sets from databases of varying sizes, all sharing the same query trajectory (green). Besides, the training trajectory (light blue) and discarded trajectory the boundary (red) are annotated. Oxford b, the green trajectory is as the query for the test set, while the entire remote sensing imagery serves as the database.
LiRSI-
5.2 Evaluation criteria
Following pervious place recognition task [8, 19], we use retrieval recall evaluate LiDAR-based place recognition based on remote sensing imagery. Recall@N means the recall at top-N retrieval results, that is, the proportion of queries for which the correct result is retrieved within the top-N results. We define a correct result as when the Euclidean distance between the center of the retrieved remote sensing submap and the center of the query point cloud submap is less than 30m. We particularly emphasize on Recall@1, as it has the most direct practical relevance.
7

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Table Performance comparison on LiRSI-XA and LiRSI-Oxford Recal1@1 and Recall 10 (%)
are reported. The best result is rendered in bold.
Recallm1
Recalt310 (%)
Methods
Test Set-S
Test Set-M
Test Sct-L
Test Set-G
11-14-02-26
14-12-05-52
LIP-Loc [21]
16.82/53,48
13,69/45.36
11 11.60/1.65
11.02/39,44
0.88/10.78
0,96/11.13
GeoDTR [35]
8.68/25.27
6,25/19.36
4.74/14.95
4,39/9.26
0.47/3.18
0.48/3.71
Sample4Gico [36]
28.63/53.92
25.39/49.75
24.23/46.77
22.95/43.02
7.13/20.68
7.51/21775
FRGeo [37]
22.26/49.88
19.72/42.45
17.16/38.05
15.54/34.45
1.62/7.34
1.58/7.86
L2RST (w/o STPE)
30.05/68.56
23.90/61.95
21.93/7.442
20.07/52.55
10.19/30.23
11,25/30.58
L2RSI (w: SuperGlobal (48)
30.39/57.54
23,90/53.00 02
22.27/48.26
20.19/42.92
8, 8,9022.98
8,98/22.86
L2RSI (w. PF)
71.66/86.88 88
55.17/72.47
50.87/72.47
47.85/58.66 65
21.40/31.4
16. 16.83/29.46
L2RSI (w. STPE)
88.93/9..13
87.95/91.64
85.49/90.65
83.27/87.82
42.29/59.41
43.77/62.76
5.3 Results
5.3.1
Evaluation on test sets
The proposed L2RSI is the first to perform cross-view LiDAR-based place recognition in large- scale urban scenes with high-resolution remote sensing imagery. Several state-of-thee place recognition networks are capable of handling preprocessed cross-modal semantic images, such as cross-modal 3D place recognition (LIP-Loc [21]) and cross-view 2D place recognition (GeoDTR [35]. Sample4Geo [36]. and FRGeo [37]). Firstly, we benchmark them to evaluate the performance of our method in the test sets of LiRSI-XA with different size databases. The training settings on LiRSI-XA for all methods are consistent, except for the image size and descriptor dimensions, which follow the official configurations. Notably, we replace the LiDAR range image projection used in the original LIP-Loc with BEV projection. The comparison is summarized in Table 3. L2RSI (w. STPE) significantly outperforms the baseline methods, reaches a Recall @1 of 88.93% with a database of 4km² As the retrieval range expands, L2RSI (w. STPE) demonstrates increased robustness, with the database area increased to 100km² and only a 5.66% degradation in Recall@ 1 and a also introduced comparison of two refinement 4.31% degradation in Recall@ 10. In addition, strategies, including SuperGlobal reranking [48] and classical particle filtering (PF). The former focuses refining individual queries and database features, leading only marginal improvements in performance. For the latter, we apply its principles within the retrieval framework, initializing the number of particles to 120. With the integration of sequence information, the performance of L2RSI (w. PF) shows considerable improvement. However, in challenging cross-view and cross-modal retrieval tasks, particles frequently decay, necessitating re-initialization and impeding the flow of sequence information. The proposed STPE substitutes filtering with aggregation, efficiently utilizing spatio-temporal information to substantially enhance the unstable performance of single query.
Secondly, we directly test the model on the LiRSI- Oxford without any fine-tuning. Given the stark differences in equipment models, road widths, architectural styles, etc., this challenge marks the highest level of generalization ability. In the lower part of Table 3, the baseline methods fail completely, while L2RSI (STPE) achieves Recall@1 1 over 40% and Recall@10 above 60% on both trajectories. Although the current results are not yet sufficient for real-world deployment, they demonstrate great potential for future appli- cations. Please refer to Sec. A.3 for comparative experiments in more scenes.
Table 4: Ablation study about effectiveness of components. The results include Recall@1 and Recall@30 (%) on Test Set-S and 11-14-02-26. The best result is rendered in bold. Sem.: Se- mantic segmentation. STPE: Spatial-Temporal
Particle Estimation. Orient.: Orientation.
11 Recall (%)
Sem.
STPE
Orient.
Test Sct-S
11-14-02-26
50.43/67.90
8.13/2542
30.05/85.50
19/45/55.90
54.37/76.14
4.92/21.22
v
88.93/95.94
42.29/70.15
5.3.2 Ablation study
The following ablation studies evaluate the impact of different components and settings on the performance of L.RSI.
Semantic information. In the first row of Table 4, we bypass semantic images, directly extracting submaps from raw remote sensing imagery and projecting 3D coordinates to generate point cloud BEV images. Compared to the full 1.2RSI (last row), Recall@l on test set-S decreases by 38.50%, and on LiRSI-Oxford, Recall@1 dropped to 8.13%. This demonstrates that semantics serve as
8

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Table 5: Ablation study about the sampling rate X. Recall@1 (%) on Test Set-S is reported. The
runtime (ms) here only involves STPE. The best result is rendered in bold.
Test
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Recolla1 (%)
83.98
88.07
88.93
88 44
88.44
88.07
88.07
87.82
88.07
88. .4
RT [m]ss)
24.1
29.4
31.7
34.7
38.0
40.2
44.3
47.5
52.2
55,4
-e-1
90.00
70.0
80.00
60.0
70.00
50.0
30.00
30.0
40.00
20.0
15
20
25
35
40
45
50
10
15
20
25
35
40
45
50
The namber perticlaa
The mumber of
Figure 4: Ablation study about the number of queries in the sequence (L) and the number of particles
for each query (K). Solid dots represent the optimal performance for each sequence length.
reliable information capable of bridging domain differences, playing a pivotal role in enhancing the generalization ability.
STPE. We ablate STPE in the second row of Table 4, and observe a performance drop of 58.88% and 32.10% 10% Recall@1 for the two test sets, respectively. In STPE, the GMMs are computed based on the top-K results (K = 30) retrieved from the query sequence. Therefore, we also provide Recall @30 as a reference.
To further explore the impact of key parameters in STPE, we experiment with different settings for the number of queries L in the sequence and the number of particles K for each query. The results are presented in Fig. 4. We observe that L is positively correlated with retrieval performance, while K exhibits peak. Additionally, larger values of and generally lead an increased runtime. Considering the trade-off between performance and computational cost, L 50 and K = 30 are adopted as default configurations.
In order to reduce the runtime, we introduce the sampling rate À to decrease the number of queries involved in STPE, without changing the sequence length. As shown in Table 5, when À 30%, Recall @1 reaches 88.93%, while the refinement overhead per query is only 31.7ms, demonstrating an excellent balance between performance and efficiency. This phenomenon attributed to the fact that the adjacent point cloud submaps are spaced only 5mn apart, resulting in highly redundancy. The sampled queries still encompass the majority of critical information. The qualitative results in Fig. clearly illustrate the effectiveness of STPE.
5.3.3 Impact of Motion Model Failure
In STPE stage, dynamic objects such as pedestrians and vehicles may introduce disturbances into the motion estimation. To investigate the impact of progressive motion model degradation on place recognition performance, we introduce varying levels of yaw angle noise Cyow and translational
(
(I
(1)
(
(a) retrieval results on Test Set-S
(b) retrieval results on 11-14-02-26
Figure 5: Visualization of retrieval results on LiRSI-XA and LiRSI-Oxford b. Points closer to red indicate that more retrieval results are required to achieve correct retrieval at that location. (I) shows the results without STPE, and (II) shows the results with STPE.

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Table 6: Impact of motion model failure in STPE stage. Recal1@1 on Test Set-G are reported. The parentheses indicate the performance drop compared to the additional noise-free setting.
Recall@1(%)
Eyau: =0
Eyau: +5
Eyam =10
Syew +15
Gyee: 4:30
Fay =0m
83.27(-0.00)
80.57(-2.70)
78.72(-4.55)
60.76(-22.51)
48.46(-34.81)
Fay +1m
(2.77(-0.50)
80.20(-3.07)
77.85(-5.42)
59.77(-23.50)
44.26(-39.01)
Fay +4m
71.58(-11.69)
67.04(-16.23)
66.91(-16.66)
56.706-26.57)
41.39(-41.88)
Ezy 99m
67.04(-16.23)
64,94(-18.33)
61.50(-21.77)
54.37(-28.90)
39.12(-44.51)
Czy 116m
57.316-25.96)
(3.87(-29:40)
53.75(-29.52)
50.92(-32.35)
35.91(-47.36)
noise Eny (m) into the estimated motion model. The translation noise is defined by the noise in the x- x-direction and y-direction as follows:
Exy = VV
(11)
where we take Cu Cy for convenience. The average (icaal@@((%) of three repeated experiments on Test Set-G (100km²² of LiRSI-XA are shown in Table 6. Please note that the disturbances in the table represent intentionally introduced additional noise, based on the noise inherently caused by the FastGICP [38] algorithm and the magnetometer used in the system. Eyaw + and Ezy =m indicates that, addition to the inherent noise, random yaw angle noise within a range of +5 and random translation noise within range of 1mm are introduced.
The results demonstrate that our method can tolerate certain level of additional noise in the estimated motion model (Cyaw < =0 and Ezy < #1m), which is typically introduced by dynamic objects affecting the FastGICP algorithm. In practice, we find that excluding the points of the data collection vehicle itself from the raw point cloud effectively reduces this noise to an acceptable level.
5.3.4 Computational cost analysis
Table Computational cost analysis on Test Set-S. The number of parameters (M) and runtime (ms) reported. The best result is rendered in bold.
GDE
STPE
Metric
((M) J. [T(ms)
P(M) L RT (ms) 4
LIP-Loc [21]
46.09
5.7
GeoDTTR [35]
26.82
Sample4Geo [36]
88.59
7.6
FRGeo [37]
27.82
4.6
31.7
In this section, we analyze the number of parameters and runtime of L2RSI in the global descriptor extraction and STPE stages. As reported in Table 7, since Sample4Geo and the proposed L2RSI use heavier backbones for feature extraction, i.e., convnext-B and MAE-B, they have more parameters. However, the runtime difference during the global descriptor extraction (GDE) is minimal. STPE significantly refines retrieval results, with an additional cost of 31.7ms. Nevertheless, the total time of 37.5m. 5ms still allows for real-time operation (< 100ms).
Conclusion
We present L2RSI, the first method for cross-view HIDAR-bascd place recognition within large- scale (over 100km²2 urban scenes using high-resolution remote sensing imagery. We unify LiDAR point cloud submaps and remote sensing submaps into a shared semantic space via carefully- designed contrastive learning network. Furthermore, we propose the Spatial-Temporal Particle Estimation to aggregate the spatio-tempon constraints of sequential queries, thereby further refining performance of global place recognition. Extensive experiments on the LiRSI-XA and LiRSI- Oxford datasets demonstrate the effectiveness and generalization ability of L2RSI. Future work will focus on reducing the reliance on additional magnetometers and further addressing the challenges of deploying this technology in real-world applications.
7
Acknowledgements
This work was partially supported by the Natural Science Foundation of China (No. 62471415), by the Fundamental Research Funds for the Central Universities (No. 20720230033), by Xiaomi Young Talents Program, by the Anhui Provincial Natural Science Foundation (No. 2508085MF142). We would like thank the anonymous reviewers for their valuable suggestions.
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A Appendix
A.1
Limitations
While the proposed framework demonstrates strong performance in LiDAR place recognition over very large-scale remote sensing imagery and exhibits the potential generalize across regions, we acknowledge two limitations of our work:
Firstly, The current success of 1.2RSI requires external provision of a roughly accurate directional reference. Our collection device is equipped with magnetometer that is calibrated with the LiDAR, providing a directional accuracy within =10 degrees.
Secondly, our approach designed for urban scenes, and the performance may be affected in rural areas where the scene semantics are relatively sparse.
In future work, we will focus on overcoming this two limitations.
A.2 Implementation details
The proposed L2RSI is implemented with Pytorch [49]. The network is trained with Adam op- timizer [50] and experiences 100 training epochs. The learning rate set to 5 x 10-5, and the batch size is set to 64. We employ a StepL. learning rate scheduler, which reduces the learning rate by factor of 0.95 every 1000 training steps to facilitate model convergence. All experiments are performed on Ubuntu 20.04.1 with a single NVIDIA GTX 3090 GPU with 24GB of memory.
A.3 Comparison on more datasets
Table 8:
Summary statistics for -ittt-Kit
Submap Num.
Seq. 00
Scq. 02
Seq. 03
Seq. 04
Seq. 05
Seq. 06
Seq. 07
Seq. 09
Seq. 10
PC
2394
2090
241
1838
857
1456
791
1941
593
RSI
3132
5677
5677
5351
5351
5351
5233
7485
7485
Area (km")
2.93
6.30
6.30
6.98
6.98
6.98
8.58
6.06
6.06
KITTI-360 [7] is an urban scene dataset consisting of 9 sequences with a mileage of approximately 80km, commonly used for 3D perception and localization. To enable more comprehensive evalua- tion, we additionally associate each trajectory with corresponding remote sensing imagery. Following the same procedure described in Sec. 5.1, we expand it to training (02. 03, 04, 05, 06, 07), one sequence for validation (00), and two sequences for testing (09, 10). The statistical information of the dataset is summarized in Table 8.
with six sequences for
Table Performance comparison on LiRSI-Kiti360, Recall@l and Recall@10 (%) are reported.
The best result is rendered in bold.
Recal Recallar0 (%)
Methods
Seq. 00
Seq. 09
Seq. 10
LIP-Loc [21]
19.84/56.31
10.87/38.38
2.04/11.93
GeoDTR [35]
5.26/20.01
6.80/19.84
2.73/10.22
Sample4Gco [36]
43.98/80.16
33.08/65.8. 84
14.82/34.58
FRGeo [37]
37.30/71.39
30.09/67.39
9.37/25.04
L2RSI (w/o STPE)
42.44/80.45
31.22/70.22
12,78/36.12
12RSI (w. SuperGlobal [48])
11.90/70.13
31.17/61.51
12.44/25.00 .54
L2RSI (w. PF)
19/19/90
66.55/78.14 14
27.13/34.81
1.2RST (w. STPE)
91.09/96.12
92.28/98.15
42.38/56.13
The quantitative results are presented in Table 9. L2RSI (w. STPE) achieves the best performance across all sequences. Due to the differences the coverage areas of the remote sensing databases, most methods perform slightly worse on Seq. 09 than in Seq. 00. The comparable performance of 1.2RSI (w. STPE) in Sequences algorithm, while L2RSI (w/o STPE) also has similar Recall@30 in both sequences. All methods exhibit noticeably poorer performance in Seq. 10, mainly cause the roadsides in this area are densely covered by trees in the remote sensing imagery, causing the roads to be heavily occluded by vegetation.
and 09 can be attributed to the use of K = 30 in the STPE
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In contrast, the Velodyne HDL-64E LiDAR used in KITTI-360 has vertical FOV of +2" to 24.8" which barely captures the vegetation above the road surface, leading severe semantic inconsistency between the LiDAR and aerial imagery.
A.4 Impact of different semantic encoders
Table 10: Ablation study about the selection of the semantic encoder. Recall@1 and Recall@10 (%) on LiRSI-XA and LiRSI-Oxford. i indicates that the input image size is 518 x 518. The best result is rendered in bold.
11coll@1 / Recall (%)
Backbones
Test Set-S
Test Set-M
Test Set-L
Test Set-G
11-14-02-26
14- 12-05555-
VGGT6 [51]
73.77788.00 04
62.83/3.27
51.39/69.96
47.45/62.83
21.60/38.86 86
80.82/36.85
ResNet50 [52]
59.38/76.99
51.26/67.01
48.93/61.60
43.52/55.20
13.56/23.27
11.73/22.82
ResNet101 [52]
72.58/87.70
68.02/81.80
62.49/76.02
54.62/66.18
24.66/41.26
26,87/43.21
ConvNeXI-S [53]
83.75/88.24
77.73/81.11
74.40/78.03
67.15/73.67
30.30/51.05
31.91/49.60
DINOv2-ST [54]
84.72/90.98
78.32/87.34
65.41/78.32
68.12/77.09
1,40/3.66
0,86/3.21
MAE-B B [47]
88.93/92.13
87.95/91.64
85.49/90.65
83.27/87.82
42.29/59.41
43.77/62.76
We consider several popular backbones in the selection of the semantic encoder, including VGG [5 I.
ResNet [52], ConyNeXt [53], DINOv2 [54] and MAE [47]. Table 10 shows the results evaluated on the test sets of LiRSI-XA and LiRSI-Oxford. Specific pre-trained model versions are selected based on their GPU memory requirements. MAE-B performs the best, especially in terms of generalization ability. We attribute this to mask modeling during training, which aids the model in interpreting the sparse semantic images generated from point cloud projections. DINOv2-S from the official source uses default image size of 518 x 518. Images that are resized are marked with (t). Furthermore, to accommodate single-GPU training, the batch size is set to 8, which hinder the model generalize to LiRSI-Oxford.
A.5 Compatibility with existing pipelines
Table 11: Ablation study about the compatibility of STPE with other existing networks on LiRSI-XA and LiRSI-Oxford. Recall@1 and Recall 10 (%) are reported. The best result rendered in bold.
RecalTwl / Recallu110 (%)
Methods
Test Set-$
Test Set-M
Test Set-1.
Test Set-G
11-14-02-26
14-12-05-52
Lip-Loc [21]
52,89/77.86
46.86/69.74 74
42.07/60.64
32.23/46.74
4.07//1.69
3.58/18.68
GeoDTR [35]
47.52/67.81
39.40/55.26
32.41/45.06
11.33/17.85
1.97/5.46
1.1
Sample4Geo [36]
83.12/89.24
76.27/85.23
70.36/80.43
62.49/72.59
32.24/48.33
31.33/52.10
FRGco [37]
81.34/85.55
68.94/77.80 80
62.32/72.28
53.95/63.06
7.95/16.87
7.98/17.39
The proposed STPE is easily compatible with existing place recognition pipelines. In Table 11, we refine the retrieval results of the baseline methods in using STPE. The results clearly indicate that all the methods achieve an improvement in recall on multiple test sets. However, when the vast majority of queries in the sequence fail completely, STPE is provided with insufficient useful information, as evidenced by the poor performance of Lip-Loc, GeoDTR and FRGeo on LiRSI-Oxford. Undoubtedly, when the relative position between LiDAR scans is available, the proposed STPE demonstrates superior effectiveness and compatibility with existing retrieval-based place recognition methods.
A.6 Visualization
We present visualization of the 100 square kilometer Test Set-G database in Fig. 6. As shown, over 60,000 remote sensing submaps are scattered across nearly all the major roads in the city. The proposed method does not require the assumption that the vehicle's trajectory is known, and it is capable of performing place recognition on all potential roads.
In Fig. 7, we present some qualitative comparisons of top-1 retrieval results on Test Set-G. Some challenging queries are selected. As can be seen, for most methods, even when the retrieval results are incorrect, they still tend to follow a road direction similar to that of the query. Especially in the fourth and sixth rows, where the query only shows roads and dense vegetation on both sides, the area within a 100km? range contains numerous highly similar locations. Competitors are helpless, while the proposed 1.2RST (w. STPE), by fully utilizing spatio-temporal information, achieves the correct Top-1 retrieval.
22

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Figure 6: The visualization of the database on Test Set-G. The black circle marks the positions of the remote sensing submaps in the database.
The last row shows failure case. Although our method has made significant effort find highly similar location, including the east-west road, appropriate vegetation, buildings on both sides, and the road extending towards the upper right corner. However, in the reference remote sensing image, the south side of the correct location lacks buildings, which is due to the three-year temporal difference between the remote sensing image and the LiDAR data. In addition, we provide video more vividly showcase the place recognition results of L2RSI. The proposed method performs poorly on the east-west oriented road at the beginning of the video, which corresponds the segment with serious temporal differences mentioned earlier.
A.7 More details comparing with particle filtering
Table 12:
The selection of particle number K for particle filtering. Recall@1 (%) Test Set-S is
reported. The runtime (ms) here only involves particle filtering. The result is rendered in bold.
Test set-S
60
80
100
110
120
130
140
160
180
Recallu1 (%)
51.92
52.50
66.78
66.66
71.66
58.42
52.26
54.82
53.43
(T(ms)
19.60
19.40
19.50
19.70
19.90
20.00
19.70
19.90
19.50
In the main paper. we mention the deployment particle filtering in the retrieval framework to compare with the proposed particle estimation algorithm. Here we provide the specific details. We begin by generating the initial particles based on the top-K retrieval of the first query. When the second query arrives, we update the positions of particles using the relative pose between the two queries provided by Fast-GICP [38], along with the directional information from the magnetometer. Particles that fall within the R 30m of the top-K results of the second query are retained. If no particles survive, we regenerate new particles based on the top-K retrieval of the current query. Table 12 provides a performance comparison for different K. We ultimately chose K 120 as the initial number of particles. In our tasks, due to unsatisfactory results for single queries, the particles need to be reset multiple times, which results in performance being weaker compared to the proposed STPE algorithm.
23

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Query
1.RRST (w. STPE)
1.2SIT (w. PF)
12RSI (w/b. STPE)
Sample4Geo
GeoDTR
FRGeo
Figure 7: Qualitative comparisons on Test Set-G. Light blue boxes indicate the query point cloud submaps. Green boxes indicate correct retrieval results, while red boxes indicate incorrect results. To facilitate identification for the readers, we rotate the query to align with the same orientation as the remote sensing imagery.
24


Reviews:
[{"review_id": "uo7jhI9A5u", "reviewer": "Reviewer_hHQY", "summary": "This paper tackles the problem of place recognition in large-scale urban environments using LiDAR point clouds without relying on a pre-built 3D map. Traditional methods either assume the availability of costly 3D maps or perform retrieval only within limited areas, which hinders scalability. To overcome these limitations, the authors propose L2RSI, a contrastive-learning framework that aligns LiDAR submaps with high-resolution remote sensing imagery in a shared semantic space. They further introduce STPE (Spatial-Temporal Particle Estimation), which models the spatio-temporal information of consecutive queries as a particle set to compensate for the instability of single-query predictions. Experiments on the XA-L&RSI and Oxford-L&RSI datasets demonstrate that validating both its effectiveness and its generalization capability.", "questions": "1. Will you publicly release the XA-L&RSI and Oxford-L&RSI datasets?\n\n2. Are there alternative strategies that could achieve comparable performance without using ego-motion information?\n\n3. How was end-to-end latency managed during real-time robotic operation, and what was the total pipeline delay?", "limitations": "Yes.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n\n1.The paper provides a clear problem definition and strong motivation, and the methodology is presented in a logical, easy-to-follow manner.\n\n2. It convincingly demonstrates the effectiveness of combining semantic contrastive learning with STPE for cross-view, cross-modal place recognition over a large (100 km²) urban area.\n\n3. Thorough evaluations on multiple datasets and retrieval ranges showcase the robustness and generalization of the proposed approach.\n\n\nWeaknesses:\n\n\n1. There is insufficient discussion on how to minimize the additional overhead introduced by the STPE module for real-time applications.\n\n2. The reliance on ego-motion measurements and magnetometer data raises concerns about applicability in scenarios where these sensors are unavailable.", "was_revised": true, "final_justification": "My primary concerns regarding STPE overhead, dataset availability, and real-time latency have been fully resolved. I therefore maintain Borderline Accept.", "rebuttal": "# Q1: How to reduce STPE overhead for real-time use?\n\nThank you for the insightful question. In practice, since our L2RSI framework incorporates the efficient FastGICP algorithm, the STPE module does not need to be activated for every query. Instead, it can be triggered periodically for high-precision place recognition. Between two STPE activations, relative poses can be propagated using FastGICP. This trigger-based design significantly reduces the computational overhead introduced by STPE in real-time applications.\n\n# Q2 and Q4: Dependence on magnetometer data.\n\n**Table1 : Ablation study about effectiveness of components. (Table 4 in the main paper)**  \nResults include _Recall@1_ and _Recall@30_ (%)↑ on **Test Set-S** and **11-14-02-26**.  \n**Sem.**: Semantic segmentation. **STPE**: Spatial-Temporal Particle Estimation. **Orient.**: Orientation.  \nThe best results are in **bold**.\n\n| Sem. | STPE | Orient. | Test Set-S | 11-14-02-26|\n|:--:|:--:|:--:|:--:|:--:|\n|| ✔ | ✔| 50.43 / 67.90  | 8.13 / 25.42  |\n| ✔    || ✔       | 30.05 / 85.50  | 10.19 / 45.90 |\n| ✔    | ✔    || 54.37 / 76.14| 4.92 / 21.24|\n| ✔| ✔ | ✔ | **88.93 / 95.94** | **42.29 / 70.15**|\n\nAs noted in our Limitations section (A.1 in the Appendix), the proposed framework relies on magnetometer data to provide a coarse orientation prior. This is crucial because, at the city scale (covering an area of $100 km^2$), LiDAR-to-remote-sensing-image retrieval is highly challenging. Even a rough orientation estimate significantly facilitates the learning of cross-view and cross-modal global descriptors. As shown in Table 1 (third row),  removing this directional input significantly degrades the performance of place recognition. In future work, we plan to address this limitation by exploring orientation-invariant or self-supervised alternatives.\n\n# Q3: Datasets open source.\n\nThank you for your interest in our dataset. We have already obtained the necessary open-source licenses for remote sensing imagery. The XA-L&RSI and Oxford-L&RSI datasets will be released to the research community upon the official publication of the paper.\n\n# Q5: End-to-end latency during real-time robotic operation.\n\nThanks for your valuable question. The total inference latency of the proposed L2RSI pipeline is approximately **$290ms$**, which includes around **$200ms$** for point cloud semantic segmentation, **$50ms$** for generating the semantic BEV image, and **$40ms$** for retrieval and STPE refinement per query. It is worth noting that our current implementation focuses on offline evaluation to validate the core contributions, and has not yet been optimized for deployment. In practical applications, the system can adopt a trigger-based design, where high-accuracy place recognition is performed periodically, which is sufficient to meet the requirements of typical urban autonomous localization tasks."}, {"review_id": "qxjWg6bzE5", "reviewer": "Reviewer_z1Y6", "summary": "This work introduces a novel method for cross-view LiDAR-based place recognition. The proposed approach extracts semantic maps from aerial images and LiDAR BEV scans, leveraging a contrastive learning framework to align features in a shared semantic space. Additionally, the authors introduce a probability propagation method based on particle estimation to refine position predictions. Experimental results on two self-collected datasets demonstrate significant performance improvements over existing methods.", "questions": "See weakness. Section 3.4 could be improved for easy understanding.", "limitations": "Limitations regarding data coverage, dependent segmentation models should be discussed.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1)The focus on LiDAR-based cross-view place recognition is innovative and addresses an important research gap.\n2)The idea of aggregating spatio-temporal information and inferring probability density for position refinement is both interesting and theoretically sound.\n3)The experiments show clear and significant improvements over prior methods, validating the effectiveness of the proposed approach.\n\nWeaknesses:\n1)The key to the proposed method is particle aggregation. However, section 3.4 is hard to follow. The usage of mathematical notations can be improved.\n2)The collected datasets only cover two cities.\n3)Missing metadata information of satellite images, such as capturing time, locations, and sensors.\n4)Format issues noticed. Table 5 appears before Table 4.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns. Therefore, I will raise my score.", "rebuttal": "# Q1 and Q5: Sec. 4.3 is difficult to understand.\n\nThank you for your suggestion. We acknowledge that the current presentation of Section 4.3 can be improved in terms of clarity and the use of mathematical notations. \n\nSection 4.3 introduces the STPE algorithm, which is designed to alleviate the unreliability of individual query retrieval results by leveraging information from a sequence of past queries.\n\nWe utilize a sequence of past queries. For each query, we treat its top-K retrieval results as particles, where the state of each particle is defined by its x and y coordinates. Eq. 3 illustrates that, if the motion model is known, each particle can be propagated forward in time, with added random noise reflecting uncertainty. In practice, the motion model at each time step is directly obtained using FastGICP combined with magnetometer-based heading correction. \n\nThe original intention of Eq. 4 and Eq. 5 was to express the probabilistic distribution of a particle's state after motion in a single time step. However, these equations may hinder understanding rather than clarify it, so we propose to remove them.\n\nEq. 6 models the initial distribution of particles at a single past time step, which we assume follows a Gaussian Mixture Model (GMM). Since the x and y dimensions are independent, we use a diagonal covariance matrix (this step was omitted for brevity).\n\nEq. 7, Eq. 8, and Eq. 9 describe how the key parameters of the GMM are estimated. Eq. 10 models the distribution of particles propagated from a single past time step to the current time step. Finally, Eq. 11 aggregates the distributions from multiple past time steps to form the probability density function at the current position. Eq. 12 then computes the probability score for each candidate remote sensing submap, which is used to re-rank the retrieval results for the current query.\n\nIn the camera-ready version, we will carefully revise this section to provide a clearer explanation of the particle aggregation process. We will also include intermediate steps and intuitive descriptions to better guide readers through the derivation, ensuring that this part is more accessible and readable in the final version.\n\n# Q2 and Q6: The datasets only covers two cities.\n\n**Table 1 : Zero-shot performance comparison on Kitti-360 Seq. 00 and Oxford-L\\&RSI .**  \n_Recall@1_ and _Recall@10_ (%)↑ are reported.\n||Kitti-360|Oxford|Oxford|\n|---|--|--|--|\n| Methods |Seq. 00 |11-14-02-26|14-12-05-52|\n| GeoDTR| 0.72 / 6.07|0.47/3.18 |0.48/3.71|\n| Sample4Geo | 5.84 / 23.64|7.13/20.68| 7.51/21.75|\n| FRGeo  | 2.87 / 14.10  |1.62/7.34 |1.58/7.86|\n| L2RSI (w/o STPE) | 6.46 / 24.67|10.19/30.23 |11.25/30.58|\n| L2RSI (w. STPE) | **41.20 / 67.84**|**42.29/59.41**| **43.77/62.76**|\n\nWe appreciate the concern of the reviewer. We are also willing to perform broader evaluations. However, existing mainstream localization datasets do not provide corresponding high-resolution remote sensing images. Due to time constraints during the rebuttal period, we selected Sequence 00 from the KITTI-360 and manually constructed a new zero-shot test set by equipping it with corresponding satellite imagery. The data processing is consistent with that of XA-L\\&RSI and Oxford-L\\&RSI. The new test set contains **$1532$** Lidar submaps, **$2747$** remote sensing submaps, and remote sensing images covering an area of **$2.28km^2$**. As shown in **Table 1**, our method still achieves the best performance on this new benchmark. We will provide additional results trained and evaluated on KITTI-360 in the camera-ready version.\n\n# Q2 and Q6: Dependence segmentation models\n\n**Table 2: Ablation study about different semantic estimators.**\n\n_Recall@1_ and _Recall@10_ (%)↑ are reported.\n| Semantic Estimator | Methods             | Test Set-S        | Test Set-M        | Test Set-L        | Test Set-G        |\n| -- |-- | -- | -- | -- |-- |\n| HSSC | L2RSI (w/o STPE) | 29.62 / 69.22     | 21.11 / 57.04     | 18.91 / 53.57     | 17.75 / 48.32     |\n|                    | L2RSI (w. STPE) | 86.27 / 95.90     | 84.73 / 91.92     | 79.75 / 85.71     | 76.99 / 81.40     |\n| SphereFormer       | L2RSI (w/o STPE)    | 30.05 / 68.56     | 23.90 / 61.95     | 21.93 / 57.42     | 20.07 / 52.55     |\n|                    | L2RSI (w. STPE) | **88.93 / 92.13** | **87.95 / 91.64** | **85.49 / 90.65** | **83.27 / 87.82** |\n\nThanks for your question. To evaluate the robustness of our method, we replaced SphereFormer with HSSC [1], and observed only a minor drop in performance. This indicates that our framework is not sensitive to the specific choice of segmentation model, as shown in **Table 2**. \n\n**[1]** Chen, S., Yang, B., Xia, Y., et al. *Bridging LiDAR gaps: A multi-LiDARs domain adaptation dataset for 3D semantic segmentation*. In *Proc. of IJCAI*, 2024, pp. 650–658.\n\n# Q3: Missing metadata information of satellite images\n\nWe thank the reviewer for pointing this out. For XA-L\\&RSI, remote sensing image data was captured in 2022 and 2023, covering the Xiang'an District of Xiamen, China (approx. $24^\\circ32^\\prime N$–$24^\\circ41^\\prime N$, $118^\\circ10^\\prime E$–$118^\\circ20^\\prime E$). For Oxford-L\\&RSI, remote sensing image data was captured in 2019, covering the Xiang'an District of Xiamen, China (approx. $51^\\circ44^\\prime N$–$51^\\circ45^\\prime N$, $1^\\circ14^\\prime W$–$1^\\circ16^\\prime W$). All the remote sensing image data was collected by the Beijing-3 satellite, using sensors BJ3N1, BJ3N, BJ3N2, and BJ3A1.\n\n# Q4: Format issues noticed.\n\nThank you for your careful review. We will thoroughly check the entire paper for formatting and spelling issues, and make the necessary corrections in the camera-ready version."}, {"review_id": "L2WwqwuN6B", "reviewer": "Reviewer_HoHD", "summary": "The paper addresses place recognition between a lidar point cloud query and a database of remote sensing image (i.e. Bird Eye View (BEV) aerial sattelite or drone images).\n\nThe contribution is a network that generates embeddings in a shared space for both the point cloud and the BEV images.\nThe network can take as input either the BEV aerial image or a BEV rasterization of the point cloud.\nTo make the embedding even more informative, the network is fed a semantic segmentation of either input: a standard semantic of the BEV aerial image; the point cloud is first segmented then rasterized.\n\nThe network is trained with the standard InfoNCE loss.\n\nTo demonstrate the relevance of the method, the paper further contribute a training and testing dataset made of aerial images and point cloud submaps.\nPart of the dataset is used for training their method and another non overlapping part is used for testing, that covers 100km2, which emulates a large-scale place recognition scenario.\n\nThe paper further completes the existing Oxford retrieval dataset with aerial images, as that already provides point cloud data but not aerial data.\nThis dataset serves to demonstrate the generalizability of the trained network.\n\nThe paper is compared against cross-view image-based place recognition i.e., natural images against a database of aerial images.\nThe proposed method outperforms the baselines demonstrating the relevance of the proposed method.", "questions": "- What augmentations are applied to the images? Are they rotated along the vertical axis to augment their robustness to orientation variations?", "limitations": "No, the authors have not adequately addressed the limitations of their work.\nThere is no potential negative societal impact of their work\n\nEvaluation:\n- The threshold used in the recall definition is 30 meters which seems to small given the resolution of the input images (that cover 60x60 meters). What is the query point cloud is 40 meter away from the center of the closest submap? Then it is within a submap that should be retrieved but the recall will still be 0.\n\nMisc comments:\n- L192: \"top-C\" should be \"top-K\"?", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "S1. The problem of cross-modality (lidar vs image) and cross-view (natural lidar cloud vs aerial images) place recognition is relevant to the research community and the industry (autonomous systems and driving).\n\nS2. The method is simple and efficient, making it reasonnable to deploy.\n\nS3. The paper provides annotated data (pairs of lidar clouds and their associated aerial images) for training and testing.\n\n\nW1. The related work is incomplete. Here is a non exhaustive list of missing references:\n\n[1] Tang, Tim Y., Daniele De Martini, and Paul Newman. \"Get to the point: Learning lidar place recognition and metric localisation using overhead imagery.\" Proceedings of Robotics: Science and Systems, 2021 (2021).\n\n[2] Shubodh, Sai, et al. \"Lip-loc: Lidar image pretraining for cross-modal localization.\" Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2024.\n\n[3] Xu, Huaiyuan, et al. \"C2L-PR: Cross-modal camera-to-LiDAR place recognition via modality alignment and orientation voting.\" IEEE Transactions on Intelligent Vehicles (2024).\n\n[4] Lee, Alex Junho, et al. \"$^{2} $: LiDAR-Camera Loop Constraints for Cross-Modal Place Recognition.\" IEEE Robotics and Automation Letters 8.6 (2023): 3589-3596.\n\n[5] Komorowski, Jacek, Monika Wysoczańska, and Tomasz Trzcinski. \"Minkloc++: lidar and monocular image fusion for place recognition.\" 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021.\n\n[6] Li, Yun-Jin, et al. \"VXP: Voxel-Cross-Pixel Large-Scale Camera-LiDAR Place Recognition.\" International Conference on 3D Vision 2025.\n\n[7] Guan, Tianrui, et al. \"Crossloc3d: Aerial-ground cross-source 3d place recognition.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.\n\n[8] Fervers, Florian, et al. \"Continuous self-localization on aerial images using visual and lidar sensors.\" 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022.\n\n[9] Tang, Tim Yuqing, Daniele De Martini, and Paul Newman. \"Point-based metric and topological localisation between lidar and overhead imagery.\" Autonomous Robots 47.5 (2023): 595-615.\n\nW2. The evaluation compares only against cross-view (natural images vs aerial images) place recognition but not against cross modality (lidar vs aerial images) methods, which makes the evaluation severely incomplete. \nRelevant methods for which code is available include [2], [3], [4]\n\n\nW3. The mathematical derivation in the particle-filter-based filtering of the retrieval candidate are confusing:\n- the state of the particle is not defined (the reader can guess that it contains the x,y coordinate of the system only?)\n- L201: What does \"aggregating the particles\" mean? That a new set of particle is added at time t to the previous obtained at t-1 by applying the motion model to all particles of t-1 and saving the particle before and after the motion?\n- Eq4 is incorrect: the conditioning variable $x_{t-1}$ replacement is not explained\n- There is no link between equations (4) and (5)\n- The Gaussian distributions starting Eq6 all miss their covariances\n- the motion step of the filter in eq10 implies that there is no noise since only the means are updated but not the covariance whereas Eq3 defines a noise v in the moition model\n\n\nGiven that important baselines are missing from the evaluation (there is not comparison against lidar-vs-image place recognition methods, the baselines are all image-vs-image methods that were retrained on the lidar-vs-image dataset), the incomplete related work and the confusion in the proposed filtering method, the paper does not pass the acceptance bar.", "was_revised": true, "final_justification": "Thank you for the explanations, they are very useful and clarify how the estimator works.\n\nThese details are important and deserve to be in the main paper (some space may be saved by writing some of the equations in lines with the text).\n\nSimilarly, reviewer 4DTw raised an important problem regarding the reliance of the \"retrieval temporal estimation\" on the motion model and how it can be \"impacted by the motion model's failure\". The additional experiments in the authors' answer that simulate such failure are very relevant and worth adding in the paper (or the supplementary).\n\nThe rebuttal addresses the main limitations that motivated the previous decision:\n\nrelated work: some of the references were missed in the review, I apologize\ncomparison with other lidar to aerial image retrieval: it appears that previous such methods did not release their code so a comparison is not possible without an important engineering effort, so it is acceptable that these comparisons are missing\ntemporal estimation of the retrieval result: the derivation is better explained in the rebuttal and these details should be included in the paper.\nIt would be useful to explicitly mention how the proposed temporal estimation differs from previous temporal or sequence-based methods for example:\n\nDynamically Modulating Visual Place Recognition Sequence Length For Minimum Acceptable Performance Scenarios C Malone, A Vora, T Peynot, M Milford, IROS 2024\nSeqmatchnet: Contrastive learning with sequence matching for place recognition & relocalization S Garg, M Vankadari, M Milford - Conference on Robot Learning, 2022\nand several others\nSince the rebuttal addresses the main limitations and that the dataset contributed in this paper and that the temporal estimation used in the retrieval is sound and useful (see Tab3 L2RSI vs L2RSI with STPE), the rating is raised to accept.", "rebuttal": "# Q1: The related work is incomplete.\nThanks for raising this concern. Firstly, among the nine references mentioned as missing, four are already cited in the introduction or related work sections, including (LC)$^2$ [4], VXP [6], Tang et al. [9], and the journal version of Tang et al. [1].\n\nSecondly, the motivation of this work is to develop a capability for immediate localization upon first entry into an unknown environment, without relying on GPS or a pre-built 3D map. Therefore, we choose globally available and easily accessible remote sensing imagery as the reference. In terms of related work, we focus on cross-view and cross-modal retrieval. The remaining five papers in the list are as follows:\n\nAs shown in **Table 3**, since [2], [3], [5], and [7] all rely on a pre-built 3D map of the target area constructed from ground-level or low-altitude observations, they are not directly related to our work.\n\n[8] is more accurately described as a geo-tracking method. It requires an initial absolute pose and uses multiple ground cameras and a LiDAR sensor to match against a small ($100\\times100m^2$) satellite image. This setup is completely unrelated to our problem.\n\n**Table 3: The task settings of the mentioned Related Works.**\n\n|Methods|Query|Reference|\n|---|---|---|\n| [2], [3]  | Ground Camera   | Ground LiDAR |\n| [5]| Ground Camera + Ground LiDAR | Ground Camera + Ground LiDAR |\n| [7] | Ground LiDAR | Aerial LiDAR |\n| Ours | Ground LiDAR | Remote sensing image  |\n\nIn summary, since these works are not strongly related to our approach, we did not cover the entire set of literature from those fields. Nevertheless, we will include [2], [3], [5], and [7] in the Related Work of the camera-ready version.\n# Q2: Lack of comparison with the methods of Lidar to aerial images.\n**Table 4: Supplementary performance comparison.**\n\n$Recall@1$ and $Recall@10$ (%)↑ are reported.\n| Methods |Test Set-S | Test Set-M| Test Set-L| Test Set-G  | 11-14-02-26| 14-12-05-52|\n|---|---|---|---|---|---|---|\n|PointLoc [1]| 10.37/27.63 | 7.89/24.61| 6.91/22.32| 6.52/19.85| 0.92/7.63| 1.01/8.14|\n| LIP-Loc (official) [2] | 3.13/10.32 | 1.74/8.12| 1.51/7.19| 0.46/3.94 | 1.18/5.48 | 1.26/6.10|\n| LIP-Loc (BEV) [2]  | 16.82/53.48 | 13.69/45.36| 11.60/41.65 | 11.02/39.44| 0.88/10.78| 0.96/11.13  |\n| L2RSI (w/o STPE) | 30.05/68.56 | 23.90/61.95 | 21.93/57.42  | 20.07/52.55 | 10.19/30.23 | 11.25/30.58 |\n| L2RSI (w. STPE)  | **88.93/92.13** | **87.95/91.64** | **85.49/90.65**| **83.27/87.82** | **42.29/59.41** | **43.77/62.76** |\n\nThanks for the suggestion. However, the recommended baselines [2], [3], and [4] address the place recognition between LiDAR and street-view camera rather than LiDAR and aerial image, and are not directly related to our work. \n\nAmong them, [3] and [4], the proposed methods are tightly coupled with monocular depth estimation from street-view imagery. However, high-quality depth estimation is not feasible for satellite imagery due to the lack of reliable geometric cues. As a result, these methods cannot be directly applied to our setting, and a fair comparison is not possible.\n\n[2] projects LiDAR into a range image and then performs image-to-image retrieval against street-view imagery. Given the substantial viewpoint discrepancy between range images and satellite imagery, along with the constraint in [2] that restricts retrieval to the vehicle's trajectory, it is expected that directly transferring this method to large-scale, city-level retrieval would result in poor performance. In **Table 4**, we retrained and compared both the official implementation of [2] and a variant where the range image is replaced with a magnetometer-corrected semantic BEV image in XA-L\\&RSI, to provide a more fair and meaningful comparison. They both significantly underperform our method across all benchmarks.\n\nIt is important to highlight that, to the best of our knowledge, our work is the first to tackle LiDAR-to-Remote-Sensing-Imagery place recognition in a city-scale area ($100km^2$). In the list of **Q1**, [1] and [9] are the most relevant. Unfortunately, neither of them is open-sourced, and [9] does not even provide a network architecture diagram, which significantly increases the difficulty of reproduction.\n\nMoreover, both [1] and [9] are designed for trajectory-constrained retrieval, meaning they assume localization occurs only along pre-defined driving routes. As a result, their performance degrades quickly as trajectory length increases, which limits their applicability in large-scale, unconstrained scenarios.\n\nDuring the rebuttal period, we made our best effort to re-implement the approach proposed in [1], and the results are reported in **Table 4**. The performance remains substantially below that of our method.\n# Q3: Mathematical derivation of STPE.\nThank you for carefully reviewing our mathematical derivation.\n\n- **Yes**, the state $\\textbf{x}_j^{[k]}$ of a particle refers to the 2D coordinate $(x, y)$ of a candidate submap in the remote sensing database retrieved by the query $Q_j$.\n\n- By \"aggregating the particles\", we refer to propagating particles from multiple past time steps *(t−L to t−1)* to time step *t* using the motion model, and then constructing a probability density function at time step *t* based on the combined particle distribution.\n\n- We agree that Eq. 4 and Eq. 5 may cause confusion. Our original intention was to express, in probabilistic terms, how the particles from time step *t−1* transition to time step *t*. However, as described from L205 to L209 of the main paper, the motion of the particles are directly obtained using FastGICP and magnetometer-based heading correction. Therefore, we have removed Eq. 4 and Eq. 5 to avoid ambiguity. This change does not affect the overall flow of the derivation. \n\n- We assume the x and y dimensions are independent, and thus the diagonal covariance matrices are used in Eq.6 and subsequent Gaussian mixture models :\n$$\n\\Sigma =\n\\left[\n\\begin{array}{cc}\n\\sigma_x^2 & 0 \\\\\\\\\n0 & \\sigma_y^2\n\\end{array}\n\\right]\n$$\n\n- The noise term v in Eq. 3 originates from the motion estimation process based on FastGICP combined with magnetometer-based heading correction. This procedure inherently produces noisy estimates of the motion model. When aggregating many particles over multiple time steps, the effect of such noise tends to average out. Therefore, we model the distribution at time t empirically as a mixture of multiple gaussian models without explicitly including a separate noise term in Eq. 10. Empirically, this approximation works well in our experiments. (Table 3 and Table 4 in the main paper, **please see Table 6 and Table 7 included in the response to Reviewer 4DTw for convenient reference.**)\n\n# Q4: Data enhancement.\nYes, we applied data augmentation. Specifically, we performed random rotations along the z-axis within $\\pm 15^\\circ$ to simulate heading noise and improve robustness to magnetic disturbances.\n# Q5: Limitations.\nThank you for the comment. As noted in the Limitations (Appendix A.1), our method relies on magnetometer-based heading priors and is designed for urban environments. Performance may degrade in semantically sparse rural areas. We acknowledge these limitations and plan to address them in future work.\n# Q6: Threshold of $30m$ in evaluation.\n**Table 5: Ablation study about the distance threshold of Retrieval θ.**\n\n$Recall@1$ and $Recall@10$ (%)↑ are reported.\n| θ| Methods| Test Set-S|Test Set-M| Test Set-L| Test Set-G| 11-14-02-26|14-12-05-52|\n|:--|:--|:--|:--|:--|:--|:--|:--|\n|30| L2RSI (w/o STPE) | 30.05 / 68.56| 23.90 / 61.95| 21.93 / 57.42| 20.07 / 52.55 |10.19 / 30.23| 11.25 / 30.58|\n|| L2RSI (w. STPE)  | 88.93 / 92.13| 87.95 / 91.64| 85.49 / 90.65| 83.27 / 87.82|42.29 / 59.41| 43.77 / 62.76 |\n|40| L2RSI (w/o STPE) | 36.54 / 73.78| 29.81 / 70.77| 27.38 / 66.01| 25.06 / 62.41| 45.33 / 63.71| 45.31 / 64.98|\n|| L2RSI (w. STPE)  | **90.04 / 97.17**| **88.19 / 95.08**| **87.82 / 94.46**| **86.47 / 91.64**| **12.73 / 33.18**| **13.88 / 34.29**|\n\nWe appreciate the reviewer’s suggestion. The choice of a distance threshold $\\theta=30m$ is a deliberate trade-off. During training, a larger threshold reduces the spatial overlap between LiDAR and positive remote sensing images, which weakens the effectiveness of contrastive learning. A smaller threshold, however, requires denser remote sensing submap sampling to ensure every LiDAR query has valid positives.\n\nDuring inference, increasing the threshold simplifies the retrieval task and leads to higher recall, but with weaker spatial alignment. Our $30m$ threshold ensures at least $50\\%$ overlap between LiDAR BEV images and positive remote sensing images to reflect a more realistic and challenging setting.\n\nFor reference, we provide results under a threshold $\\theta=40m$ in **Table 5**, which show higher recall due to relaxed retrival criteria.\n\n# Q7: L192: top-C should be top-K?\nThank you for the question. We intentionally use \"top-C\" to refer to the number of retrieval candidates produced by global place recognition for a single current query ($C=200$ in our setting). In contrast, \"K\" denotes the number of top retrieval results selected from each historical query in the sequence during the STPE refinement stage ($K=30$ in our setting). If $L$ denotes the number of historical queries in the sequence, the STPE module operates on $K\\times L$ particles in total and re-ranks the top-C retrieval candidates of the current query."}, {"review_id": "wIoWxd48eJ", "reviewer": "Reviewer_4DTw", "summary": "This paper addresses the problem of LiDAR-based localization in large-scale urban environments without relying on GPS or pre-built maps. It proposes a cross-view, cross-modality localization framework called L2RSI. The method achieves the matching between remote sensing imagery and LiDAR BEV (bird’s-eye view) maps through a framework that integrates semantic feature extraction, contrastive learning, and Spatial-Temporal Particle Estimation.", "questions": "See the weaknesses", "limitations": "See the weaknesses", "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\n1.\tThe paper is well-structured and well-written. The proposed localization framework, which combines semantic feature extraction, contrastive learning, and Spatial-Temporal Particle Estimation, is easy to understand, and the experimental design is relatively rigorous.\n2.\tThe engineering value is significant. The LiDAR + magnetometer-based localization solution eliminates the dependence on GPS or pre-built maps, making it practically meaningful for real-world engineering applications.\n3.\tExperiments are comprehensive. Extensive evaluations are conducted on two datasets, comparing the proposed method against multiple state-of-the-art approaches as well as different ablation variants of the method itself. The experimental design is well-structured and reasonable.\n\n**weaknesses:**\n\n1.\tThe completeness of this article is good, but the method itself lacks novelty and might be more suitable for submission to conferences or journals focused on engineering applications.\n\n2.\tThe framework of “semantic feature extraction + contrastive learning” is not novel; this framework has been ubiquitously applied in the Place Recognition field. \n\n3.\tIn Eq. 3 of the paper, the authors do not explain the motion model Mt-1, nor do they discuss the impact of the motion model's failure. If the FastGICP algorithm is used, consideration should be given to the interference from dynamic objects (such as pedestrians, vehicles, etc.) on motion estimation. Pose estimation drift would degrade the accuracy of point cloud submap generation, consequently reducing localization precision.\n\n4.\tA major highlight of the paper is the construction of the XA-L&RSI dataset. The lack of high-quality datasets is a significant impediment to progress in this field. However, the authors state in the checklist that this dataset will not be open-sourced, which limits the impact of this work.", "was_revised": true, "final_justification": "The authors addressed key concerns with empirical results and detailed ablations. The STPE module shows practical value despite limited novelty, and the dataset will be open-sourced, enhancing impact. Some concerns remain (e.g., motion model sensitivity), but overall the paper offers a solid and useful contribution. I recommend borderline accept.", "rebuttal": "# Q1 and Q2: Lack of novelty in the method and framework.\n\nThanks for your question. The main contribution of this work relies on two aspects. \n\nFirst, we attempt to build an ability that can be located immediately upon entering an unknown area for the first time, without GPS, without the pre-built map. To achieve this, our work has two settings that are significantly more challenging compared to traditional place recognition works: (1) High-resolution remote sensing imagery, which is easy to acquire, is applied for the first time as reference maps for retrieval in conjunction with lidar point cloud queries. (2) We have expanded the retrieval range to $100 km^2$, rather than searching along trajectories. For this cross-view, cross-domain retrieval problem, under such a strict setting, most previous works fail to provide satisfied performance, and we achieve a retrieval rate higher than $80\\%$ for the first time. \n\nSecond, at the technical level, our proposed retrieval framework based on semantic domains is a principled and effective solution, not a naive combination of existing modules. We conducted comprehensive ablation and comparative experiments on each component of the framework, demonstrating that our method provides a more robust and innovative integration (**Table 6 - Table9**). The core contribution lies in the proposed Spatial-Temporal Particle Estimation (STPE) module, which fully leverages both spatial and temporal continuity in sequential queries. This leads to a substantial improvement in retrieval accuracy, boosting Recall@1 from $20.07\\%$ to $83.27\\%$.\n\nBased on this, we believe that the exploration and technique contribution in this work will bring beneficial inspiration to the community.\n\n**Table 6: Performance comparison on XA-L&RSI and Oxford-L&RSI. (Table 3 in the main paper)**\n\n_Recall@1_ and _Recall@10_ (%)↑ are reported. The best result is rendered in **bold**.\n\n| Methods| Test Set-S| Test Set-M| Test Set-L| Test Set-G| 11-14-02-26| 14-12-05-52|\n|:--|:--:|:--:|:--:|:--:|:--:|:--:|\n| GeoDTR| 8.68 / 25.27 | 6.25 / 19.36 | 4.74 / 14.95| 4.39 / 9.26  | 0.47 / 3.18  | 0.48 / 3.71 |\n| Sample4Geo| 28.63 / 53.92 | 25.39 / 49.75| 24.23 / 46.27 | 22.95 / 43.02 | 7.13 / 20.68  | 7.51 / 21.75 |\n| FRGeo| 22.26 / 49.88 | 19.72 / 42.45 | 17.16 / 38.05 | 15.54 / 34.45     | 1.62 / 7.34  | 1.58 / 7.86 |\n| L2RSI (w/o STPE)| 30.05 / 68.56 | 23.90 / 61.95 | 21.93 / 57.42  | 20.07 / 52.55 | 10.19 / 30.23 | 11.25 / 30.58    |\n| L2RSI (w. SuperGlobal)  | 30.39 / 57.54| 23.90 / 53.02 | 22.27 / 48.26     | 20.19 / 42.92  | 8.90 / 22.98  | 8.98 / 22.86     |\n| L2RSI (w. PF) | 71.66 / 86.88| 55.17 / 72.47| 50.87 / 72.47 | 47.85 / 58.65 | 21.40 / 31.43 | 16.83 / 29.46    |\n| L2RSI (w. STPE)| **88.93 / 92.13** | **87.95 / 91.64** | **85.49 / 90.65** | **83.27 / 87.82** | **42.29 / 59.41**| **43.77 / 62.76**|\n\n**Table 7 : Ablation study about effectiveness of components. (Table 4 in the main paper)**  \nResults include _Recall@1_ and _Recall@30_ (%)↑ on **Test Set-S** and **11-14-02-26** are reported. \n**Sem.**: Semantic segmentation. **STPE**: Spatial-Temporal Particle Estimation. **Orient.**: Orientation.  \nThe best results are in **bold**.\n\n| Sem. | STPE | Orient. | Test Set-S | 11-14-02-26|\n|:--:|:--:|:--:|:--:|:--:|\n|| ✔ | ✔| 50.43 / 67.90  | 8.13 / 25.42  |\n| ✔    || ✔       | 30.05 / 85.50  | 10.19 / 45.90 |\n| ✔    | ✔    || 54.37 / 76.14| 4.92 / 21.24|\n| ✔| ✔ | ✔ | **88.93 / 95.94** | **42.29 / 70.15**|\n\n**Table 8: Ablation study about the selection of the semantic encoder. (Table 1 in Appendix)**  \n_Recall@1_ and _Recall@10_ (%)↑ on XA-L&RSI and Oxford-L&RSI are reported.  \n† indicates that the input image size is 518×518. The best result is rendered in **bold**.\n\n| Backbones | Test Set-S| Test Set-M | Test Set-L| Test Set-G| 11-14-02-26|14-12-05-52|\n|:--|:--|:--|:--|:--|:--|:--|\n| VGG16 | 73.77 / 88.04 | 62.83 / 73.27      | 51.39 / 69.96      | 47.45 / 62.83      | 21.60 / 38.86      | 20.82 / 36.85   |\n| ResNet50   | 59.38 / 76.99  | 51.26 / 67.01  | 48.93 / 61.60  | 43.52 / 55.20   | 13.56 / 23.27   | 11.73 / 22.82 |\n| ResNet101 | 72.58 / 87.70   | 68.02 / 81.80   | 62.49 / 76.02  | 54.62 / 66.18 | 24.66 / 41.26  | 26.87 / 43.21  |\n| ConvNeXt-S | 83.75 / 88.24  | 77.73 / 81.11  | 74.40 / 78.03  | 67.15 / 73.67  | 30.30 / 51.05   | 31.91 / 49.60   |\n| DINOv2-S† | 84.72 / 90.98   | 78.32 / 87.34  | 65.41 / 78.32  | 68.12 / 77.09  | 1.40 / 3.76    | 0.86 / 3.21   |\n| MAE-B | **88.93 / 92.13**  | **87.95 / 91.64**  | **85.49 / 90.65**  | **83.27 / 87.82**  | **42.29 / 59.41**  | **43.77 / 62.76**  |\n\n**Table 9: Ablation study about the compatibility of STPE with other existing networks. (Table 2 in Appendix)**  \n_Recall@1_ and _Recall@10_ (%)↑ on XA-L&RSI and Oxford-L&RSI are reported. The best result is rendered in **bold**.\n\n| Methods | Test Set-S| Test Set-M| Test Set-L | Test Set-G| 11-14-02-26| 14-12-05-52|\n|:--|:--:|:--|:--|:--|:--|:--|\n| GeoDTR  | 47.52 / 67.81      | 39.40 / 55.26      | 32.41 / 45.06      | 11.33 / 17.85      | 1.97 / 5.46        | 1.11 / 5.67        |\n| Sample4Geo   | **83.12 / 89.24**  | **76.27 / 85.23**  | **70.36 / 80.43**  | **62.49 / 72.59**  | **32.24 / 48.33**  | **31.33 / 52.10**  |\n| FRGeo                 | 81.34 / 85.55      | 68.94 / 77.80      | 62.32 / 72.28      | 53.95 / 63.06      | 7.95 / 16.87       | 7.98 / 17.39       |\n\n# Q3: The impact of the motion model's failure.\n\nAs noted in Lines 205–209 of the main paper, the motion model for each time step} is directly obtained using FastGICP combined with magnetometer-based heading correction.\n\nWe acknowledge that dynamic objects (e.g., vehicles, pedestrians) can indeed introduce noise to motion estimation. To mitigate this, we deliberately avoided prolonged proximity to large moving vehicles during data collection. Additionally, we filtered out point clouds within a 3-meter radius of the data collection vehicle to reduce self-interference.\n\nEmpirically, the submaps constructed via FastGICP show high quality in visualizations, and the particle aggregation mechanism in STPE over multiple time steps helps to neutralize occasional noisy estimates. This robustness is also reflected in the strong performance of place recognition observed in our experimental results (**Table 6** and **Table 7**).\n\n# Q4: The XA-L\\&RSI dataset open source.\n\nThanks for acknowledging the value of our dataset. We fully agree with your view on the importance of open data, especially given the scarcity of datasets in this research area — a challenge we are also deeply aware of. Before submitting the paper, we only acquired a usage license (rather than an open license) for the high-resolution ($0.5m$) satellite imagery of Xiang'an District, Xiamen, which prevented us from releasing the dataset in the short term. This was clearly stated in the checklist.\n\nHowever, we are pleased to share that the licensing issues have recently been resolved. We now hold the appropriate rights to release the imagery, and we plan to open-source the XA-L\\&RSI dataset upon acceptance of the paper."}]

Author rebuttal:

Meta-review: This paper addresses the challenge of LiDAR-based place recognition. It introduces a new dataset, XA-L&RSI, which contains approximately 110,000 remote sensing submaps and 13,000 LiDAR point cloud submaps captured in urban scenes. It further proposes a method, L2RSI, for cross-view LiDAR-based place recognition.

The paper initially received mixed scores (2× borderline accept, 1× borderline reject, 1× reject). Reviewers highlighted several strengths:
- The motivation is clear, the task is important, and the dataset is valuable;
- The proposed method is simple, effective, and technically sound;
- The evaluation is thorough.

Concerns were raised about the method’s novelty, paper clarity, completeness of related work, and the dataset license. The authors provided a strong rebuttal that effectively addressed these issues, resulting in all positive final scores (2× borderline accept, 2× accept). The AC concurs with this recommendation.
Decision: Accept (poster)