GTR-Loc: Geospatial Text Regularization Assisted Outdoor LiDAR Localization
Authors: Shangshu Yu
Wen Li
Xiaotian Sun
Zhimin Yuan
Xin Wang
Sijie Wang
Rui She
Cheng Wang
Venue: NeurIPS 2025 poster ()
Keywords: LiDAR Localization
Geospatial Text Regularization
Scene Coordinate Regression
Primary area: applications
TL;DR: This paper proposes GTR-Loc, the first text-assisted LiDAR localization framework that integrates geospatial text regularization into an SCR network to reduce localization ambiguities.

Abstract: Prevailing scene coordinate regression methods for LiDAR localization suffer from localization ambiguities, as distinct locations can exhibit similar geometric signatures — a challenge that current geometry-based regression approaches have yet to solve. Recent vision–language models show that textual descriptions can enrich scene understanding, supplying potential localization cues missing from point cloud geometries. In this paper, we propose GTR-Loc, a novel text-assisted LiDAR localization framework that effectively generates and integrates geospatial text regularization to enhance localization accuracy. We propose two novel designs: a Geospatial Text Generator that produces discrete pose-aware text descriptions, and a LiDAR-Anchored Text Embedding Refinement module that dynamically constructs view-specific embeddings conditioned on current LiDAR features. The geospatial text embeddings act as regularization to effectively reduce localization ambiguities. Furthermore, we introduce a Modality Reduction Distillation strategy to transfer textual knowledge. It enables high-performance LiDAR-only localization during inference, without requiring runtime text generation. Extensive experiments on challenging large-scale outdoor datasets, including QEOxford, Oxford Radar RobotCar, and NCLT, demonstrate the effectiveness of GTR-Loc. Our method significantly outperforms state-of-the-art approaches, notably achieving a 9.64%/8.04% improvement in position/orientation accuracy on QEOxford. Our code is available at https://github.com/PSYZ1234/GTR-Loc.

GTR-Loc: Geospatial Text Regularization Assisted Outdoor LiDAR Localization
Shangshu Yu', Wen Li Xiaotian Sun?., Zhimin Yuan', Xin Wang'. Sijie Wang', Rui She Cheng Wang2
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
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
"School of Artificial Intelligence and Software Engineering, Nanyang Normal University, China $ Nanyang Technological University, Singapore Beihang University, China yushangshu @ cse neu.edu.cn cwang xmw.edu.cn
Abstract
Prevailing scene coordinate regression methods for LiDAR localization suffer from localization ambiguities, as distinct locations can exhibit similar geometric signatures challenge that current geometry-based regression approaches have yet to solve. Recent vision-language models show that textual descriptions can enrich scene understanding, supplying potential localization cues missing from point cloud geometries. In this paper, we propose GTR-Loc, novel text-assisted LiDAR localization framework that effectively generates and integrates geospatial text regularization to enhance localization accuracy. We propose two novel designs: Geospatial Text Generator that produces discrete pose-aware text descriptions, and LiDAR- Anchored Text Embedding Refinement module that dynamically constructs view-specific embeddings conditioned on current LiDAR features. The geospatial text embeddings act as regularization to effectively reduce localiza- tion ambiguities. Furthermore, we introduce a Modality Reduction Distillation strategy to transfer textual knowledge. It enables high-performance LiDAR localization during inference, without requiring runtime text generation. Extensive experiments on challenging large-scale outdoor datasets, including QEOxford, Ox- ford Radar RobotCar, and NCLT, demonstrate the effectiveness GTR-Loc. Our method significantly outperforms state-of -fate-of-the-art approaches, notably achieving a 9. 664%/8.049 improvement in position//orienation accuracy on QEOxford. Our code is available at httttss: htte::///ttttt com www./PSY2123//CTR--EEE
Introduction
Accurate and robust LiDAR localization, which estimates the position and orientation of LiDAR sensors, is fundamental autonomous vehicles and robotics. Traditional approaches [17
44 [ typically perform localization by matching a query point cloud to a pre-built 3D map. Although effective, these methods often incur high storage costs for 3D maps (52) and substantial communication overhead [22), limiting their wide applications in large-scale outdoor environments.
End-to-end regression models have recently propelled LiDAR localization forward, overcoming limitations of previous methods by enabling deep networks to learn scene-specific representations. These models mainly fall into categories based on different regression objectives: Absolute Pose
Conresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Ambigaonx Iocation
Query Iocation
36 Query point cloud (b)
36 Ambiguous point cloud
(4)
(c)
Figure I: Illustration of cross) and a spatially distinet ambiguous location (green cross), whereas panels (b) and (c) show the two sites' LiDAR point clouds, whose geometries appear nearly identical despite the distance.
ambiguities: panel (a) marks the true query location (red
Regression (APR) and Scene Coordinate Regression (SCR). APR models directly regress the 6-DoF pose from point clouds, offering fast inference via compact architectures, yet often compromise accuracy due to limited geometric exploitation. Differently, SCR methods [23] 45 [1] predict world scene coordinates for each point, and then solve for the pose using RANSAC. SCR enforces geometric consistency during training, usually leading to improved performance.
46 48
40
22
12]
However, current SCR methods suffer from critical challenge-localizztion ambiguity arising from scene similarity, as shown in Fig. [T] This ambiguity occurs because distinct locations large-scale outdoor environments often share highly similar local geometric structures. Consequently, methods (like SGLoc (23) and Lightl Loc (2T) relying only on local point cloud geometric features struggle to disambiguate visually similar, spatially distinct areas. This often results in erroneous coordinate predictions and subsequent localization failure. While LiSA [45] attempts to mitigate this ambiguity using semantic segmentation, geometrically similar regions often share similar semantic attributes, leaving the inherent problem fundamentally unsolved.
Recent advancements [1 13 in vision-language models highlight text's potent ability to enrich scene description and understanding. This insight motivates us to integrate textual cues that supply crucial scene localization information missing from common geometric data, thereby reducing localization ambiguities and improving performance. However, conventional text descriptions can be subjective, inconsistent across different times, or ambiguous for continuous observations [1 50) making text-enhanced localization particularly challenging.
This paper proposes GTR-Loc, a novel text-assisted localization method that enhances SCR by generating and refining geospatial text regularization, thereby effectively mitigating ambiguities in LiDAR localization. Specifically, we propose two novel designs: Geospatial Text Generator (GTG) and produce formatted text directly conveying discrete pose information. We partition geospatial positions and orientations into discrete districts and directions, then generate text based on the point cloud's current position and orientation. Unlike describing scene layout or the objects present, GTG directly constructs formatted text descriptions relevant to localization. Second, we propose LATER, module that dynamically produces view-specific text embeddings. We Iearn Transformer to leverage point cloud features to condition the instantiation of GTG, constructing refined LiDAR-tex representations specific to the immediate view. By incorporating visual diversity within each district and direction category, offers more effective regularization to reduce localization ambiguities. Finally, we introduce Modality Reduction Distillation (MRD) strategy to enable LiDAR-only localization at inference, maintaining both effectiveness and efficiency. It distills textual regularization via a feature distillation module coupled with distillation loss, transferring knowledge from LiDAR text SCR to pure LiDAR SCR. Our contributions can be summarized as follows:
Text Embedding Refinement (LATER). First, we propose GTG to
We propose GTR-Loc, a novel text-assisted LiDAR localization framework. GTR-Loc is the first work to effectively design and integrate geospatial text descriptions as regularization to improve LiDAR SCR, leading to promising localization performance.
We propose Geospatial Text Generator and a
Text Embedding Refinement module to dynamically create view-specific text descriptions focused on discrete pose information, providing significantly enhanced disambiguation capabilities for LiDAR localization.
We devise a Modality Reduction Distillation strategy to enable LiDAR-only localization during
inference. Extensive experiments QEOxford [23] Oxford (2] and NCLT (26) datasets demon-

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strate the great effectiivnness of GTR-Loc, particularly outperforming tfate-of-the-rn methods by 9.64% on QEOxford.
2
Related Work
LiDAR Localization. Traditional LiDAR localization approaches typically aim to establish cor-
respondences between the query point cloud and a pre-built 3D map. These approaches primarily achieve localization via map matching, such as PoinNNetVLAD [36]. SOE-Net [43] SC2-PC+++ [8].
and IDM-RPMNet 49). While potentially accurate, map-matching approaches suffer from expensive map storage, often at the terabyte to petabyte scale [52]. To address these limitations, regression-based
localization proposed, enabling inference without relying on pre-built 3D maps.
Absolute Pose Regression (APR). APR methods 15
32
37 33
34 6 directly regress the
6-DoF pose from the input view (e.g., image point cloud). Pioneering LiDAR APR works like PointLoc [41]. employing PointNet++ [29 with an MLP head, demonstrate the approach's feasibility but show limitations in complex scenes. Subsequent works enhance APR with new architectures and loss functions. Notable examples include PosePN [46] with universal encoding and memory-ave regression; HypLiLoe [40] introducing cross- moosllmodal fusion and regression; and FlasMix [2]
focusing
on training acceleration. In the realm of multi-frama LiDAR localization, STCLoc 48 enforces
spatio-temppral consistency across consecutive LiDAR scans, NIDALoc 47 draws inspiration from neurobiologically inspired mechanisms, and DiffLoc (22) refines poses through an iterative diffusion process. Nevertheless, APR's s reliance on global scene representations can hinder the effective geometric encoding and potentially limit localization accuracy.
Scene Coordinate Regression (SCR). SCR methods 4
39
3
38
25
learns to predict per-point first decouples localization distills semantic knowledge to
world coordinates, with RANSAC determining the final pose. SGL
2
into point correspondence regression and pose estimation. LiSA enhance SCR. LightLoc [21) learns large-scale outdoor localization within I hour. Although SCR methods usually achieve higher localization accuracy than APR, they suffer from ambiguities arising from similar scenes, resulting in unreliable localization. To overcome the limitations of current methods, we propose GoRRLoc, a novel text-assisted LiDAR SCR framework. By incorporating and distilling geospatial textual regularization, we can effectively reduce localization ambiguities.
45
Vingon-Language Models. Recent Large Language Models (LLMs), e.g., ChatGPT (27).
LLaMA [35]. and PaLM [9), have emerged as a promising way for understanding human languages. The success of LLMS has ignited interest in the Vision-Language (VL) research area. Foundational VL models like CLIP (3I, COCOOP [5T]. and VL-Mamba (30] employ large-scale pre-training to align visual and textual representations.
These models also enable remarkable progress in diverse downstream tasks [
Since language and text can offer high-level scene descriptions, recent works explore their potential to address challenges in SLAMM LP-SLAM [50] and TextSLAM 18] first enable place recognition based text labels within the SLAM system. Subsequently, Text2Pos [16), Text2Loc [42). and MNCL 24] propose using text to perform large-scale urban place recognition. Whereas existing approaches merely match text queries with images or point clouds, embed structured text inside the pose estimation pipeline, enabling direct localization. We generate view-specific, pose-aware text deseriptions regularization for enhanced localization. Furthermore, introduce novel distillation strategy, enabling high-performanu LiDAR-onI, localization during inference.
20 19
Method
3.1 Problem Formulation and Overview
Problem Formulation. The standard SCR framework for LiDAR- based localization aims to learn a mapping function. fe, parameterized by 8. Given an input LiDAR point cloud P = {p, E < )= where each p; is the point in the sensor's local frame, the objective is to predict the corresponding world scene coordinates Ö = (c E R | i= for each point é; such that Ö fa(P). Subsequently, the 6-DoF pose [t. v] (a translation vector / E R and rotation vector 4 E R²) can be estimated from the predicted point-to-per correspondences, typically using RANSAC-based algorithm. this paper, we formulate the SCR learning multimodal regression, incorporating LiDAR point clouds
3

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GTG LATER
MRD
FLATER
v
V
[District I, Wesl
Transformer g
Taro
Transformer
Text encoder
LiDAR-text Rogressor
World coordinate (4]
1
%
Leamable prompt
Pos.
Ori
Proj. layer
Fp
GTR-Loc
LiDAR coordinate (Pt) Point encuder
LiDAR-only Regressor
World coordinate (4)
Train and inference
Train only
Distillation loss
weights Element-wise sum
Figure 2: Overview of our method. GTR-Loc enhances LiDAR localization with text assistance to regularize SCR: Geospatial Text Generator (GTG) provides discrete pose-aware text descriptions ToTG. and LiDAR-Anchoree Text Embedding Refinement (LATER) module dynamically con- structs siew-specific text embeddings FL FLATER conditioned on point features Fp. A Transformer is employed to fuse multimodal features for LiDAR- text regression. Furthermore, a Modality Reduction Distillation (MRD) strategy enables LiDaR-only inference by distilling textural regularization.
with an additional input, geospatial text description T. Then, we learn a new mapping function Jp. parameterized by d, that uses both inputs perform point regression, depicted as C ( T).
This paper proposes GTR-Loc, novel aext-assisted LiDAR SCR framework, to address the challenge of ambiguities in LiDAR localization. Fig. 2 illlstrrts the network architecture of GTR-Loc. First, the point encoder extracts point cloud features Fp from the input LiDAR scan. Concurrently, create discrete pose-aware text descriptions for each view to aid localization. Specifically, we propose two novel designs: Geospatial Text Generator (GTG) and LiDAR-Anchored Text Embedding Refinement (LATER) module. The proposed GTG (Sec. 3.2) generates formatted text descriptions Taro that directly convey discrete position and orientation information, which is relevant to localization. Then, the proposed LATER (Sec. B.3) dynamically produces view-specific text embeddings FLATER by incorporating current Fp. regularizing the SCR network for reducing ambiguities. Finally, a Transformer & combines text embeddings FT with point cloud features Fp and feeds the result FLr to the LiDAR-text regressor. Furthermore, we propose Modality Reduction Distillation (Sec.(.4) to distill geospatial text regularization, enabling LiDAR-only localization during inference. To supervise the model training, we adopt standard LI losses (Sec. to constrain the predicted and ground truth points, The SCR framework mostly follows the architecture of LightLoe [21], which consists of multi-scale encoder and regressor. We encode text using the text encoder of pre-trained CLIP model B1I. Detailed descriptions are provided below.
Overview.
3.2 Geospatial Text Generator
Traditional Vision- Lasigun models |0 18 leveraging text for SLAM often focus on generating descriptions of surrounding environments, e.g., scene layout or prominent objects. For example, a description of the scene point cloud is illustrated Fig. (b). While intuitive, relying on such descriptive text for LiDAR localization may face significant challenges. When revisiting the same location, dynamic changes in the scene can result in inconsistent textual descriptions. This inconsis- tency can significantly impede robust localization. In addition, describing scene content provides only indirect cues for localization, falling short of the direct position and orientation information required for 6-DoF pose estimation. To overcome these limitations, propose a Geospatial Text Generator (GTG) to produce formatted pose-aware text descriptions that directly aid LiDAR localization.
Specifically, GTG generates concise descriptions grounded in predefined position and orientation partitions, as shown Fig- (c). We first divide the planar map into M distinet geographical districts to describe the LiDAR sensor's position. We define a mapping function M - (1. ..., M). which maps a planar position a E TR² of the input point cloud Concurrently, we discretize the compass directions into K directional bins to indicate the LiDAR
discrete district identifier = = M(x).

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LiDAR point cloud
Scene description
Geospatial text description
A car in the driving lane is moving. It is in the front left of the ego car and close bus.
District
KKW
North
District 2
North-Northest
www.
District 3
Northeast
District 99
North- Northwsst
* Current position
Current orientation
(a)
(b)
Figure Scene description comparison. (a) Input LiDAR point cloud. (b) A free-form scene description, which is often subjective and verbose. (c) Our structured geospatial text description provides discrete pose cues for localization by encoding a district ID and a quantized direction.
sensor's heading. The direction set 'D includes cardinal (e.g., North), intercardinal (e.g.. Northeast), and finer- grained bearings (c.g., North-Northeast) to capture more precise orientation. The orientation angle [0, 2x) of the input point cloud P is then mapped to discrete direction bin d via discretization function O, such that = O E D. Finally, an example of the ToTe template can be expressed as "District 99, West SouttSsstt
These discretely determined identifiers, district & and direction d, represent the current geospatial state. Compared to conventional scene layout or object-centric descriptions, the proposed GTG provides more stable and informative cues for localization. For example, the constructed text, e.g., "District 99, West-Southwest", yields a signal that is stable and repeatable whether the scene changes. Conversely, free-form descriptions of the scene are subject to change due to moving objects and construction. Hence, the discrete pose-aware text in standardized format offers clear advantages in aiding localization and solving ambiguities. The generated descriptions Tero are then input to the LATER module, which builds upon them construct refined textual representations crucial for reducing scene ambiguities and thereby improving LiDAR localization performance.
3.3 LiDAR- Text Embedding Refinement
While GTG provides standardized, pose-aware text descriptions, its static prompts usually fail to capture the visual diversity within each district and direction category. For instance, consecutive LiDAR scans within the same district or direction may differ geometrically due viewpoint shifts,
However, TaTG yields identical geospatial text for these LiDAR point clouds. This consequently obscures fine- une-grained differences, leading to descriptive ambiguities that can impede high-accuracy localization. To address this, we propose -inhReAnchored Text Embedding Refinement (LATER) module to refine TerG based on input point cloud features. Then, we can ensure that the refined text embeddings dynamically adapt to the unique characteristies of every input point cloud.
occlusions, or environmental variations [16]
42 24
To be specific, we learn Transformer decoder, parameterized by 3. to generate conditional text embeddings for each LiDAR point cloud. As shown in Fig. 2 the Transformer takes two primary ..., ...,
2n } and view-specific point cloud features Fp. The prompts are represented as learnable parameters using nn Parameter. The feature Fp directly extracted from the input point cloud. Within the Transformer, both self-attention and cross-attention mechanisms are utilized layer-wise to fuse these modalities. Self-attention stays inside on; cross- attention then connects v, (as queries) to Fp (as keys/values). Finally, the refined text embedding FLATER is constructed by combining the Transformer sb's output vn with WerG. depicted as:
sets of inputs: n learnable prompts {V1, U2,
{U U2 ... V = Trans (Q = {U1, U2 ... JnJ. J. K, V = =P).
(1)
FLATER {V1, ..., uni Wara),
where WorG is the word embedding of Toro-
The LiDAR anchoring mechanism refines the initial static prompt TeTG with visual evidence from the input point cloud, effectively yielding view-specific textual embeddings FLATER- It provides more discriminative representation to distinguish subtle variations even within the same district or direction category. FLATER not only aligns and adapts more closely to the input LiDAR scan, but
5

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also delivers critical pose information for localization. Previous VLMs bridge vision and language via methods like COCOOP's learnable prompts [5T]. BLIP's centrastive-fusionn learning [20], or Flamingo's & gated cross-attention for injecting visual tokens into frozen L.MMs []. However, they are designed for image understanding tasks such as image captioning or zero-shot classification, while LATER is specifically designed for LiDAR localization. Hence, it can effectively regularize SCR to
reduce localization ambiguities. FLATEE is then processed by a frozen CLIP text encoder to produce the text embedding I'T for SCR.
3.4 Modality Reduction Distillation
Although the proposed geospatial text is effective, its construction relies on ground truth 6-DoF poses. This dependency is impractical for inference, as the pose is the very thing our localization task aims to estimate. Meanwhile, the absence of text or the presence of inaccurate text can lead erroneous localization results. Hence, we introduce Modality Reduction Distillation (MRD) strategy designed to transfer the knowledge from LiDAR-text SCR to pure LiDAR SCR. This allows us to achieve hegh-performance localization with only LiDAR inputs at inference, while retaining regularization imposed by the geospatial text.
As shown in Fig. 2 the multimodal regressor (LiDAR-text) acts as the teacher, while the pure point cloud regressor (LiDAR-only) acts as the student. We introduce projection layer, a 3-layer MLP with identical hidden size, to transform point cloud features Fp into H'p The text embedding Fr and Fp are then integrated into a new Transformer decoder, parameterized by ç, to be aggregated for the subsequent regression. The fused LiDAR- features Für for SCR are depicted as:
Fir F/ + aT @.Transformerg
Fr, KV Ft).
Hence, the projection layer serves a dual role: it enables multimodal feature fusion for LiDAR-text SCR and, through skip connection with Fp, contributes to LiDAR-only SCR well. This allows for simultaneous feature enhancement and knowledge distillation. Finally, we define a distillation loss CD measure the discrepancy between the output the teacher and student, represented as:
N
LD
Σ ||a:
where è, and é, denote the predicted world coordinates from the LiDAR-only regressor and LiDAR- text regressor, respectively. MRD enables the student regressor to mimic the teacher's 's refined outputs. The student thus inherits the text-assisted disambiguation, learning to narrow the search space using only its point cloud input. Once training is complete, the student SCR delivers accurate LiDAR-only localization at inference-an no runtime text generation required.
no
3.5 Loss Functions
Our GTR-Loc architecture employs multi-head learning framework that jointly optimizes LiDAR- text SCR and LiDAR-only SCR. The main optimization objective is to learn dense point predictions for localization. Hence, the proposed model is supervised by two loss functions during training:
Lu Σ lc CLo = Σ 14 c/11.
(5)
aml
where & the ground trurh world coordinates. The overall loss function LSer weighted summation of LiT, LLo, and the distillation loss LD with balancing weight B, represented as:
LSer = 31T + CLo + 3gLp.
(6)
Experiments
4.1 Experiment Settings
Datasets. We evaluate GTR-Loc on three commonly used large-scale outdoor datasets: Oxford Radar RobotCar (Oxford) 22 QEOxford [23], and NCLT (26). Oxford is large-scale urban dataset

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Table 1: Quantitative results the QEOxford dataset. We report the position (m) and orientation
error ()). Mechanism (Mech.) types are denoted as follows: MA for Multi-frame APR; SA for Single-frame APR; and SS for Single-frant frame SCR. Test Frames (TFs) denote the number of point cloud frames used during testing. We highlight the best and second-best results.
Methods
Mech.
TFs
15-13-06-37
17-13-26-39
17-14-03-00
18-14-14-42
Avg. [m/"]
STCLoc 48
MA
3
5.14/1.27
6,12/1.21
5.32/1.08
4.76/1.19
5,34/1.19
NIDAL
47
MA
5
3.71/1.50
5.40/1440
3.94/1.30
4.08/1.30
1,28/1.388
DiffLoc
22
MA
2.03/1.04
1.78/0.79
2,05/0.83
1.56/0.83
1.86/0.87
PointLoc 41
SA
10.75/2.36
11.07/2.21
11.53/1.92
9.82/2.07
10.79/2.14
PosePN 46
SA
9.47/2.80
12.98/2.35
8.64/2.19
6.26/1.64
9.34/2.25
PosePN++
46
SA
4.54/1.83
6.44/1.78
4.89/1.55
4.64/1.61
5.13/1.6
PoseMinkLoc
SA
6.77/1.84
8.84/1.84
8,08/1.69
6.56/2.06
7.56/1.86
PoseSOE 46
SA
4.17/1.76
6,16/1.81
5.42/1.87
4.16/1.70
4,98/1.79
Hypl.il.oc
SA
5.03/1.46
4.31/1.43
3.61/1.11
2.61/1.09
3.89/1.27
FlashMix
SA
2.04/1.95
195/1883
2.44/2.18
2.81/2.14
2.31/2.03
SGLoc 23
SS
1.79/1.67
L81/1.76
133//159
1.19/1399
1.53/1.60
LiSA 45
SS
0.94/1.10
1.17/1.21
0.84/1.55
0.85/1.11
0,95/1.14
LightLoc
SS
0.82/1.12
0.85/1.07
0.81/1.11
0.82/1.16
0.83/1.12 .12
GTR-Loc SS
0.77/1.02
0.77/1.01
0.67/1.01
0.80/1.07
0.75/1.03
SGLoe
LSA
$0
Posttion Emoo (c)
(b)
Lightloe
GER-Loc
GRASS
Orientation Erior ["] (f)
(d)
(c)
Figure 4: Visual comparisons on QFOxford. (a) (b) (d) (e): predicted trajectories (red) overlaid on ground truth (black); a star marks the starting position, and the blue box highlights a challenging road segment. (c) (f): cumulative error distribution curves for position (top) and orientation (bottom).
collected using a Nissan LEAF platform equipped with dual Velodyne HDL-32E sensors, providing dense LiDAR scans across multiple 10km trajectories within 2km² city area. This dataset captures diverse variations in weather and traffic density. QEOxford is a quality- Oxford dataset, more suitable for localization evaluation. The original Oxford dataset is further refined to mitigate the inherent errors in raw GPS/INS measurements. NCLT is a campus-scale scale dataset collected by Segway robot with Velodyne HDL-32E, covering 5.5km of trajectories within a 0.45km² area. This dataset encompasses both complex outdoor and indoor environments.
version of the
Baselines and Evaluation Metrics. To evaluate localization performance, compare our approach against various LiDAR-based based SCR and APR methods. For SCR, we include SGLoc [23], LiSA [45] and LightLoc (21). the latter being the state-of-the-art (SOTA) method. For APR, evaluate against fingle-frame methods including PointLoc [4T) PosePN (46), PosePN++ 46), PoseSOE 46),
PoseMinkLos [46). HiLiLoc [4)). and FlashMix [2]. well as multi-frame methods such as
STCLoc 48 NIDALoc [7]. and DiffLoe (22). Following previous methods 22 21], we report the
position/orienntation error [m/2] for each trajectory and the overall average
all trajectories.
Implementation Details. In this paper, we employ LightLoc (1 as the SCR backbone and pre-trained CLIP (31 as the text encoder. Following LightLoe, we load the pre-trained encoder weights. We adopt the A optimizer with one-cycle learning rate schedule ranging from 5e to 5e 3. The model is trained for 25 epochs Oxford/QEOxford and 30 epochs NCLT. Input

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Table 2: Quantitative results the Oxford dataset.
The notations follow Tab.
Methods
Mech.
TFs
15-13-06-37
17-13-26-39
17-14-03-00
18-14-14-42
Avg. [m/"]
STCLoc 48
MA
3
6,93/1,48
7.55/1.23
7.44/1.24
6.13/1.15
7.01/1.28
NIDALoc 47
MA
5,45/1,40 44
7.63/1.56
6,68/1.22 26
4.80/1.18
6.14/1135
DiffLoc
2
MA
3.57/0.88
3.65/0.68
4.03/0.70
2.86/0.60
3.53/0.72
PointLoc
41
SA
12.42/2.26
13. 13.14/2.50
12,91/1.92
11.31/1.98
12,45/2. 17
PosePN 46
SA
14,32/3 06
16.97/2.49
13.48/2.60
9.14/1.78
13,48/2.48
PosePN++ 46
SA
9.59/1.92
10.66/1.92
9.01/1.51
8,44/1.71
9.43/1.77
PoseMinkLog
46
SA
11.20/2.66 6.
14.24/2.42
12.35/2.46
10.06/2.15
11.96/2.41
PnseSOF 46
SA
7.59/1.99 .94
10.39/2.08
9,21/2.12
7.27/1.87
8.62/2.00
HypLiLLoc
44
SA
6,88/1.09
6.79/1229
5.82/0.97
3.45/0.84
5. 5.74/1.05
FlashMix 12
SA
3.05/1.96
4.55/2.05
4,67/2.05
2.94/1.79
3. 3,80/1.96
SGLoc 23
SS
3.01/1.91
4.07/2.07
3.37/1.89
2.12/1.66
3.14/1.88
LiSA 45
SS
2.36/1.29
3,47/1.43
3.19/1.34
1.95/123
2.74/1.32
LightLoc 21
SS
2.33/1.21
3.19/1.34
3.11/1.224
2.05/1.20
2.67/1.25
GTR-Loc
SS
2.29/1.17
3.07/1.21
2.99/1.20
2.00/1.18
2.59/1.19
point clouds are voxel-downsampled with a voxel size of 0.25m on Oxford/QQEOOxorr and 0.3m on NCLT. The number of districts is 100, and the number of directions is 16. Eg. is set to 0.1. 31 and 32 in Eq. [6 are set to 1, 93 is set to 0.I. GTR-Loc is implemented in Py PyTochh [28
and MinkowskiEngine [0]] All experiments are conducted single NVIDIA RTX 4090 GPU.
4.2 Comparison With Statc-of-the-Art Methods
Results on Oxford. We first evaluate GTR-Loc on the challenging QEOxford dataset. As shown in Tab. [ GTR-Loc achieves SOTA accuracy among all single-frame SCR and APR methods, with mean position/orientation error 0.755//1.03* This performance significantly surpasses the previous leading SCR method, LightLoc (0.83m/1.. 1,2)),, by 9.64%/8.044 showcasing our effectiveness. Although our method processes only single frame, it still surpasses DiffLoc, the SOTA multi frame APR approach, by substantial margin in positional accuracy. The results shown in Tab. 2 further demonstrate the superiority of our method (2.59m/1.199) on the original Oxford dataset. This notable improvement highlights the effectiveness of integrating geospatial text regularization in resolving localization ambiguities, particularly in complex urban environments like Oxford.
To provide further insights into performance, we visualize representative predicted trajectories and cumulative error distribution curves on the QEOxford dataset. As illustrated Fig. 4 our estimated trajectory on sequence 17-14-03-00 closely follows the ground truth trajectory over the entire sequence. As marked by blue boxes, GTR-Loc maintains stable and accurate tracking while competitor trajectories display systematic offsets, highlighting its robustness. The cumulative error distribution curves offer a quantitative summary supporting these observations. The sharper rise of curves indicates consistently higher accuracy across most trajectory points. For example, over 85% GTR-Loc's position errors fall below m, compared to just 80% for LightLoc
Results on NCLT. The NCLT dataset involves long-term data collection in a diverse setting, span- ning both outdoor and indoor campus environments. As presented in Tab. 3 GTR-Loc achieves leading performance of 1..4m/22.62 ranking first and second position and orientation among single- frame SCR and APR methods. Our approach also delivers performance comparable to that of SOTA multi-frama APR methods, yet it operates using only a single frame during inference, thereby offering greater flexibility. This dataset presents unique difficulties like abrupt environmental transitions and varying structural complexity. GTR-Loc incorporates geospatial cues that remain informative across indoor and outdoor settings, resolving ambiguities in perceptually similar areas.
position accuracy. Our approach yields mean position/orientation errors
Runtime Analysis. We report the training (h)inference (ms) time in Tab. 4 The entire model training process lasted approximately 4 hours, with peak GPU memory consumption of around 10 GB. On Oxford/QEOxxool datasets, the average inference time per sample is 29ms (34 FPS), and on NCLT dataset, it is 48ms (21 FPS). These speeds are well within the respective scanning frequencies of 20 Hz. for Oxford/QFOOxford and 10 Hz for NCLT, highlighting the model's ability to maintain high accuracy during real-time operation.

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Table 3: Quantitative results the NCLT dataset.
The notations follow Tab.
Methods
Mech.
2012-02-12
2012-02-19
2012-03-31
2012-05-26
Avg. [m/"]
STCLac 48
MA
4.91/4.34
3.25/3.10
3.75/4.0M
7.53/4.95
4.86/4.11
NIDALoc
MA
4.48/3.59
3.14/2.52
3.67/3.46
6.32/4.67
4.40/3.56
DiffLoc
MA
0.99/2.40
0.92/2.14
0.98/2.27
1.36/2.48
1.06/.332
PointLoc
41
SA
7.23/4.88
6.31/3.89
6.71/4.32
9.55/5.21
7,45/4.58
PosePN 446
SA
9.45/7.47
6.15/5.05
5.79/5.28
12.32/7.42
8.43/6.31
PosePN++
SA
4.97/3.75
3.68/2.65
4.35/3.38
8.42/4.30
5,36/3.52
PoseMinkL
46
SA
6.24/5.03
4.87/3.94
4.23/4.03
9.32/6.11
6.17/4.78
PoseSOE 46
SA
13.09/8.05
6.16/4.51
5,24/4.56
13.27/7.88
9.44/6.24
HypLiLoc
40
SA
1.71/3.56
1.68/2669
1,52/2.90
2.29/3.34
1,80/3112
FlashMix
12
SA
2.59/4.27
1.54/3.26
1.42/3.65
4.96/5.80
2.63/4.25
SGL.oc 23
ss
1.20/3.08
1.20/3005
1.12/3.28
3.48/4.43
1.75/3.46
LiSA 45
SS
0.97/2.23
0.91/2.09
0,87/2.21
3,11/2.72
1,47/2.11
Lightt occ
21
SS
0.98/2.76
0.89/2.51
0.86/2.67
3.10/3.26
1,46/2.80
GTR-Loc
SS
0.95/2.53
0.82/2.45
0.82/2.52
3.01/2.99
140/2.62
4.3
Ablation Study
Effects Geospatial Text Generator. To comprehensively evaluate GTR-Loc, we first conduct ablation studies on the proposed Geospatial Text Generator (GTG). We report the average error for each dataset. As shown in Tab. removing all proposed modules degrades performance from 0.75m/1.03 to 0.83m/1.13 1.2 (the vanilla model's result). highlighting the importance of geospatial text regularization. Then, we conduct experiments using different types of text descriptions. Specifically, we replace our geospatial text with SOTA 3D scene description generator, TOD3Cap [33,, that produces text describing scene layout and objects present. An example of the generated text for scene is shown in Fig. (b). Demonstrating no significant gains compared to the vanilla model on different datasets, this variant also remains markedly inferior to our full model. Human-lin free-form scene descriptions are inherently unstable for localization, dynamic objects and environmental changes create inconsistent cues. In addition, they provide only indirect and noisy cues for pose estimation. Hence, the discrete pose-aware generated by GTG delivers more direct and effective localization cue than unconstrained scene descriptions, leading to better performance.
In addition, we conduct ablation studies by replacing the CLIP text encoder with embedding layers (ELs) to demonstrate the necessity using textual representations. Specifically, we learn two separate, learnable embedding layers (nn. Embedding) initialized from scratch: one for the 100 district IDs and another for the direction IDs. The resulting vectors are concatenated and passed through an MLP to match the feature dimension of the original CLIP embedding. The results shown in the table indicate that using simple Icarnable embeddings only leads to a small performance improvement. By processing geospatial text with CLIP, we can leverage pre-trained semantic priors, climinating the need to learn complex semantic and spatial relationships from scratch.
Effects of District z and Direction d. To investigate the impact of spatial partitioning granularity in GTG, we conduct an ablation study by varying the number of districts z and directions d. We train and evaluate GTR-Loc with different configurations, varying across values like 49, 100, 144, and d across values like 8, 16, 32, As shown Tab. localization accuracy improves as z increases from 49 to 100 and d from 8 to 16, indicating that finer geospatial discretization offers more discriminative contextual cues. However, further increasing z and yields negligible gains, as excessive granularity fails to provide additional benefits in resolving localization ambiguities. The chosen values of ==100 and d=16 this paper are appropriate for our method. This suggests that while sufficient granularity needed for disambiguation, excessive partitioning may hinder performance.
Our method's partitioning scheme both robust and scalable, making practical for real-world, large-scale deployment. Its robustness stems from using an abstract coordinate grid rather than semantic environmental features, ensuring stability against long-term environmental changes like construction or seasonal variations. Results the NCLT dataset, known for its data collection spanning several months, also demonstrate this. Furthermore, this design is also inherently scalable. The 10x 10 uniform grid is illustrative and can be seamlessly extended to ccty-scale applications using

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Table 4: Training (h)/Infernnce (ms) time.
Table 5; Ablation of GTG.
Method
QEOxford
Oxford
NCLT
Method
QBOxford
Oxford
NCLT
SGL.c
50/38
50/38
42/75
vanilla
0.83/1.12
2.67/1.25
1,46/2.80
LiSA
53/38
53/38
44/75
TOD3Cap
0.84/1.15
2.66/1.24
1,47/2.78
LightLoce
1/29
1/29
1/48
ELs 0.80/1.10
2.64/1.23
1.44/2.75
GTR-Loc
4/29
4/29
3/48
GTG
0.75/1.03
2.59/1.19
1400/.62
Table 6: Ablation of and
Table Ablation LATER.
Method
QEOxford
Oxford
NCLT
Method
QEOxford
Oxford
NCLT
2=49, d=8
0.79/1.09
2.62/1.21
1,43/2.67
w/o LATER
0.78/1.10
2.62/1.22
1,43/2.70
2= 100 d=16
0.75/1.03
2.59/1.19
1,40/2.62
Sum
0.76/1.06
2.62/1.21
1,41/2.66
2= 144, d=32
0.76/1 02
2.60/1.22
1.41/2.61
Concatenate
0.77/1.09
2.61/1.22
1.41/2.68
Transformer
0.75/1.03
2.59/1.19
1.40/2.62
Table 8: Ablation of MRD.
Method
QEOxford
Oxford
NCLT
MRD (w/o text) MRD (w text)
0.75/1.03
2.59/1.19
1.40/2.62
0.74/0.93
2.58/1.07
1.12/2.40
standard hierarchical grids (e.g.. UTM). Our ablation study confirms its robustness the number of partitions changes (e.g., from == 100 to z=49 or 2=144).
Text Embedding Refinement. To evaluate the contribution of dynami- cally refining text embeddings using visual context, we conduct an ablation study on the proposed LiDAR-Anchored Text Embedding Refinement (LATER) module. We compare four distinct con- figurations: (1) bascline using only the static prompt generated from GTG without LATER; (2) a simplified variant that replaces Transformer fusion with element-wise summation (3) another variant with feature concatenation fusion; and (4) our proposed LATER, which utilizes a Transformer to fuse text embeddings with point cloud features. The results reported in Tab. clearly favor proposed approach. Using only static prompts without LATER led to worse results, e.g., 0.78m/1. 10 QEOxford. The summation and feature concatenation fusion variant performs intermediatell. The Transformer-based LATER achieves the best performance. It allows creating highly discriminative, scene-specific text embeddings using visual context for accurate localization.
Effects of
Effects of Modality Reduction Distillation. We further evaluate the Modality Reduction Distillation (MRD) strategy in Tab. We present ablation studies with text inputs, generated by ground truth poses, at inference. Results indicate that using text during inference usually leads to better perfor- mance, e.g., 0.74m/0.93 QEOxford. However, generating text inference can be impractical and introduce unwanted dependencies, since depends on discrete pose estimates. The distilled model without text achieves performance nearly matching that of the text-assisted localization approach, e.g., 0.75m/1 .033 on QEOxford. This demonstrates that MRD distills geospatial text regularization from the teacher effectively, enabling high-performance LiDAR-only localization during inference. In the Appendix, we provide additional ablation studies and visualizations for the Oxford and NCLT datasets, along with discussion of limitations and future work.
Conclusion
This paper introduces GTR-Loc, the first text-assisted LiDAR localization framework that integrates geospatial text regularization into SCR network to reduce localization ambiguities. The proposed geospatial text regularization consists of two components: Geospatial Text Generator, which produces formatted, discrete pose-aware text descriptions, and -inhoeedchored Text Embedding Refinement module, which dynamically constructs view-specific embeddings conditioned on current point cloud features. Furthermore, we introduce Modality Reduction Distillation strategy to distill textual regularization knowledge, enabling pigh-peeformance LiDAR-only localization at inference time. Comprehensive experiments on challenging large-scale outdoor datasets, QEOxford, Oxford Radar Robotcar, and NCLT, demonstrate the effectiveness of GTR-Loc.
10

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Acknowledgments
This work was supported by the Fundamental Research Funds for the Central Universities under Grant N25XQD053. We would like to thank the anonymous reviewers for their valuable suggestions.
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Appendix
A More Experimental Results
More Dataset Details. Regarding the datasets, ground truth poses for the Oxford [ and QEOx- ford [3] datasets are obtained through interpolation from an integrated GPS/INS system. For the NCLT dataset, ground truth poses are generated post-collection using SLAM. Further details on the data splits can be found in Tab. and T
More Ablation Study, Importantly, our deployed model (a distilled, LiDAR-only only GTR-Loc) requires no text during inference and uses only a single LiDAR scan. However, we further investigate the case where text is used during inference. First, we train an auxiliary classification network to predict district and direction categories at test time. This network is designed with a LightLooc (2] encoder, followed by four fully-connected layers of identical feature dimensions, to separately predict partitions for position and orientation. The training configurations are consistent with those used for GTR-Loc. During inference, this classification network first predicts districts = and directions d. This prediction is then used to generate conditioned textual input for our primary localization network. The detailed classification accuracy of this auxiliary network presented in Tab. and Tab. 4 The results (row 2) indicate that this approach delivers virtually no performance gains, while it increases both test time and computational complexity owing to the additional classification network and the LiDAR-text regression. The limited classification accuracy in the specific dataset, i.e., NCLT, produces faulty text outputs, which in turn propagate errors into the localization results. We then test hypothetical scenario where ground truth poses are available for text generation at inference. This better result (row 3) establishes the theoretical upper bound for performance achievable with text. However, it impractical, as gt poses are unavailable during real-world inference. Therefore, using MRD distillation is a more viable solution.
localization results alongside the position/orientatio
Table I: Details of the Oxford dataset.
Table 2: Details of the NCLT dataset.
Sequence
Length (km)
Weather
Split
Sequence
Length (km)
Weather Split
11-14-02-26
9.37
sunny overcast
Train
2012-01-22
6.10
overcast
Train
14-12-05-52
9.22
Train
2012-02-02
6.20
sunny
Train
14-14-48-55
9.05
overcast
Train
2012-02-18
6.20
sunny
Train
18-15-20-12
9.04
overcast
Train
2012-05-11
6.00
sunny Train
15-13-06-37
8.85
overcast
Eval
2012-02-12
5.80
sunny
Eval
17-13-26-39
9.02
sunny
Eval
2012-02-19
6.20
overcast Eval
17-14-03-00
9.02
sunny overcast
Eval
2012-03-31
6.00
overcast Eval
18-14-14-42
9.04
Eval
2012-05-26
6.30
sunny Eval
Table 3:
Ablation of using text at inference.
Table 4:
Classification accuracy of posi- tion/oricntation on different datasets.
Method
QFOxford
Oxford
NCLT
Dataset
Accuracy
vanilla
0.83/1.12
2.67/1.25
1.46/2.80
w pred text
0.75/1.02
2.60/1.21
1.41/2.70
QEOxford 99. 1t..199997.644 6.6%
gt text
0.74/0.93
2.58/1.07
1.12/2.40
Oxford
98.99%
ours
0.75/1.03
2.59/1.19
1.40/2.62
NCLT
98.44%
More visualization. To further dissect the performance on the Oxford and NCLT datasets, Fig. and Fig. 2 provide trajectory visualizations and cumulative error distribution curves. The results of sequences 17-13-2-33 (Oxford) and 2012-02-19 (NCLT) are provided for comparison, respectively. GTR-Loc's estimated trajectory adheres closely to the ground truth throughout different sequences. Even within structurally complex repetitive areas (as marked by blue boxes), GTR-Loc maintains consistent localization. Methods like SGLoc or LiSA exhibit jumps in these areas. The cumulative error distribution curves also demonstrate GTR-Loc's 's leading performance across most error ranges. Our curves for both position and orientation errors lie predominantly above those of other methods, suggesting overall lower error magnitudes.
Localization Failure Cases. The model's failures are confined to rare, extreme scenarios where LiDAR data contain almost distinctive geometric features. For example, very long street in
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the Oxford dataset, the building facades, streetlights, and other structures are completely identical. In these few instances (<1%) % of the trajectory). the teacher model succeeds by leveraging geospatial text, an external cue that the student lacks. While these specific failures can produce large errors, their statistical impact becomes negligible when averaged over the entire dataset. In the vast majority of cases, the student effectively utilizes subtle geometric cues, achieving performance nearly identical to the teacher's.
B Limitations and Future Work
Limitation. Despite its promising results, GTR-Loc has limitations that highlight areas for future research. While our distillation approach effectively eliminates the need for text processing during inference, limitation that leveraging text directly at inference time demonstrably achieves better performance. Our experiments demonstrate that using ground-trure text, rather than erroneously predicted text, more beneficial for accurate localization.
Future Work. Consequently, our future work will concentrate on exploring methods for generating more accurate textual descriptions without using ground truth poses during the inference phase. The aim to significantly enhance LiDAR-text localization precision by effectively harnessing these improved textual cues the point of decision-makenn makinng.
PArcenTa www.Antaag [96)
$01.c
00
(b)
Lightkoc
OTR-Loc
(f)
Figure Visual comparisons Oxford. (a) (b) (d) (e): predicted trajectories (red) overlaid on ground truth (black); a star marks the starting position, and the blue box highlights a challenging road segment. (c) (f): cumulative error distribution curves for position (top) and orientation (bottom).
Position Eirer [m]
G134cc
Rercontage [N
Orientation
(8)
(0)
(1)
Figure 2: Visual comparisons on NCLT. The notations follow Fig.
References
[1] Dan Barnes, Matthew Gadd, Paul Murcutt, Paul Newman, and Ingmar Posner: The oxford radar robotcar dataset: radar extension the oxford robotcar dataset, In ICRA, pages 6433-6438. 2020.
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[2] Wen Li, Chen Liu, Shangshu Yu, Dunqiang Liu, Yin Zhou, Siqi Shen, Chenglu Wen, and Cheng Wang. Lightloe: Learning outdoor lidar localization light speed. In CVPR, 2025.
[3] Wen Li, Shangshu Yu, Cheng Wang, Guosheng Hu, Siqi Shen, and Chenglu Wen. Sgloc: Scene geometry encoding for outdoor lidar localization. In CVPR, pages 9286-9295, 2023.
[4] Carlevaris-Biac Bianco Nicholas, K. Ushani Arash, and M. Eustice Ryan. University of michigan north campus long-term vision and lidar dataset. IRRR 35:545-565, 2015.
23


Reviews:
[{"review_id": "yZhx8axEao", "reviewer": "Reviewer_kvSg", "summary": "This paper proposes GTR-Loc of the text-assisted localization method for Scene Coordinate Regression (SCR) in pointclouds obtained from LiDAR. Their GTR-Loc incorporates Geospatial Text Generator (GTG) to encode the geospatial text description, such as addresses: \"District 99, West-Southwest.\"  LiDAR-Anchored Text Embedding Refinement (LATER) module generates view-specific text embeddings.  The extracted textual representations are combined with ordinal pointcloud representation by point encoder. They also used Modality Reduction Distillation (MRD) to  enhance LiDAR-only inference from LiDAR-text inference. In experiments, they performed with three different datasets: Oxford, QEOxford and NCLT datasets. They confirmed the effectiveness of their GTG in the ablation study and achieved SoTA performance in QEOxford. Their research idea of injecting geospatial textual knowledge is overall interesting.", "questions": "- Q1. Can you clarify how the generated geospatial text description by GTG for each dataset is utilized in the inference? I assume that you use the distilled LiDAR-only inference module in the test set. If you use textual description in both the training and inference, please clarify how did you prepare geospatial textual description for test sets of those datasets. Also see W4(1) and (2).\n- Q2. How rich is the Geospatial text description? Is it merely the combination of positional and", "limitations": "Overall achievement looks good, although I am not sure if this methodology works in the real scenes with rich contexts considering the limited textual diversity.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\n- [S1] The research idea novelty: the generation of geospatial textual description is interesting and indeed the effectiveness of the proposed method is (at least partially) confirmed in the experiments.\n- [S2] The effectiveness of the GTG is confirmed by the ablation study, which supports the main contribution of the generating and utilizing geospatial textual description.\n- [S3] The SoTA performance in the QEOxford dataset.\n\n\nWeakness\n- [W1] The lack of the diversity in the geospatial textual descriptions: they seemed written following a very limited template. For example, although Fig.3 presents the “free-form scene description,” they used template-based Geospatial text description that is a combination of positional and orientational labels, not rich textual expressions.\n- [W2] As I explained in W1, GTG depends on some templates and I suspect that it is focused on the specific environments, e.g., those in the QEOxford dataset.\n- [W3] The analyses why the proposed model performed well on the QEOxford dataset while it performed on-per with their baselines in other two datasets are not well-discussed.\n- [W4] Writing issue: The authors can include further details of the proposed models, e.g., (1) how they prepare geospatial textual descriptions in the inference time if they are needed, (2) whether they use some additional information (texts) that are not used in the previous studies in the inference time or not.", "was_revised": true, "final_justification": "The use of the text is limited to the training, while it is used for the regularization in the inference: this result sounds a bit strange, although it doesn't deviate from the task setting nor fair comparisons with other studies. I do not strongly recommend the acceptance of the paper although I cannot find critical faults that prevent the acceptance of this paper.", "rebuttal": "Thank you for your insightful comments.\nWe have addressed each point and will integrate all corresponding revisions into the final version of the paper.\n\n**Response to W1\\&Q2.**\n(1) *Disadvantages of rich text for LiDAR localization.*\nHuman-like text generation, such as TOD3Cap in Figure 3, tends to describe target-environment-relationship, such as vehicles or vegetation. \nThese free-form scene descriptions are inherently unstable; dynamic objects and environmental changes lead to inconsistent characterizations of the same location over time, providing an unreliable description. \nFurthermore, these descriptions offer only indirect cues for 6-DoF pose estimation, with irrelevant details acting as noise that can impede learning. \nThis challenge is compounded by the sparse, geometric-only nature of LiDAR point clouds, which, unlike images, lack rich texture and color.\n\n(2) *Advantages of GTG for LiDAR localization.*\nIn contrast, our GTG produces text (e.g., \"District 99, West-Southwest\") that yields a standardized representation of universal pose information, creating a signal that is stable, repeatable, and directly relevant to pose estimation. \nThe text's invariance to dynamic content provides the consistency needed to resolve localization ambiguities via regularization. \nThen, the LATER module refines GTG-generated text by fusing it with the current point cloud's geometry, creating viewpoint-specific embeddings that enrich the final training feature with diverse visual-geometric information beyond the core pose.\n\n(3) *Experimental validation and proof.*\nIn Table 4, we demonstrate that GTG is more effective than free-form scene descriptions. \nWe also present this table here.\nWe replace GTG with a SOTA 3D scene captioning model, TOD3Cap, to generate rich text. \nThis free-form text mainly describes object motion, appearance, relationship, and environment. \nHowever, using free-form descriptions from TOD3Cap yields no significant performance gain over the vanilla model. \nOur template-based GTG leads to significant improvements.\n\n|Method|QEOxford|Oxford|NCLT|\n|-|-|-|-|\n|vanilla|0.83/1.12|2.67/1.25|1.46/2.80|\n|TOD3Cap|0.84/1.15|2.66/1.24|1.47/2.78|\n|GTR-Loc|0.75/1.03|2.59/1.19|1.40/2.62|\n\n**Response to W2.**\n*The proposed GTG does not focus on specific environments.*\nFirst, as discussed in the previous response, the GTG is not tied to any specific environmental features like roads or buildings. \nIt partitions any given map into discrete districts and directions, which is a universal geospatial cue that can be applied to any environment that can be mapped. \nSecond, the proposed GTG and the entire text modality are used exclusively during training and are completely absent during inference.\nWe propose MRD distillation to conduct LiDAR-only localization at inference, without requiring text inputs.\nA localization model that does not use text at inference avoids overfitting to the templates of any specific environment.\n\n**Response to W3.**\n*Performance analysis.*\nWe would like to respectfully clarify that GTR-Loc achieves superior performance on all three datasets.\nOur method outperforms all baselines on QEOxford, achieves SOTA position accuracy on Oxford and NCLT, and ranks second in orientation estimation.\nSpecifically, on QEOxford, GTR-Loc (0.75m/1.03°) achieves a significant 9.64\\%/8.04\\% improvement in both position and orientation over the previous SOTA, LightLoc (0.83m/1.12°).\nGTR-Loc consistently outperforms LightLoc on both the Oxford (2.59m/1.19° vs. 2.67m/1.25°) and NCLT (1.40m/2.62° vs. 1.46m/2.80°) datasets.\nThe absolute improvement in position accuracy over previous leading SCR methods, LiSA and LightLoc, is significant.\n\nThe primary goal of GTR-Loc is to solve localization ambiguity, where a model incorrectly identifies its location due to geometrically similar scenes. \nThe Oxford dataset, including QEOxford (a version with enhanced ground-truth), features long, repetitive urban roads with highly similar geometric structures, which creates significant ambiguities. \nHence, the margin of improvement is most pronounced on QEOxford and still significant on Oxford.\nThe NCLT dataset, gathered via a Segway, is less stable than data from vehicle platforms, making localization more challenging. \nNevertheless, our superior performance against the SOTA method, LightLoc, validates the effectiveness of using geospatial text regularization.\n\n**Response to W4\\&Q1.**\n(1) *If inference with text.*\nIt is crucial to distinguish our final model—a distilled, LiDAR-only regressor (red flow shown in Figure 2) requiring only a single LiDAR scan for a fair comparison—requires no text at inference.\nTo address the reviewer's concern and strengthen our results, we have conducted additional experiments using GTR-Loc with text during inference.\nSpecifically, we first train an auxiliary classification network to predict district and direction categories from LiDAR point clouds.\nWe set this network's architecture as a LightLoc encoder followed by four FC layers.\nDuring inference, the pre-trained classification network predicts the district and direction ID for text generation.\nThis generated text, along with the original point cloud, is then fed into the LiDAR-text Regressor (blue flow shown in Figure 2) to perform prediction. \n\n|Method|QEOxford|Oxford|NCLT|\n|-|-|-|-|\n|vanilla|0.83/1.12|2.67/1.25|1.46/2.80|\n|GTR-Loc w text at inference|0.75/1.02|2.60/1.21|1.41/2.70|\n|GTR-Loc|0.75/1.03|2.59/1.19|1.40/2.62|\n\nThe results indicate that this approach is not effective.\nIt delivers virtually no performance gains (the table above) while increasing both test time and computational complexity due to the additional classification network and multimodal regression. \nInaccurate classification (the table below) leads to wrong text generation, thus resulting in inaccurate localization.\nTherefore, it further demonstrates that transferring knowledge via MRD into a LiDAR-only model is more efficient and effective.\nThese additional experiments have also been reported in Tables 3 and 4 of the appendix. \n\n|Dataset|Accuracy|\n|-|-|\n|QEOxford|99.19\\%/97.64\\%|\n|Oxford|98.99\\%/97.52\\%|\n|NCLT|98.44\\%/85.50\\%|\n\n(2) *Additional information for inference.*\nThere is no additional text information used for GTR-Loc during inference.\nOur model's input is identical to that of previous SCR methods like LightLoc—only the LiDAR point cloud. \nThe proposed MRD distillation allows LIDAR-only inference. \nOnce training is complete, this model has no access to GTG, text, or pose information."}, {"review_id": "6Hu8g3EmnQ", "reviewer": "Reviewer_ispn", "summary": "This paper introduces GTR-Loc, a text-assisted LiDAR localization framework that enhances scene coordinate regression (SCR) by integrating geospatial text regularization. The key contribution is the novel use of discrete pose-aware textual descriptions to mitigate localization ambiguities in large-scale outdoor environments. This design augments geometric data with structured text, providing additional spatial cues. To handle intra-region variability, the authors propose a LiDAR-Anchored Text Embedding Refinement (LATER) module that adapts text embeddings based on local point cloud features. The model is trained using a dual-regression architecture with a Modality Reduction Distillation (MRD) strategy to enable efficient LiDAR-only inference. The authors conduct experiments showing that the proposed method consistently improves localization robustness. Furthermore, the paper presents extensive ablation analyses, demonstrating that the proposed components contribute meaningfully to resolving ambiguity in visually similar scenes.", "questions": "The paper is well-executed, and the results are valuable. My questions focus on clarifying some details in experiments.\n\nVision-language fusion. The LATER module uses a Transformer to refine text embeddings with point-cloud features, yet its conceptual relation to existing prompt-tuning or cross-attention paradigms (e.g., CoOp, BLIP, Flamingo) is not discussed. Please clarify (i) which design choices are novel versus inherited, and (ii) why LATER is preferable to directly adopting those prior fusion schemes.\n\nResult variability. The experimental results are promising, but all reported numbers appear to be single-run averages. Given that some gains (e.g., over LightLoc) are modest, can the authors report variance estimates or confidence intervals across multiple random seeds or splits, especially for QEOxford and NCLT?\n\nAdaptivity of discretization. The use of fixed spatial partitioning (z = 100 districts, d = 16 directions) is central to GTG, yet Table 5 suggests diminishing returns beyond certain granularity. Have the authors considered data-driven or learnable discretization schemes? Additionally, are there qualitative failure cases that arise from coarse or misaligned partitions?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\nStructured text regularization. The method leverages discrete, pose-aware textual descriptions as a form of geospatial regularization to complement geometric features in scene coordinate regression. This formulation offers an alternative pathway to reduce localization ambiguities, and represents a reasonable extension of multimodal integration in the context of LiDAR-based localization.\n\nModular and generalizable design. The proposed framework is composed of well-defined modules—namely, the Geospatial Text Generator, LATER refinement module, and the Modality Reduction Distillation strategy—which collectively form a coherent and extensible pipeline. This modularity not only aids interpretability but also facilitates adaptation to related multimodal perception tasks beyond localization.\n\nStrong empirical performance. The paper presents comprehensive experimental results showing consistent improvements over a range of existing LiDAR localization baselines across multiple benchmarks. Compared to prior work on the same task, the proposed method demonstrates stronger overall performance, supported by clear quantitative evidence. This level of empirical validation reinforces the practical effectiveness of the approach and lends credibility to its contributions.\n\n### Weaknesses\n\nAssumptions on Spatial Discretization. The proposed framework relies on predefined offline discretization of spatial regions and orientation bins, which, while effective under controlled settings, may pose challenges in dynamic or evolving environments. The paper does not explicitly address how the method generalizes under long-term environmental changes or large-scale deployment scenarios, potentially limiting its applicability in real-world conditions where map partitioning is not fixed or stable.\n\nLimited Comparison in Multimodal Design. While the LATER module presents a promising integration of point cloud features and geospatial text, its relation to prior multimodal fusion paradigms—such as prompt-based vision-language models (e.g., CoOp, BLIP)—is not thoroughly explored. This lack of comparative positioning slightly weakens the clarity around the novelty boundary of the method and may leave readers uncertain about its conceptual advances relative to existing multimodal architectures.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful comments.\nWe have addressed each point and will integrate all corresponding revisions into the final version of the paper.\n\n**Response to W1 Assumptions on Spatial Discretization.**\n(1) *Long-term environmental changes.*\nAlthough our method relies on a predefined map partition, our partitioning scheme is designed to be robust to environmental changes. \nThe partition is not based on semantic features of the environment (like roads or buildings), but on an abstract, universal coordinate grid. \nHence, with long-term environmental changes, such as construction and demolition, seasonal and vegetation changes, the partition is still stable.\nResults on the NCLT dataset, known for its data collection spanning several months, also highlight our method's robustness to long-term variations.\n\n(2) *Large-scale deployment.*\nOur design is also methodologically scalable for large-scale deployment. \nWhile we use a 10x10 uniform grid in this paper, it is not a limitation of the method. \nFor larger deployments (e.g., an entire city), the strategy can be seamlessly extended. \nFor example, we can adopt standard practices from geospatial applications, such as a hierarchical UTM grid, to programmatically partition an area of any size.\nIn addition, our ablation study (Table 5) also demonstrates the robustness of our approach (e.g., increasing from 100 to 144 districts).\nThis is critical for scalability, as a reasonable, predefined partition is sufficient, making the framework practical for real-world use.\n\n**Response to W2\\&Q1 Vision-language fusion.**\n(1) *Novelty of LATER.*\nThe novelty of our approach stems from both its design purpose and specific architectural details.\nPrevious VLMs bridge vision and language via methods like CoOp's learnable prompts, BLIP's contrastive-fusion learning, or Flamingo's gated cross-attention for injecting visual tokens into frozen LLMs.\nThey are designed for image understanding tasks such as image captioning or zero-shot classification, while our task is LiDAR localization.\nLATER is specifically designed to dynamically refine a pose-aware text embedding based on the unique geometric features of the current LiDAR scan, thus regularizing localization.\n\nIn addition, our novel module details are also different from previous VLMs.\nFollowing the well-established paradigm set by VLMs, LATER also utilizes a Transformer architecture and learnable prompts.\nHowever, LATER does not simply fuse the point cloud and text; instead, it independently trains a Transformer to dynamically refine learnable prompts ${v_n}$ using point features $F_p$. \nSubsequently, it concatenates these visually-tuned prompts $v'_n$ with a static text embedding that contains coarse pose information from our custom generator. \n\n(2) *Advantages of LATER.*\nOur LATER is preferable because it is a tailored solution to solve view-dependent ambiguities in LiDAR localization.\nDirectly adopting a fusion module from models like CoOP or Flamingo would be suboptimal for our task.\nThey are designed to fuse dense images with rich, descriptive texts, aiming to maximize the semantic understanding of scene contents.\nHowever, our inputs are sparse LiDAR point clouds and structured texts, and we aim to regularize localization.\nWe also conduct a new ablation study by replacing LATER with CoOP and Flamingo.\nWe retain the original fusion configuration, replacing only the fusion module itself.\nThe results shown in the table below indicate that with these fusion mechanisms, we can not achieve optimal results. \nThis proves that the unique dynamic refinement mechanism of LATER is both effective and necessary.\n\n|Method|QEOxford|Oxford|NCLT|\n|-|-|-|-|\n|vanilla|0.83/1.12|2.67/1.25|1.46/2.80|\n|CoOP|0.80/1.10|2.62/1.22|1.44/2.70|\n|Flamingo|0.82/1.11|2.65/1.25|1.47/2.79|\n|LATER|0.75/1.03|2.59/1.19|1.40/2.62|\n\n**Response to Q2 Result variability.**\nIn our initial submission, we follow the common practice within the LiDAR localization field, where most prior works report mean errors from a single, deterministic run. \nThis ensures that our results are directly and fairly comparable to the existing SOTA.\nTo further strengthen our conclusions, we have conducted additional experiments to report the variance and confidence interval. \nWe re-run our method (GTR-Loc) and the strongest baseline (LightLoc) for 5 iterations using different random seeds on the QEOxford and NCLT datasets. \nThe results (mean ± standard deviation) of position/orientation errors are presented in the table below.\n\n|Method|QEOxford|NCLT|\n|-|-|-|\n|LightLoc|0.83 ± 0.012/1.12 ± 0.016|1.46 ± 0.016/2.80 ± 0.018|\n|GTR-Loc|0.75 ± 0.010/1.03 ± 0.014|1.40 ± 0.012/2.62 ± 0.014|\n\nAs shown in the table above, the standard deviations across multiple runs are very small, indicating that the performance of both methods is highly stable.\nAs shown in the table below, GTR-Loc's variance (Var) of position error and orientation error is consistently and robustly superior to that of LightLoc across all 5 runs. \nThe 95\\% confidence intervals (CI) of our method's performance do not overlap with the baseline's, confirming that the reported improvements are statistically significant and not an artifact of random chance.\n\n|QEOxford|Var. (Pos. Err.)|Var. (Ori. Err.)|Pos. Err. CI (95\\%)|Ori. Err. CI (95\\%)|\n|-|-|-|-|-|\n|LightLoc|0.000144|0.000256|0.83 ± 0.0149|1.12 ± 0.0199|\n|GTR-Loc|0.000100|0.000196|0.75 ± 0.0124|1.03 ± 0.0174|\n\n|NCLT|Var. (Pos. Err.)|Var. (Ori. Err.)|Pos. Err. CI (95\\%)|Ori. Err. CI (95\\%)|\n|-|-|-|-|-|\n|LightLoc|0.000256|0.000324|1.46 ± 0.0199|2.80 ± 0.0224|\n|GTR-Loc|0.000144|0.000196|1.40 ± 0.0149|2.62 ± 0.0174|\n\n**Response to Q3 Adaptivity of discretization.**\n(1) *Learnable discretization schemes.*\nIn our current work, we use a fixed, uniform grid partitioning primarily for its interpretability and efficiency. \nA uniform grid is a simple, structure-agnostic approach.\nTo explore the potential benefits of adaptive partitioning, we conduct new ablation studies with a learnable strategy.\nSpecifically, we use learnable parameters, nn.Parameter, to learn districts z and directions d, with initial z=100 and d=16.\nThe results are shown below, where the learnable discretization scheme is not better than ours.\nWe think the reason is that a simple learnable scheme may introduce optimization challenges, as this important parameter is difficult to learn.\n\n|Method|QEOxford|Oxford|NCLT|\n|-|-|-|-|\n|Learnable discretization|0.78/1.08|2.64/1.22|1.43/2.72|\n|GTR-Loc|0.75/1.03|2.59/1.19|1.40/2.62|\n\n(2) *Failure cases.*\nFirst, coarse districts containing geometrically similar but distinct locations (e.g., parallel streets) may receive an identical text signal that fails to provide disambiguation. \nIn such cases, performance may revert to the baseline, though this text is benign rather than actively harmful. \nSecond, partition boundaries cutting across long roads can cause textual labels to hop during training. \nHowever, this issue is mitigated by our LATER module, which leverages real-time visual input, and is further smoothed by distillation, ensuring minimal impact on the final inference model."}, {"review_id": "4eT1EKs3lu", "reviewer": "Reviewer_zga8", "summary": "This paper introduces GTR-Loc, a text-assisted LiDAR localization framework that addresses the critical problem of localization ambiguities in outdoor environments. The method enhances Scene Coordinate Regression (SCR) by integrating geospatial text descriptions to distinguish between geometrically similar but spatially distinct locations. The framework consists of three main components: a Geospatial Text Generator (GTG) that creates discrete pose-aware text descriptions, a LiDAR-Anchored Text Embedding Refinement (LATER) module that produces view-specific embeddings, and a Modality Reduction Distillation (MRD) strategy that enables LiDAR-only inference.", "questions": "1. The discrete geospatial text, while robust, may be overly simplistic. The fixed district/direction partitioning might not capture all relevant localization cues, particularly in environments with rich semantic content that could aid disambiguation.\n\n2. One of the important factor in LiDAR Localization is efficiency. The reviewer did not find any comparison to previous methods in main paper and Appendix. It seems like there are more complicated modules involving self-/cross-attention that might make the method less efficient. It will be better to provide some quantitative results on this (for example, processing fps during evaluation).\n\n3. The benefit of text comes from the distillation loss. In Table 7, the results show that with distillation, the performance is similar to the performance using text in testing. This leads to two questions: a) using text is actually using an approximate ground truth location. But seems like the performance improvement against previous methods is not that significant. Does it indicate that progress can be made in the way to integrate text? b) The reviewer is wondering how the performance is to simply cut the MRD module and train the network for longer time because seems like the information provided in text is also provided in ground truth localization labels used for training. The LATER module makes it explicit and utilize text embedding to help training. This **might** only accelerate training process. Thus the reviewer hope to see more results to support the claims.", "limitations": "Please see Questions part and Weakness part.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "## Strengths\n1. Novel Problem Formulation and Solution\nThe paper identifies a fundamental limitation in current LiDAR localization methods - the inability to distinguish between geometrically similar locations. The proposed text-assisted approach is well-motivated.\n2. Extensive experiments.\n3. The authors provide codes in the supplementary materials.\n\n## Weakness\n1. Limited Scope of Text Information.\nThe discrete geospatial text, while robust, may be overly simplistic. The fixed district/direction partitioning might not capture all relevant localization cues, particularly in environments with rich semantic content that could aid disambiguation.\n2. Lack of Efficiency Comparison. One of the important factor in LiDAR Localization is efficiency. The reviewer did not find any comparison to previous methods in main paper and Appendix. It seems like there are more complicated modules involving self-/cross-attention that might make the method less efficient. It will be better to provide some quantitative results on this (for example, processing fps during evaluation).\n3. The benefit of text comes from the distillation loss. In Table 7, the results show that with distillation, the performance is similar to the performance using text in testing. This leads to two questions: a) using text is actually using an approximate ground truth location. But seems like the performance improvement against previous methods is not that significant. Does it indicate that progress can be made in the way to integrate text? b) The reviewer is wondering how the performance is to simply cut the MRD module and train the network for longer time because seems like the information provided in text is also provided in ground truth localization labels used for training. The LATER module makes it explicit and utilize text embedding to help training. This **might** only accelerate training process. Thus the reviewer hope to see more results to support the claims.", "was_revised": true, "final_justification": "During rebuttal, the authors well resolved the reviewer's concerns about (1) the text descriptions. (2) computational cost. (3) convergence. (4) understanding of the role that text descriptions play in the pipeline.\n\nThe reviewer raise the score to 5.", "rebuttal": "Thank you for your insightful comments.\nWe have addressed each point and will integrate all corresponding revisions into the final version of the paper.\n\n**Response to W1\\&Q1.**\n(1) *Inconsistent and indirect descriptive texts.*\nHuman-like text generation, such as TOD3Cap in Figure 3, tends to describe target-environment-relationship, such as vehicles or vegetation. \nThese free-form scene descriptions are inherently unstable; dynamic objects and environmental changes lead to inconsistent characterizations of the same location over time, providing an unreliable description. \nFurthermore, these descriptions offer only indirect cues for 6-DoF pose estimation, with irrelevant details acting as noise that can impede learning. \nThis challenge is compounded by the sparse, geometric-only nature of LiDAR point clouds, which, unlike images, lack rich texture and color.\n\n(2) *Stable and direct GTG.*\nIn contrast, our GTG produces text (e.g., \"District 99, West-Southwest\") that yields a standardized representation of universal pose information, creating a signal that is stable, repeatable, and directly relevant to pose estimation. \nThe text's invariance to dynamic content provides the consistency needed to resolve localization ambiguities via regularization. \nThen, the LATER module refines GTG-generated text by fusing it with the current point cloud's geometry, creating viewpoint-specific embeddings that enrich the final training feature with diverse visual-geometric information beyond the core pose.\n\n(3) *Experimental validation and proof.*\nIn Table 4, we demonstrate that GTG is more effective than free-form scene descriptions. \nWe also present this table here.\nWe replace GTG with a SOTA 3D scene captioning model, TOD3Cap, to generate rich text. \nThis free-form text mainly describes object motion, appearance, relationship, and environment. \nHowever, using free-form descriptions from TOD3Cap yields no significant performance gain over the vanilla model. \nOur template-based GTG leads to significant improvements.\n\n|Method|QEOxford|Oxford|NCLT|\n|-|-|-|-|\n|vanilla|0.83/1.12|2.67/1.25|1.46/2.80|\n|TOD3Cap|0.84/1.15|2.66/1.24|1.47/2.78|\n|GTR-Loc|0.75/1.03|2.59/1.19|1.40/2.62|\n\n**Response to W2\\&Q2 Time evaluation.**\nOur method is real-time and highly efficient during inference.\nOn the Oxford/QEOxford datasets, the average processing time per sample is 29ms (34 FPS), and on the NCLT dataset, it is 48ms (21 FPS). \nThese speeds are well within the respective scanning frequencies of 20 Hz for Oxford/QEOxford and 10 Hz for NCLT, highlighting the model’s ability to maintain high accuracy during real-time operation.\nWe also report the runtime comparison with SCR baselines, as shown in the table below.\nOur runtime outperforms SGLoc and LiSA, while being on par with LightLoc. \nHowever, our superior performance demonstrates the advantage of GTR-Loc in both accuracy and efficiency.\n\nOur model's complex self/cross-attention modules are confined to the teacher network (blue flow, Fig. 2), which is used only during offline training. \nThis design ensures that our final inference model, obtained via MRD distillation, is a lightweight, LiDAR-only network (red flow, Fig. 2) with efficiency comparable to baselines like LightLoc. \nTo better highlight this, we will move the detailed time analysis from the appendix into the main text.\n\n|Method|QEOxford|Oxford|NCLT|\n|-|-|-|-|\n|SGLoc|38ms|38ms|75ms|\n|LiSA|38ms|38ms|75ms|\n|LightLoc|29ms|29ms|48ms|\n|GTR-Loc|29ms|29ms|48ms|\n\n**Response to W3\\&Q3.**\n(1) *Is text integration the bottleneck?*\nFirst, considering our text input as an \"approximate ground truth location\" may be a misunderstanding. \nTaking the QEOxford dataset as an example, its area is approximately 1000m x 1500m, which we divide into a 10x10 grid. \nThis means each geospatial district is about 100m x 150m in size. \nFor a localization task requiring meter- or even sub-meter-level accuracy, a regional label spanning hundreds of meters is far from an \"approximate ground truth.\" \nIn addition, our framework is SCR, which predicts world coordinates for each input point.\nThe ground truth label is coordinate labels rather than pose labels.\n\nSecond, we would like to respectfully clarify that GTR-Loc achieves superior performance on all three datasets. \nOur method outperforms all baselines on QEOxford, achieves SOTA position accuracy on Oxford and NCLT, and ranks second in orientation estimation. \nSpecifically, on QEOxford, GTR-Loc (0.75m/1.03°) achieves a significant 9.64%/8.04% improvement in both position and orientation over the previous SOTA, LightLoc (0.83m/1.12°). \nGTR-Loc consistently outperforms LightLoc on both the Oxford (2.59m/1.19° vs. 2.67m/1.25°) and NCLT (1.40m/2.62° vs. 1.46m/2.80°) datasets. \nThe absolute improvement in position accuracy over previous leading SCR methods, LiSA and LightLoc, is significant.\nThe results in Table 7 mainly demonstrate the effectiveness of our MRD distillation, with the LiDAR-only model achieving performance nearly on par with the LiDAR-text version.\n\n(2) *Does text regularization merely accelerate training?*\nOur method does not simply accelerate training; it fundamentally alters the learning process. \nTraditional SCR methods, using per-point loss, can get trapped in local minima when encountering geometrically similar scenes. \nTo address this, our method introduces text regularization as a unified, global constraint. \nFor instance, the text \"District 50, North\" mandates that all predicted coordinates are macroscopically consistent with this region, creating a more favorable optimization landscape when combined with the original per-point loss. \nTherefore, this text guidance is not merely a training accelerator but a fundamental tool for resolving localization ambiguities.\n\n|Method|QEOxford|Oxford|NCLT|\n|-|-|-|-|\n|w/o MRD (normal epoch)|0.83/1.12|2.67/1.25|1.46/2.80|\n|w/o MRD (3x epoch)|0.84/1.11|2.66/1.24|1.46/2.81|\n|w MRD (normal epoch)|0.75/1.03|2.59/1.19|1.40/2.62|\n|w MRD (3x epoch)|0.75/1.02|2.60/1.18|1.40/2.61|\n\nWe conduct new ablation studies by taking the baseline model (the LiDAR-only network architecture without a teacher and MRD) and extending its training time by 3x (from 25 to 75 epochs on QEOxford/Oxford and 30 to 90 epochs on NCLT). \nAs shown in the table, simply training for longer yields nearly no performance gain without MRD.\nIt comes nowhere close to matching the results of GTR-Loc w MRD (normal epoch) under its standard training schedule. \nIn addition, training GTR-Loc for a longer time also will not improve performance because the network has already converged.\nThis provides strong evidence that our method reshapes the learning process, rather than merely accelerating it."}, {"review_id": "ZvtvmVP4H3", "reviewer": "Reviewer_YNwr", "summary": "This paper introduces GTR-Loc, a method for outdoor LiDAR localization designed to address the problem of geometric ambiguity in scene coordinate regression methods. The core idea is to regularize the learning process using structured, pose-aware textual descriptions. The authors propose a \"Geospatial text generator\" that creates formatted text (eg. \"District 1, West\") from ground truth pose information during training. This text is then refined by a LiDAR-Anchored Text embedding refinement module, which conditions the text embedding on the current LiDAR scan to create a view-specific representation. To make the method practical for inference where ground truth pose is unavailable, the paper proposes a distillation strategy. This involves training a LiDAR-only \"student\" model to mimic the predictions of the text-assisted \"teacher\" model. Experiments on standard datasets like QEOxford, Oxford, and NCLT show that GTR-Loc outperforms existing state-of-the-art methods.", "questions": "- The core of your method is using discretized pose information as a regularizer. Could you elaborate on why this is framed as \"text-assisted\" localization, which implies a connection to natural language understanding, rather than as learning with auxiliary supervision from discretized pose labels? My score could be raised if you can provide a strong justification for why the \"language\" aspect is fundamental, beyond simply being a carrier for the discrete IDs. Alternatively, re-framing the contribution around a novel auxiliary task and distillation scheme would also clarify the paper's core novelty.\n\n- Could you justify the use of a pre-trained CLIP text encoder for your simple, template-based strings? Have you considered or run experiments with a simpler baseline where the district and direction IDs are encoded via a simple, randomly initialized embedding layer and then concatenated or fused? Demonstrating that the CLIP encoder provides a significant, non-trivial benefit over a much simpler embedding scheme would strongly justify your design choice. If a simpler method performs comparably, it would be a valuable finding to include, potentially simplifying the model.\n\n- Table 7 shows a small but consistent performance gap between the full \"teacher\" model (using ground truth text at inference) and the distilled \"student\" model. Could you provide more insight into this gap? For example, are there specific scenarios, environments (eg. open areas vs. dense city blocks), or types of ambiguity where the distilled model is less effective than the teacher?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n- The paper tackles the well-known and significant problem of localization ambiguity in large-scale environments, where geometrically similar but spatially distinct places can confuse SCR-based methods. The motivation is clear and the proposed direction of using additional context is sound.\n\n- The MRD strategy is interesting. It provides an elegant solution to the problem of needing to generate text at inference time. This teacher-student framework makes the approach practical for real-world deployment and is a good contribution in this context.\n\n- The method demonstrates state-of-the-art results on several challenging benchmarks. The reported 9.64%/8.04% improvement in position/orientation accuracy over a strong baseline (LightLoc) on the QEOxford dataset is good and highlights the effectiveness of the proposed regularization.\n\n- The authors have conducted a comprehensive set of ablation studies that validate the contribution of each component of GTR-Loc. The experiments effectively demonstrate the benefits of using the proposed geospatial text over scene descriptions (Table 4), the effectiveness of the LATER module's Transformer-based fusion (Table 6), and the impact of the MRD strategy (Table 7).\n\n**Weaknesses**\n- The main weakness lies in the framing of the core contribution as \"geospatial text regularization.\" The \"text\" generated by GTG is not natural language but rather a simple, structured concatenation of two discrete labels (district ID and direction ID). While effective, this feels more like a very clever form of feature engineering or an auxiliary task with discretized pose labels than a true integration of language and vision. Invoking the representation power of vision-language models like CLIP seems somewhat overstated when the input vocabulary is so limited and structured.\n\n- Following the point above, the paper does not sufficiently justify the use of a powerful, pre-trained text encoder from CLIP for such simple, template-based strings. The semantic richness that a CLIP encoder is designed to capture seems like overkill for inputs like \"District 99, West-Southwest.\" It is unclear if a much simpler approach, such as using separate learnable embedding layers for the district and direction IDs, would have performed similarly. \n\n- While the paper includes an ablation on the number of districts and directions (Table 5), the discussion is brief. The choice of z=100 and d=16 is presented as optimal, but there is little analysis of the trade-offs. For instance, how does performance degrade in very large-scale maps where 100 districts might be too coarse? Conversely, how sensitive is the model to overfitting if the granularity is too fine?", "was_revised": true, "final_justification": "Overall, the paper proposes an interesting idea centered around using embeddings of places to resolve ambiguity in SCR-based localization. My concerns regarding the performance of CLIP vs randomly initialized embeddings was satisfactorily addressed. I still feel that the \"semantic\" nature of the text is not really apparent when formulated as [district ID, direction ID] and could be better utilised. My final rating is Borderline Accept.", "rebuttal": "Thank you for your insightful comments.\nWe have addressed each point and will integrate all corresponding revisions into the final version of the paper.\n\n**Response to W1\\&Q1.**\n(1) *Why \"text-assisted\" localization?*\nThe focus of our method is not on processing natural language, but on learning a novel geospatial text regularization to solve localization ambiguities.\nWe use the term \"text\" because we convert discrete geospatial labels into text strings (e.g., \"District 50, North\"), which are then processed within the text modality. \nThis textification is critical because it leverages the rich semantic knowledge embedded in the CLIP text encoder. \nIf we use discrete IDs (e.g., 50) as categorical labels instead, the model needs to learn complex relationships between these meaningless symbols and point features from scratch. \nThis not only presents significant optimization challenges but also leads to overfitting.\nThe model cannot comprehend inherent spatial relationships like the adjacency of \"District 50\" and \"District 51\" or the opposition of \"North\" and \"South.\"\n\nIn contrast, by converting the labels into text with semantic meaning (e.g., \"District 50, North\"), we leverage the vast world knowledge and text understanding capabilities embedded in pre-trained VLMs. \nThe powerful text encoders of traditional VLMs (e.g., CLIP) have excellent robustness and generalization capabilities for text.\nCLIP understands that \"North\" and \"Northeast\" are semantically related. \nThis significantly reduces the dependency on task-specific labeled data, and because of the flexibility of text, a fixed network architecture can easily adapt to different datasets or changing data categories.\nHence, the proposed geospatial text is not a simple feature engineering.\n\n(2) *Re-framing the contribution.*\nWe acknowledge that the framing could be clearer. \nIn the final version, we will refine our paper to better articulate that we use CLIP to process our novel geospatial text, rather than proposing a new natural language processing.\n\n**Response to W2\\&Q2.**\n(1) *Why use CLIP?*\nWe use a pre-trained CLIP encoder to leverage its rich prior, even for simple, structured text. \nA randomly initialized embedding layer will treat district and direction IDs as independent and arbitrary tokens. \nHence, it needs to learn relationships from scratch, leading to less efficiency and poor generalization. \nIn contrast, CLIP already understands that \"North\" and \"South\" are opposites and \"Northeast\" is an intermediate geographical concept.\nThis meaningful relational structure can adapt to scene changes (e.g., adding districts or changing datasets) without retraining. \nCLIP can also better handle the composition of tokens like \"District 50\" and \"North,\" rather than simple concatenation.\n\n(2) *Replace CLIP with embedding layers.*\nWe conduct new ablation studies by replacing the CLIP text encoder with embedding layers (ELs).\nSpecifically, we learn two separate, learnable embedding layers (nn.Embedding) initialized from scratch: one for the 100 district IDs and another for the 16 direction IDs. \nThe resulting vectors are concatenated and passed through an MLP to match the feature dimension of the original CLIP embedding. \nThe results shown below indicate that using simple learnable embeddings only leads to a small performance improvement.\nAlthough it is still effective, its performance is noticeably inferior to the full GTR-Loc that uses CLIP.\nHence, using a powerful, pre-trained CLIP text encoder is not overkill. \n\n|Method|QEOxford|Oxford|NCLT|\n|-|-|-|-|\n|vanilla|0.83/1.12|2.67/1.25|1.46/2.80|\n|w ELs|0.80/1.10|2.64/1.23|1.44/2.75|\n|w CLIP (ours)|0.75/1.03|2.59/1.19|1.40/2.62|\n\n**Response to W3.**\n(1) *Coarse partitioning on large-scale maps.*\nCoarser partitions create overly large districts that can encompass multiple visually similar yet geographically separate locations. \nThen, the text would lose its power to disambiguate. \nSince the dataset scale cannot be increased, we use fewer partitions to illustrate the same situation.\nThe original results in Table 5 show that as partitions decreases from z=100 to z=49, localization accuracy decreases. \nAs shown below, we conduct new ablation studies with fewer partitions (z=25 and z=9). \nThe results indicate that coarser partitions can not provide effective discriminative cues. \nHowever, as the district number is a hyperparameter, it can be scaled with the map size.\n\n(2) *Sensitivity to fine-grained partitioning.*\nExcessively fine partitions risk overfitting due to very sparse data in each district, which can turn the textual cue into a rigid lookup key instead of a soft regularizer. However, our design demonstrates robustness to this. \nResults in Table 5 and the table below, respectively, show that as we increase partitions to z=144, and even to extreme values like z=2500 and z=10000, performance plateaus rather than degrades sharply. \nIt suggests that the model is not overly sensitive to overfitting.\nThis resilience stems from the regularizing effect of MRD distillation, which effectively smooths out noise from the sparse data within these fine-grained partitions.\n\n|Method|QEOxford|Oxford|NCLT|\n|-|-|-|-|\n|z=9,d=16|0.82/1.11|2.64/1.24|1.44/2.74|\n|z=25,d=16|0.81/1.11|2.64/1.25|1.43/2.73|\n|z=100,d=16|0.75/1.03|2.59/1.19|1.40/2.62|\n|z=2500,d=16|0.82/1.11|2.64/1.24|1.43/2.76|\n|z=10000,d=16|0.82/1.15|2.63/1.28|1.45/2.87|\n\n**Response to Q3.**\n(1) *Performance gap analysis.*\nThe small performance gap shows that knowledge distillation successfully transfers the teacher's reasoning, not just its results. \nBy mimicking the teacher's text-regularized approach, the student learns to resolve subtle geometric ambiguities.\nThis is highly effective on datasets focused on geometric ambiguities like QEOxford/Oxford, where the student's position error gap is minimal (as low as 1.33%/0.39%). \nThe gap is larger on NCLT, likely due to less stable data from its Segway collection platform. \nAcross all datasets, the consistent gap in orientation error (more than 5%) suggests that distilling knowledge for precise orientation is more challenging than for position.\n\n(2) *Failure scenario for the student.*\nThe student model's failures are confined to rare, extreme scenarios where LiDAR data contain almost no distinctive geometric features.\nFor example, on a very long street in the Oxford dataset, the building facades, streetlights, and other structures are completely identical. \nIn these few instances (<1% of the trajectory), the teacher model succeeds by leveraging geospatial text, an external cue that the student lacks. \nWhile these specific failures can produce large errors, their statistical impact becomes negligible when averaged over the entire dataset. \nIn the vast majority of cases, the student effectively utilizes subtle geometric cues, achieving performance nearly identical to the teacher's."}]

Author rebuttal:

Meta-review: In the paper the author introduce a method called GTR-Loc, which use a combination of text cues and LIDAR data. The main contribution is this novel combination framework, i.e. the use of textual descriptions (here it was decided to use fairly specific type of text cues that give rich geometric information) to mitigate localization ambiguities in large-scale outdoor environments. To handle intra-region variability, the authors propose a LiDAR-Anchored Text Embedding Refinement (LATER) module that adapts text embeddings based on local point cloud features. The model is trained using a dual-regression architecture with a Modality Reduction Distillation (MRD) strategy to enable efficient LiDAR-only inference. The authors conduct experiments showing that the proposed method consistently improves localization robustness. Furthermore, the paper presents extensive ablation analyses, demonstrating that the proposed components contribute meaningfully to resolving ambiguity in visually similar scenes.
Strengths include (i) novel ideas, (ii) solid ablation study, (iii) solid experimental validation, which show state of the art performance on the QEOxford dataset, (iv) well designed (modular) architecture that allows for improvement (and ablation studies). 
Weaknesses include (i) it seems that the method was designed with the geometrically informative text descriptions in The QEOxford dataset and results were only on par with other methods for the other datasets, (ii) discussions could be improved, e g on the use of CLIP for such limited type of text descriptions and on the ablation studies. 
The authors and reviewers were actively engaged in discussion during the rebuttal period and the authors made a good job in answering weaknesses/questions posed by the reviewers. In the end reviewers were all for accepting the paper and I agree and therefore recommend accepting the paper.
Decision: Accept (poster)