# Gaussian Splatting Map Registration with Orthographic Bird's-Eye-View Renderings

Hugo Leblond, Gilles Simon, Renato Martins, Cédric Demonceaux,
Marie-Odile Berger

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Hugo Leblond, Gilles Simon, Renato Martins, Cédric Demonceaux, Marie-Odile Berger. Gaussian Splatting Map Registration with Orthographic Bird's-Eye-View Renderings. WACV 2026 - IEEE/CVF Winter Conference on Applications of Computer Vision, IEEE/CVF, Mar 2026, Tucson (Arizona), United States. hal-05444500

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Gaussian Splatting Map Registration with Orthographic
Bird's-Eye-View Renderings

H. Leblond¹ G. Simon¹ R. Martins² C. Demonceaux² M.-O. Berger¹

¹Université de Lorraine, INRIA, LORIA, France

²Université Bourgogne Europe, CNRS, ICB, France

{hugo.leblond, gilles.simon, marie-odile.berger}@inria.fr

{renato.martins, cedric.demonceaux}@ube.fr

**Abstract**

Gaussian Splatting (GS) is a promising scene representa-
tion for visual localization and SLAM. Recent works have
explored loop closure detection via Gaussian registration,
improving map consistency and accuracy. However, achiev-
ing reliable registration given two GS representations from
different acquisitions remains challenging. In this paper,
we propose a complete pipeline to perform the matching
and registration given two GS maps. The proposed method
is grounded in generating orthographic bird's-eye views
(BEVs) of optimized Gaussian models. The proposed ap-
proach leverages photometric and geometric information
extracted directly from the GS to provide a trade-off of ac-
curacy and invariance to different viewing changes (e.g.,
as types of GS maps, seasons, or illumination). Unlike ex-
istig 3D registration methods, which become inefficient
as the number of Gaussians grows, our approach lever-
ages 2D orthographic renders thus considerably reducing
the registration complexity. Experiments on two public
datasets demonstrate that our method achieves higher ac-
curacy than several existing baselines, while also maintain-
ing better registration results when dealing with GS maps
learned by different techniques (e.g., 3DGS to LightGaus-
sian), or GS maps presenting viewing changes such as vary-
ing illumination conditions. Source code is available at:
https://gitlab.inria.fr/tangram/bev-splatreg

1. Introduction

Simultaneous Localization and Mapping (SLAM) has ad-
vanced significantly with the emergence of neural rendering
techniques. Neural Radiance Fields (NeRF) [25] have en-
abled photorealistic scene reconstructions, while 3D Gaus-
sian Splatting (3DGS) [14] has recently improved rendering
speed and memory efficiency, offering compact and scal-
able scene representations. Several works have explored

integrating 3DGS into SLAM pipelines, especially with
RGB-D data [36]. Matsuki et al. [24] introduced the first
SLAM system relying solely on GS as a 3D representa-
tion for both monocular and RGB-D inputs. However, these
systems lack essential capabilities for ensuring global con-
sistency, such as loop closure and map-level relocalization.
This motivates the need for robust 3D registration methods
that can align independently reconstructed GS maps–an es-
sential step for enabling loop closure, relocalization, and
collaborative mapping in GS-based systems.

While Bird's-Eye-View-based methods have shown ef-
fectiveness for loop closure and map alignment [4, 12, 23]
in traditional SLAM pipelines, they are typically designed
for point cloud data and do not leverage the photorealis-
tic scene representations enabled by neural rendering. This
opens the door for exploring Bird's-Eye-View (BEV) style
registration directly on 3D GS models, which offer both ge-
ometric structure and visual richness. On the other hand, to
address 3DGS registration, several methods rely on a two-
stage pipeline using the 3DGS model alongside the origi-
nal camera poses. GaussReg [5] uses learning-based point
cloud registration on Gaussian centers to obtain an initial
pose, which is then refined using volumetric features ex-
tracted from overlapping camera subsets. LoopSplat [39]
performs loop closure using NetVLAD descriptors to find
visually similar keyframes and assesses alignment using the
residuals from rendered Gaussian splats. PhotoReg [37]
goes further by leveraging 3D foundation models to esti-
mate 3D structure from images, enabling coarse alignment
without depth sensors, and refines poses via photometric
loss. However, all these methods depend on the original
camera extrinsics and assume access to a specific GS train-
ing pipeline, which limits generalization.

In contrast, our work targets a more flexible and self-
contained solution: registration directly from the GS mod-
els alone, without requiring access to original training poses
or images. This is particularly important when such meta-
<figure><img src="image_2.png" /><figcaption>Figure 1. Main steps of our GS registration method on two sequences with 20% overlap. Top row: example from two reconstructed GS scenes and of the BEV images rendering process. Bottom row: registration pipeline showing feature matching between BEV~A~ and BEV~B~, RANSAC-based affine transformation estimation, height estimation, and final alignment of the scenes.</figcaption></figure>

data is missing, unavailable, or costly to store. Moreover, prior approaches have not been systematically evaluated for robustness across different GS reconstruction methods, sensor types, or scene conditions, such as weather variations.

To address this, we propose a BEV-based approach for aligning 3DGS maps. Inspired by bird's-eye view (BEV) techniques used in LiDAR-based loop closure [12, 19, 22, 23], we generate orthographic projections from the GS models along a common known direction. This direction is often the gravity vector, which is known in many datasets captured with a vertical image plane; however, it can also be reliably estimated in urban environments using vanishing point (VP) detection, achieving high registration accuracy, as shown in the experiments section of this paper. Orthographic projection allows us to estimate 2D in-plane transformations (rotation and translation), as well as the camera BEV height. This reduces the 6-DoF registration problem to 4 DoF and enables efficient alignment. An overview of this approach is shown in Fig. 1. Optionally, a final refinement step can be applied to further optimize a full 6-DoF registration by minimizing the similarity between renderings of the Gaussian Splatting models. Unlike typical BEV methods that rely on point density or elevation maps, our method leverages the anisotropic Gaussians on color intensity and Gaussian density, presenting a trade-off between appearance distinctiveness and semantic invariance to scene changes.

Our method is validated across indoor and outdoor environments, and we show that it remains robust when applied to GS models generated by different reconstruction pipelines, as well as under changing weather conditions.

These experiments highlight the potential of GS-based registration methods that are input-agnostic, storage-efficient, and applicable in real-world SLAM systems. To the best of our knowledge, we are the first to propose a registration method for Gaussian Splatting scenes that does not rely on training camera poses, demonstrating its effectiveness in both indoor and outdoor environments. The main contributions of this work are summarized as follows:

* We propose a lightweight 3D registration method of two GS maps based on orthographic BEV renders of GS models. Our model estimates 4 DOF: the BEV camera height, followed by 2D in-plane transformation (rotation and 2D translation). In addition, we show how gravity vectors can be robustly obtained from zenith VP detections, and an optional refinement step is proposed for more accurate registration.
* Our method demonstrates robustness to variations in the underlying 3D Gaussian splatting reconstruction pipeline, addressing the challenge of aligning GS models generated using different algorithms (e.g., 3DGS or LightGaussian).
* We validate the approach on multiple public datasets, including both indoor and outdoor scenes, and provide comprehensive experiments demonstrating its accuracy and robustness under diverse conditions.

## 2. Related Work

### 2.1. Point Cloud Registration

3D point cloud registration estimates the spatial transformation between two overlapping point clouds captured from different viewpoints. Classical methods typically follow a
two-step pipeline: correspondence searching using hand-crafted features (e.g., FPFH [31], SHOT [35]), followed by transformation estimation. Techniques like RANSAC [10] offer robustness to outliers, while ICP [2] and its variant colored ICP [26] iteratively refine alignment using geometric and color information. Fast Global Registration [38] (FGR) formulates the problem as a robust global optimization for improved scalability and convergence domain. Recent learning-based approaches, such as GeoTransformer [28], use geometry-aware transformer architectures and overlap-aware losses to improve matching. Despite these advances, classical methods remain strong baselines, especially in scenarios with limited supervision or real-time constraints. These methods can struggle with noise, occlusion, and density variation, as shown in the experiments section.

## 2.2. Bird's Eye View (BEV) Representations

Bird's Eye View (BEV) representations have recently gained attention for their ability to compress 3D point cloud information into 2D while preserving spatial structure. This projection simplifies computation and provides robustness to viewpoint changes, making it attractive for tasks like loop closure and localization [16, 17, 19, 22, 23]. Most existing BEV-based methods are LiDAR-centric and focus primarily on place recognition or coarse localization. Examples include ScanContext [16], ScanContext++ [17], ContourContext [13], BVMatch [22], and other global descriptor methods [19, 23]. These approaches typically operate on elevation or density maps derived from LiDAR scans but do not estimate 3D transformations. MapClosure [12] take a step further by computing 2D relative poses between local map projections using density images, which can then be refined with geometric registration techniques. This demonstrates the potential of BEV projections to support accurate point cloud alignment, particularly when combined with classical registration methods. In parallel, image-based BEV methods have emerged in the context of cross-view geolocation and autonomous driving. These methods aim to learn BEV features from RGB images captured by ground or multi-camera rigs. Notable examples include BEV-CV [32], which transforms ground-level images to BEV for geo-localization, U-BEV [3] and BEVSegFormer [27] for BEV-based scene segmentation and relocalization, and BEVFormer [20], which use spatiotemporal transformers to build BEV representations from sequential image inputs. Incorporating suitable BEV-based image descriptors suitable to registration pipelines with scene changes remains an open and promising research direction.

## 2.3. 3DGS Registration

Most existing 3D Gaussian Splatting (3DGS) registration methods [5, 37, 39] rely on access to the original set of camera poses (extrinsic) used during training, in addition

to the 3DGS model. Typically, a coarse-to-fine strategy is adopted: an initial alignment is followed by refinement guided by known camera viewpoints. For instance, Gauss-Reg [5] performs learning-based point cloud registration on the Gaussians using GeoTransformer [28] to obtain a coarse pose, which is then refined using volumetric features extracted from overlapping camera subsets. LoopSplat [39] addresses loop closure by leveraging NetVLAD-based [1] descriptors to identify visually similar viewpoints between submaps, treating registration as a keyframe localization problem. Residuals from rendered Gaussian splats are used to evaluate alignment quality. PhotoReg [37] removes the need for depth sensors by using 3D foundation models to reconstruct scene geometry from 2D image pairs, enabling coarse alignment even with minimal overlap, followed by photometric refinement. However, these approaches rely on the availability of the training-time camera poses during registration. This dependency poses practical limitations – especially when extrinsic are not preserved or when avoiding storage of auxiliary training data is desirable. Moreover, they have a high sensitivity to GS changes (e.g., type of GS map), and none of these methods operate directly on the 3DGS model alone, motivating the development of approaches that perform registration purely on the Gaussian representation.

## 3. Method

Given two independent data sequence inputs,

$$D_A = \{C_i^A, p^A, (R_i^A, T_i^A)\}_{i=0}^M, \quad D_B = \{C_i^B, p^B, (R_i^B, T_i^B)\}_{i=0}^N \quad (1)$$

which represent the same scene with an overlapping region, where $C_i$ denotes the $i$-th training image in the input sequence, $(R_i, T_i)$ represents its corresponding pose and $p$ denotes an initial point-cloud representing the sequence. Given the common, usually vertical, direction for each sequence our objective is to register the two Gaussian Splatting representations, $GS_A$ and $GS_B$ representing respectively the data input A and B. To achieve this, we aim to determine the rigid transformation $R, T$ between two Bird's Eye View (BEV) renders of the scene, as well as to estimate the relative BEV camera height. This approach com-pacts the 3D registration problem with 6 degrees of freedom (DOF) into a lower-dimensional 4 DOF space, effec-tively reducing the complexity of the alignment process. The workflow of our method is illustrated in Fig. 1, providing an overview of its key steps.

### 3.1. Gaussian Scene Modeling

From the input sequences $D_A$ and $D_B$, we first construct their respective Gaussian Splatting representations, $GS_A$ and $GS_B$, by optimizing an initial set of Gaussians using a photometric loss. Each Gaussian representation is defined
as a set of individual Gaussians, where each Gaussian $G_j$ is parameterized by its mean $\mu \in \mathbb{R}^3$, scale $S \in \mathbb{R}^3$, rotation $R \in \mathbb{R}^{3 \times 3}$, opacity $o \in \mathbb{R}$, and color $C \in \mathbb{R}^3$. Formally, the Gaussian cloud for each scene is given by:

$$ \mathcal{P}_i = \{G_j(\mu, S, R, o, C) \mid j = 1, \dots, N\}. \quad (2) $$

Following 3DGS [14], the ellipsoidal shape of each Gaussian is reconstructed via the covariance matrix: $\Sigma = RSSR^T$. Once the initial Gaussian splatting clouds $GS_A$ and $GS_B$ are generated, the optimization process refines the Gaussian distribution by minimizing a rendering loss, ensuring an accurate and compact scene representation. The optimization is performed iteratively over all keyframes in the sub-map by minimizing the photometric loss. This is defined as a weighted combination of the L1 loss and the Structural Similarity Index (SSIM) loss, designed to capture both pixel-wise accuracy and perceptual similarity:

$$ \mathcal{L}_{\text{render}} = (1 - \lambda) \cdot \|\hat{I} - I\|_1 + \lambda \cdot (1 - \text{SSIM}(\hat{I}, I)), \quad (3) $$

where $\hat{I}$ and $I$ denote the rendered and ground-truth color images, respectively, and $\lambda$ controls the trade-off between the two terms. This optimization process refines the Gaussian representation to accurately reconstruct photometric information while maintaining consistency across the sub-map. As a result, we obtain two optimized Gaussian splatting models, $GS_A$ and $GS_B$, which serve as the basis for the subsequent registration process. GS approaches such as LightGaussian [8] and SuGAR [11] propose modifications to this optimization process, introducing strategies as progressive sparsification and geometrically consistent updates to enhance efficiency and representation quality.

## 3.2. BEV Rendering and Matching

**Principle:** Inspired by BEV properties for localization [12, 19, 22], our method leverages the efficient rendering capabilities of Gaussian splatting to compress 3D scene information into an orthographic 2D BEV render. An orthographic camera is solely defined by its projection direction. In our method, the projection of the GSs is done along a preferred global direction, often the vertical axis in our experiments, though the method is not restricted to this choice. To ensure a consistent scale across different scenes, we define the resolution per pixel in metric units and generate BEV images with a fixed metric resolution. We also limit the scene area which is projected in accordance with the camera path. As a result, while the generated BEVs can be of different size, they can be matched by a 2D isometric transform $Rx + T$ due to the fixed metric resolution. In addition, using orthographic projection along the same direction avoid perspective distortions typically introduced by traditional rendering perspective methods.

**Gravity detection:** We assume that a dominant scene direction is known—typically the gravity vector. This assumption holds for many datasets where the image plane is aligned vertically. Alternatively, the gravity vector may be estimated through detection of the vertical VP (zenith), a task that is generally straightforward in man-made environments where many lines converge toward it. For this purpose, we rely on the robust a-contrario method [33], which identifies the zenith as the VP supported by the largest set of converging segments. In practice, one zenith is estimated for each image in the GS dataset. Some estimates, however, may be unreliable, as near-vertical structures such as fences, railings, or trees can produce misleading directions. To mitigate this, we select a representative gravity vector by computing the medoid [34] of all estimates, i.e., the vector minimizing the sum of cosine distances to the others —note that the same procedure can be applied to a gravity vector from an IMU.

**Rendering Orthographic BEV:** To generate the BEV orthographic renders, we position a virtual camera above each scene using a systematic approach designed to ensure comprehensive coverage and consistency across different submaps. We start by defining a fixed spatial metric resolution per pixel, which guarantees a uniform level of detail for all scenes. This resolution then defines the rendered image size. To prevent excessively large renders caused by outlier points in the GS maps, we discard the Gaussians that are significantly distant from a fixed threshold relative to the median position of each GS scene centroid. Additionally, a nearest-neighbor filter removes isolated Gaussians that could introduce noise in the final render. After cleaning the Gaussian cloud, we compute its bounding box in the camera coordinate system associated with each GS and proceed with the rendering. From each cleaned GS model, we render color, depth, and Gaussian density images to capture both photometric and spatial information in the BEV domain.

**Matching BEVs:** After generating the orthographic renders for the Gaussian Splatting submaps $G_A$ and $G_B$, we employ a feature-based matching approach to compute the rigid transformation between them. We adopt SuperPoint [7] for keypoint detection and description, followed by Light-Glue [21] for feature matching, processing the density and RGB renders in parallel. The matches obtained from both modalities are then fused to compute a set of joint correspondences. We fuse matches from both density and RGB renderings to combine the appearance invariance of density (e.g., to viewing changes between the GS maps) with the discriminative capabilities of RGB, improving robustness in ambiguous or low-texture scenarios. The 2D rigid transform is finally estimated using a RANSAC-based alignment strategy. This initial 3-DOF transform then allows to perform depth map alignment to obtain height estimation.
tion. Unlike traditional alpha blending used in depth and color rendering—which may introduce errors when Gaussian opacities are low and viewpoint changes are extreme— we propose an adapted depth rendering approach. Instead of integrating over all Gaussians, we define the depth at each pixel (x, y) as the depth of the most visible Gaussian:

$$D(x, y) = \mu_j \quad \text{with} \quad j = \arg \max_i (T_i \cdot \alpha_i), \quad (4)$$

where $\mu_j$ denotes the depth of the Gaussian (as defined in Eq. (2)) that contributes most to the final pixel color, based on the product of its transmittance [14] $T_i$ and opacity $\alpha_i$ over the raycasting in the BEV vertical direction. This formulation improves robustness in scenarios where transparency leads to unreliable depth values. To further mitigate the effect of local mismatches or noise in the depth estimates, we compute the final height-offset using the median of the per-pixel depth differences between the aligned depth maps:

$$\Delta z = \text{median}(|D_{\text{ref}}(x, y) - D_{\text{align}}(x, y)|), \quad (5)$$

where $D_{\text{ref}}$ and $D_{\text{align}}$ denote the reference and aligned depth maps, respectively, and the median is taken over all valid pixels. This robust statistic reduces the influence of outliers and ensures stable estimates even in the presence of noisy depth estimates (as shown in the experiments).

**Refinement stage:** The registration can optionally be refined by adjusting the 6-DOF parameters through minimization of the photometric loss in Eq. (3) between renderings of $D_A$ and $D_B$. This refinement requires selecting a camera viewpoint in $D_A$, which in practice can be done by using one of the dataset images.

## 4. Experiments

We evaluate our registration method through a series of experiments designed to assess its accuracy, robustness, and generalizability to different types of GS maps. First, we compare our approach against several baseline methods on two standard public datasets in sections 4.5 and 4.6 to establish performance under typical conditions. Next, we test the robustness of our method across various types of 3D Gaussian Splatting (3DGS) reconstructions, examining its adaptability to different scene representations in section 4.7. We further evaluate performance under varying lighting conditions to demonstrate invariance to appearance changes in section 4.8. Finally, we conduct ablation and sensitivity studies to analyze the impact of key components in our pipeline and to validate robustness against depth noise and parameter variations in Sec. 4.9.

### 4.1. Experimental Setup

We evaluate our method on two public datasets: Tanks and Temples [18], tested on 13 outdoor sequences, and Scan-

<figure><img src="image_3.png" /><figcaption>Figure 2. Registration with BEV on the Tanks and Temples Caterpillar sequence (overlap ratio 0.2) and ScanNet (overlap 0.4). First two columns: keypoint matching, 3rd column: registered BEV.</figcaption></figure>

Net [6], evaluated on the first 25 scenes (comprising 50 sequences in total). For each dataset, Gaussian Splatting (GS) scenes were trained from either COLMAP or pre-registered poses. For the Tanks and Temples [18] scenes, we used COLMAP camera poses and 3D models, aligning them to a common world frame using the zenithal (gravity) direction; the initial point cloud was taken directly from COLMAP. Sky regions were masked to reduce the influence of irrelevant background content. For ScanNet, we used the provided camera poses and initialized the GS training with 100k randomly sampled points. Each dataset is split into two subsets: $D_A$ and $D_B$, where for ScanNet, each subset contains 400 images with varying degrees of overlap. For Tanks and Temples [18], the number of images per subset ranges between 33 and 368 depending on the scene and desired overlap. To simulate realistic misalignments, we applied random in-plane (zenithal axis) rotations of up to 30°, along with translations whose magnitudes were sampled as a ratio of the total camera path length to the submaps from $D_B$. These transformations are stored as ground-truth for evaluating registration performance.

To assess the robustness of our method, we tested scenes with varying levels of overlap between submaps. Unlike GaussReg [5], which computes results using random overlap ratios between 0.2 and 0.6, we evaluated our method at three decreasing overlap levels 0.6, 0.4, and 0.2 to assess its impact on the registration procedure. The BEV images used for matching were rendered at a resolution of 0.005m per pixel, ensuring high-detail scene representations. For ScanNet, which provides metric-scale camera poses, this directly corresponds to real-world distances. In the case of Tanks and Temples [18], where absolute scale is unknown, we adopted the same rendering resolution for consistency across datasets, interpreting it as a relative unit. This approach ensures comparable detail levels and supports fair evaluation of registration performance, especially in indoor environments.
<table><caption>Table 1. Evaluation of registration performance across different overlap ratios on the Tanks and Temples [18] dataset. Lower is better for RRE and RTE, higher is better for SR.</caption><thead><tr><th rowspan="2">Method</th><th colspan="3">Overlap 0.6</th><th colspan="3">Overlap 0.4</th><th colspan="3">Overlap 0.2</th><th colspan="3">Mean (0.2-0.6)</th></tr><tr><th>SR↑</th><th>RTE↓</th><th>RRE↓</th><th>SR↑</th><th>RTE↓</th><th>RRE↓</th><th>SR↑</th><th>RTE↓</th><th>RRE↓</th><th>SR↑</th><th>RTE↓</th><th>RRE↓</th><th>Time(s)↓</th></tr></thead><tbody><tr><td>GaussReg [5]</td><td>0.690</td><td>0.051</td><td>1.120</td><td>0.460</td><td>0.045</td><td>1.140</td><td>0.150</td><td>0.053</td><td>0.650</td><td>0.433</td><td>0.050</td><td>0.970</td><td><strong>5.5</strong></td></tr><tr><td>FGR [38]</td><td>0.461</td><td>0.103</td><td>2.624</td><td>0.230</td><td>0.081</td><td>2.730</td><td>0.153</td><td>0.045</td><td>2.909</td><td>0.282</td><td>0.076</td><td>2.754</td><td>31.2</td></tr><tr><td>ICP [2]</td><td>0.769</td><td>0.070</td><td>0.755</td><td>0.692</td><td>0.062</td><td>0.988</td><td>0.538</td><td>0.072</td><td>1.116</td><td>0.666</td><td>0.068</td><td>0.953</td><td>6.5</td></tr><tr><td>Colored ICP [26]</td><td>0.615</td><td>0.039</td><td>0.744</td><td>0.231</td><td>0.035</td><td>0.504</td><td>0.384</td><td>0.104</td><td>1.188</td><td>0.410</td><td>0.059</td><td>0.812</td><td>20.9</td></tr><tr><td>RANSAC</td><td>0.538</td><td>0.064</td><td>1.450</td><td>0.538</td><td>0.060</td><td>1.504</td><td>0.384</td><td>0.045</td><td>0.875</td><td>0.487</td><td>0.056</td><td>1.276</td><td>571.2</td></tr><tr><td>MapClosure [12]</td><td>0.846</td><td>0.005</td><td>0.153</td><td>0.692</td><td>0.019</td><td>0.252</td><td>0.307</td><td>0.042</td><td>1.300</td><td>0.615</td><td>0.022</td><td>0.568</td><td>11.2</td></tr><tr><td>Ours</td><td><strong>1.000</strong></td><td><strong>0.012</strong></td><td><strong>0.106</strong></td><td><strong>1.000</strong></td><td><strong>0.021</strong></td><td><strong>0.078</strong></td><td><strong>0.923</strong></td><td><strong>0.055</strong></td><td><strong>0.257</strong></td><td><strong>0.974</strong></td><td><strong>0.029</strong></td><td><strong>0.147</strong></td><td>10.7</td></tr><tr><td>Ours + VP</td><td><strong>1.000</strong></td><td>0.016</td><td>0.107</td><td><strong>1.000</strong></td><td>0.032</td><td>0.185</td><td><strong>0.923</strong></td><td>0.061</td><td>0.221</td><td><strong>0.974</strong></td><td>0.036</td><td>0.171</td><td>10.7</td></tr><tr><td>Ours + refinement</td><td>1.000</td><td>0.011</td><td>0.089</td><td>1.000</td><td>0.014</td><td>0.024</td><td>0.923</td><td>0.042</td><td>0.117</td><td>0.974</td><td>0.022</td><td>0.077</td><td>22.7</td></tr></tbody></table>

<table><caption>Table 2. Evaluation of registration performance across different overlap ratios on the ScanNet dataset. Lower is better for RRE and RTE, higher is better for Success Ratio (SR).</caption><thead><tr><th rowspan="2">Method</th><th colspan="3">Overlap 0.6</th><th colspan="3">Overlap 0.4</th><th colspan="3">Overlap 0.2</th><th colspan="3">Mean (0.2-0.6)</th></tr><tr><th>SR↑</th><th>RTE↓</th><th>RRE↓</th><th>SR↑</th><th>RTE↓</th><th>RRE↓</th><th>SR↑</th><th>RTE↓</th><th>RRE↓</th><th>SR↑</th><th>RTE↓</th><th>RRE↓</th><th>Time(s)↓</th></tr></thead><tbody><tr><td>GaussReg [5]</td><td>0.920</td><td>0.060</td><td>1.354</td><td>0.780</td><td>0.071</td><td>1.740</td><td>0.408</td><td>0.090</td><td>1.810</td><td>0.703</td><td>0.074</td><td>1.635</td><td>4.6</td></tr><tr><td>FGR [38]</td><td>0.360</td><td>0.072</td><td>2.183</td><td>0.100</td><td>0.096</td><td>3.223</td><td>0.062</td><td>0.135</td><td>4.153</td><td>0.174</td><td>0.101</td><td>3.186</td><td><strong>2.2</strong></td></tr><tr><td>ICP [2]</td><td>0.220</td><td>0.102</td><td>2.780</td><td>0.140</td><td>0.094</td><td>2.360</td><td>0.040</td><td>0.073</td><td>2.246</td><td>0.133</td><td>0.090</td><td>2.462</td><td>2.3</td></tr><tr><td>Colored ICP [26]</td><td>0.640</td><td>0.051</td><td>1.763</td><td>0.220</td><td>0.070</td><td>2.409</td><td>0.140</td><td>0.078</td><td>2.816</td><td>0.333</td><td>0.066</td><td>2.330</td><td>4.8</td></tr><tr><td>RANSAC</td><td>0.280</td><td>0.100</td><td>3.009</td><td>0.120</td><td>0.085</td><td>2.608</td><td>0.020</td><td>0.085</td><td>2.129</td><td>0.140</td><td>0.090</td><td>2.582</td><td>367.1</td></tr><tr><td>MapClosure [12]</td><td>0.520</td><td>0.024</td><td>1.209</td><td>0.380</td><td>0.032</td><td>1.454</td><td>0.160</td><td>0.066</td><td>1.521</td><td>0.353</td><td>0.041</td><td>1.395</td><td>4.2</td></tr><tr><td>Ours</td><td><strong>1.000</strong></td><td><strong>0.009</strong></td><td><strong>0.258</strong></td><td><strong>1.000</strong></td><td><strong>0.011</strong></td><td><strong>0.299</strong></td><td><strong>0.958</strong></td><td><strong>0.020</strong></td><td><strong>0.679</strong></td><td><strong>0.986</strong></td><td><strong>0.013</strong></td><td><strong>0.412</strong></td><td>4.6</td></tr></tbody></table>

## 4.2. Metrics

We evaluate our method using the same registration metrics adopted by previous works [5, 9]. Specifically, we measure Relative Rotational Error (RRE) between the two maps, Relative Translation Error (RTE) and registration success ratio. As defined in GaussReg [5] and NeRFuser [9], we computed this last metric as the proportion of successful registrations (SR), where a registration is considered successful if the RTE is inferior to 0.2 and RRE remains below 5 degrees. This metric provides an overall measure of the robustness and reliability of the methods. It is important to note that the reported metrics are directly tied to the success ratio. Specifically, they are only calculated for successfully registered scenes, meaning that focusing solely on the metrics without considering the SR may provide a misleading interpretation.

## 4.3. Baselines

We compare our method against a diverse set of state-of-the-art and classical 3D registration approaches. First, we include GaussReg [5], a recent method specifically developed for 3D Gaussian Splatting (GS) registration. Since the training code and fine registration module are not publicly available (by the time of this submission), we evaluate the approach using the released pretrained model for coarse registration. We also benchmark against traditional

geometric registration algorithms on Gaussian centroids including Iterative Closest Point (ICP) [2], Colored ICP [26], RANSAC [10] based alignment, and Fast Global Registration (FGR) [38], to assess our performance relative to well-established baselines. We also include a 2D BEV matching strategy in our comparisons by adapting MapClosure [12] to operate on GS-generated maps. In this variant, BEV images are rendered from the Gaussian Splatting reconstructions and matched using ORB [29] features, allowing us to evaluate the performance of a loop-closure method designed for 2D map alignment in the context of GS-based 3D scene representations. We also report the mean of results across different overlaps. This ensures that our evaluation captures the general performance of the methods under various conditions.

## 4.4. Qualitative Results

Some qualitative registration examples are shown in Fig. 2 for Tanks and Temples [18] (first row, overlap ratio .2) and ScanNet Dataset (second row, overlap ratio .4). As the overlap ratio between the two sequences are only 0.2 and 0.4 in these examples, we can see on the BEVs that the GS models have different detail accuracy which depends on the number of images showing certain parts of the scene. Despite this, several matches can still be obtained allowing successful registration.
## 4.5. Comparison on Tanks and Temples

As demonstrated in Table 1, our method significantly out-
performs the competing approaches in terms of RRE and
RTE across all overlap ratios, while maintaining comparab-
le or superior SR values. Notably, our approach achieves
the highest SR and lowest RRE and RTE in nearly all tested
scenarios, with particularly strong gains observed in chal-
lenging low-overlap conditions (0.2 and 0.4) while main-
taining a competitive runtime. The results obtained with the
estimated gravity, denoted as *Ours + VP*, demonstrate that
comparable accuracy can be achieved without prior knowl-
edge of the reference direction. The final row, *Ours + re-
finement*, reports results after the refinement stage, show-
ing noticeable improvements in both rotation and transla-
tion accuracy. This step, however, incurs a relatively high
computational cost due to the use of a Powell optimization
implemented in Python. These experiments illustrate that
our method delivers high-quality registrations, which can be
further refined when required. Finally, regarding memory
footprint, the average GPU memory footprint of the regis-
tration process was 1.5 GB. Additional details are provided
in the supplementary material.

4.6. Comparison on ScanNet

Table 2 summarizes the registration performance of vari-
ous methods across different overlap ratios on the ScanNet
dataset. For these interior scenes, we do not use estimated
gravity, as the images often have narrow fields of view that
make reliable VP estimation difficult. Our approach con-
sistently achieves the highest SR across all overlap levels,
indicating superior robustness in challenging indoor envi-
ronments. Additionally, our method yields the lowest RTE
and RRE for nearly all overlap ranges, demonstrating pre-
cise and reliable pose estimation. Notably, in scenarios with
high overlap (0.6), our method attains perfect success (SR
= 1) with minimal translation and rotation errors. The run-
time of our method on ScanNet is significantly lower than
on Tanks and Temples, mainly due to the smaller number
of Gaussians in indoor scenes. Other methods also benefit
from faster runtimes, likely due to the more discriminative
indoor geometry. Even in low-overlap cases (0.2), where
registration is typically more difficult, our approach main-
tains high success and significantly better accuracy com-
pared to other methods. Although the GaussReg [5] model
was trained on ScanNet data, we observed that the perfor-
mance reported in their paper could not be fully reproduced
using the publicly available code and weights.

4.7. Cross GS Comparison

Recent advances in Gaussian Splatting have led to more
compact and efficient representations, such as LightGaus-
sian [8] and SuGaR [11], which improve scalability, mem-
ory efficiency, and geometric fidelity. These improvements

raise the question of how well registration methods general-
ize across different GS variants, especially in practical sce-
narios where submaps may originate from different recon-
struction pipelines. To explore this, Table 3 reports perfor-
mance in two settings: intra-variant matching within 3DGS,
LightGaussian, or SuGaR models, and cross-variant match-
ing between them. This evaluation provides insight into the
robustness of competitors under architectural variations in
the GS representation. Our method maintains strong per-
formance within each GS type, achieving significantly bet-
ter results than existing methods. In the cross-model set-
ting—where submaps are reconstructed with different vari-
ants such as 3DGS, LightGaussian, or SuGaR—we observe
a performance drop, which is expected due to differences in
point distribution, density, and surface alignment (some ex-
amples are shown in Fig. 1 of the supplementary). Nonethe-
less, our approach still outperforms the strongest considered
baselines.

4.8. Lighting Condition Robustness

To evaluate robustness under real-world lighting changes,
we perform experiments on the first two scenes from
the NeRF-OSR [30] dataset: *Galerie Europa* and *Lud-
wigskirche*. For each scene, we select a sequence of images
taken at different times of the day with significant lighting
variations– such as transitions from gray to sunny weather,
or sunrise to sunset (some examples are provided in Fig.
2 in the supplementary). We compare our method in two
configurations (with RGB only and RGB-density informa-
tion) against state-of-the-art methods. As shown in Tab. 4,
our approach maintains perfect registration accuracy across
all tested conditions, while existing methods fail or degrade
significantly in the presence of strong illumination changes.

4.9. Ablation and Sensitivity Analysis

**Ablation on feature modality.** To assess the contribution of the different input modalities used in our 2D matching pipeline, we conduct an ablation study comparing performance with RGB-only inputs versus the combined use of RGB and density projections derived from the 3DGS models. As shown in Tables 3 and 4, while the RGB-only variant already achieves strong performance across most settings, the addition of density maps consistently improves both registration accuracy and success rate. This gain is particularly noticeable in challenging scenarios involving low-overlap regions or significant appearance variations. The density images, which encode structural and spatial information inherent to the Gaussian representation, complement the photometric features from RGB and help disambiguate regions with appearance changes. These results confirm that leveraging both modalities leads to more robust and accurate registration.
<table><caption>Table 3. Evaluation of map matching performance across different reconstruction methods and overlap ratios on the Tanks and Temples [18] dataset. Results are averaged across overlap ratios (0.2, 0.4, and 0.6). Higher is better for SR, while lower is better for RTE and RRE.</caption><thead><tr><th rowspan="2">Method Pair</th><th colspan="3">GaussReg</th><th colspan="3">MapClosure</th><th colspan="3">Ours RGB only</th><th colspan="3">Ours</th></tr><tr><th>SR↑</th><th>RTE↓</th><th>RRE↓</th><th>SR↑</th><th>RTE↓</th><th>RRE↓</th><th>SR↑</th><th>RTE↓</th><th>RRE↓</th><th>SR↑</th><th>RTE↓</th><th>RRE↓</th></tr></thead><tbody><tr><td>3DGS ↔ 3DGS</td><td>0.433</td><td>0.050</td><td>0.970</td><td>0.615</td><td>0.022</td><td>0.568</td><td>0.897</td><td>0.026</td><td>0.393</td><td><strong>0.974</strong></td><td>0.016</td><td>0.147</td></tr><tr><td>LightGaussian ↔ LightGaussian</td><td>0.333</td><td>0.080</td><td>1.328</td><td>0.000</td><td>Div.</td><td>Div.</td><td>0.693</td><td>0.025</td><td>0.319</td><td><strong>0.769</strong></td><td>0.026</td><td>0.440</td></tr><tr><td>SuGaR ↔ SuGaR</td><td>0.564</td><td>0.065</td><td>1.445</td><td>0.077</td><td>0.023</td><td>2.056</td><td>0.795</td><td>0.021</td><td>0.335</td><td><strong>0.820</strong></td><td>0.019</td><td>0.244</td></tr><tr><td>3DGS ↔ LightGaussian</td><td>0.205</td><td>0.075</td><td>1.415</td><td>0.000</td><td>Div.</td><td>Div.</td><td>0.458</td><td>0.028</td><td>0.464</td><td><strong>0.461</strong></td><td>0.027</td><td>0.374</td></tr><tr><td>3DGS ↔ SuGaR</td><td>0.103</td><td>0.126</td><td>3.447</td><td>Div.</td><td>Div.</td><td>Div.</td><td><strong>0.359</strong></td><td>0.070</td><td>0.204</td><td><strong>0.359</strong></td><td>0.089</td><td>0.342</td></tr><tr><td>LightGaussian ↔ SuGaR</td><td>0.103</td><td>0.076</td><td>2.181</td><td>0.077</td><td>0.152</td><td>1.950</td><td><strong>0.487</strong></td><td>0.036</td><td>0.660</td><td><strong>0.487</strong></td><td>0.032</td><td>0.775</td></tr></tbody></table>

**Impact of the depth computation.** To verify the effectiveness of our approach for depth computation described in eq. 4, we conduct an evaluation comparing our depth computation to the alpha blending method in terms of registration accuracy across different overlap ratios on the Tanks and Temples dataset. Table 5 presents these results, showing that the proposed depth estimation consistently leads to improved RTE while maintaining similar success ratios.

**Impact of noisy gravity vector estimation.** The gravity vector estimated from VPs exhibits an average error of approximately 2°, yet this has little impact on registration accuracy, as shown in Table 1. Perturbation experiments with synthetic noise, however, indicate that fixed-magnitude errors of 1° and 2° with random orientation, applied to both scenes, reduce the success ratio from 0.889 to 0.717 (Table 6), which is still better than competing methods. We note, however, that these simulations represent a worst-case scenario in which the noise on the gravity vectors is independent. In practice, our VP estimations reveal that this

<table><caption>Table 4. Registration performance under varying lighting conditions on two NeRF-OSR scenes. The Galerie Europa scene represents clear weather with dynamic lighting, while Ludwigskirche captures strong temporal shifts such as sunrise and sunset.</caption><thead><tr><th rowspan="2">Method</th><th colspan="3">Galerie Europa</th><th colspan="3">Ludwigskirche</th></tr><tr><th>SR↑</th><th>RTE↓</th><th>RRE↓</th><th>SR↑</th><th>RTE↓</th><th>RRE↓</th></tr></thead><tbody><tr><td>GaussReg</td><td>0.000</td><td>0.208</td><td>25.356</td><td>0.000</td><td>0.315</td><td>31.467</td></tr><tr><td>MapClosure</td><td>0.000</td><td>6.332</td><td>98.561</td><td>0.000</td><td>185.071</td><td>59.412</td></tr><tr><td>Ours RGB only</td><td>1.000</td><td>0.023</td><td>2.763</td><td>0.000</td><td>1.17</td><td>79.875</td></tr><tr><td><strong>Ours</strong></td><td><strong>1.000</strong></td><td><strong>0.000</strong></td><td><strong>0.092</strong></td><td><strong>1.000</strong></td><td><strong>0.003</strong></td><td><strong>0.245</strong></td></tr></tbody></table>

<table><caption>Table 5. Impact of depth estimation on registration accuracy across overlap ratios on Tanks and Temples. We compare our robust depth method with standard alpha blending (RRE is omitted as it's unaffected by depth quality).</caption><thead><tr><th>Overlap</th><th>Method</th><th>SR↑</th><th>RTE↓</th></tr></thead><tbody><tr><td rowspan="2">0.2</td><td>Alpha Blend</td><td>0.923</td><td>0.053</td></tr><tr><td><strong>Ours</strong></td><td><strong>0.923</strong></td><td><strong>0.033</strong></td></tr><tr><td rowspan="2">0.4</td><td>Alpha Blend</td><td>1.000</td><td>0.021</td></tr><tr><td><strong>Ours</strong></td><td><strong>1.000</strong></td><td><strong>0.008</strong></td></tr><tr><td rowspan="2">0.6</td><td>Alpha Blend</td><td>1.000</td><td>0.013</td></tr><tr><td><strong>Ours</strong></td><td><strong>1.000</strong></td><td><strong>0.008</strong></td></tr></tbody></table>

<table><caption>Table 6. Impact of gravity vector noise on registration metrics for Tanks and Temples [18].</caption><thead><tr><th rowspan="2">Noise (°)</th><th colspan="3">Independent noise</th></tr><tr><th>SR↑</th><th>RTE↓</th><th>RRE↓</th></tr></thead><tbody><tr><td>0</td><td>0.974</td><td>0.029</td><td>0.147</td></tr><tr><td>1</td><td>0.899</td><td>0.068</td><td>0.125</td></tr><tr><td>2</td><td>0.717</td><td>0.094</td><td>0.086</td></tr></tbody></table>

noise is actually correlated, resulting in success rates significantly higher than those reported in this study. Further details are provided in the supplementary material.

## 5. Conclusion

In this work, we present a novel registration method for Gaussian Splatting models based on bird's-eye view projections, enabling robust map alignment without relying on original images and camera poses. Our extensive evaluation across multiple datasets indicates that our approach consistently outperforms existing methods in terms of accuracy (RRE, RTE) and robustness (SR) in both indoor and outdoor scenarios. The proposed method generalizes well across different types of Gaussian Splatting reconstructions, and outperforms other registration methods on cross-model registration between standard 3DGS, Light-Gaussian and SuGaR. Furthermore, our approach remains competitive under challenging lighting and weather conditions, where other methods experience significant degradation. These results highlight the effectiveness and practical applicability of our BEV-based registration method.

While our method shows strong performance on multiple scenarios, future work will focus on improving robustness in cross-representation scenarios and scaling to very large datasets, for example, through adapting the method to recent hierarchical Gaussian representations [15]. These extensions could directly benefit SLAM pipelines, particularly for relocalization across time and varying conditions, enabling more reliable long-term operation.

**Acknowledgements.** This work was partially supported by grants ANR MOVIS from “Conseil Régional de Bourgogne-Franche-Comté” and ANR MANYVIS (ANR-23-CE23-0003-01), to whom we are grateful.
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