UnLoc: Leveraging Depth Uncertainties for Floorplan Localization
Authors: Matthias Wüest
Francis Engelmann
Ondrej Miksik
Marc Pollefeys
Daniel Barath
Venue: ICLR 2026 Poster (2026)
Keywords: floorplan localization
sequential localization
depth uncertainties
mono-depth networks
Primary area: applications to robotics, autonomy, planning

Abstract: We propose UnLoc, an efficient data-driven solution for sequential camera localization within floorplans. Floorplan data is readily available, long-term persistent, and robust to changes in visual appearance. We address key limitations of recent methods, such as the lack of uncertainty modeling in depth predictions and the necessity for custom depth networks trained for each environment. We introduce a novel probabilistic model that incorporates uncertainty estimation, modeling depth predictions as explicit probability distributions. By leveraging off-the-shelf pre-trained monocular depth models, we eliminate the need to rely on per-environment-trained depth networks, enhancing generalization to unseen spaces. We evaluate UnLoc on large-scale synthetic and real-world datasets, demonstrating significant improvements over existing methods in terms of accuracy and robustness. Notably, we achieve $2.7$ times higher localization recall on long sequences (100 frames) and $42.2$ times higher on short ones (15 frames) than the state of the art on the challenging LaMAR HGE dataset.

Published as a conference paper at ICLR 2026
UNLoC: LEVERAGING DEPTH UNCERTAINTIES FOR FLOORPLAN LOCALIZATION
Matthias Wüest
Francis Engelmann
Ondrej Miksik
Mare Pollefeys Daniel Barath
'ETH Zurich "ZHAW Stanford University
USI Lugano
"Microsoft
HHUN-RN SZTAKI
Ground tritt P'Lo
Inpet image aana
Ualave
Flocrpion
....
Ground truth
high
Flacrgion depal Ins]
Pilac
UnLoe (ours)
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Figure 1: UnLoc processes an input image sequence to predict the floorplan depth (in meters) and associated uncertainty for each image column. Using these predictions, it generates probability distribution over potential SE(2) camera poses and outputs the most likely one (blue arrow). The ground truth pose is also shown (red arrow), overlapped by the predicted pose.
ABSTRACT
We propose UnLoc, an efficient data-driven solution for sequential camera local- ization within floorplans. Floorplan data is readily available, long-term persistent, and robust to changes in visual appearance. We address key limitations of re- cent methods, such the lack of uncertainty modeling in depth predictions and the necessity for custom depth networks trained for each environment. We intro- duce novel probabilistic model that incorporates uncertainty estimation, model- ing depth predictions as explicit prohability distributions. By leveraging off-the- shelf pre-trained monocular depth models, we eliminate the need to rely on per- euvironment-traine, depth networks, enhancing generalization unseen spaces. We evaluate UnLoc on large-scale synthetic and real-world datasets, demonstrat- ing significant improvements
existing methods in terms of accuracy and ro- bustness. Notably, we achieve 2.7 times higher localization recall on long se- quences (100 frames) and 42.2 times higher short ones (15 frames) than the state of the art on the challenging LaMAR HGE dataset. Code and materials: https: / //git thub. wwtt/ttttt/tt.cctttt.t www.wteeeeee /
INTRODUCTION
Camera localization within indoor environments a fundamental problem computer vision, es- sential for applications in augmented reality and robotics. Accurate localization enables devices to understand their spatial context, facilitating tasks such as navigation, object interaction, and au- tonomous exploration. Traditional localization methods often rely on pre-built 3D models (Sattler et al., 2011; 2016b; Liu et al., 2017: Sarlin et al., 2019; Panek et al., 2022) or extensive image databases (Schonberger & Frahm, 2016; Arandjelovic et al., 2016; Keetha et al., 2023; Wei al., 2024), which are storage-intensive and require substantial maintenance, limiting their scalability to new dynamically changing environments.

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Floorplans offer a lightweight and readily available alternative for indoor localization. As 2D rep- resentations of spaces, floorplans are easy to obtain (Liu et al., 2018; Yue et al., 2023) and remain unaffected by changes in appearance, such furniture rearrangements lighting variations. Re- cent methods have leveraged floorplans for localization by aligning images with the map (Howard- Jenkins & Prisacariu, 2022; Min et al., 2022; Chen et al., 2024a), enabling devices to localize within new environments as long as a floorplan is available.
Among these, Loc (Chen al., 2024a) has emerged as recent promising approach for sequen- tial visual floorplan localization, significantly outperforming all previous baselines. It integrates observations over time using a histogram filter, achieving impressive accuracy and computational efficiency on range of datasets. However, as promising as it is, Loc has notable limitations that hinder its practical deployment, which we aim to improve upon in this paper:
Lack of Uncertainty Modeling, Loc combines monocular and multi-view depth estimation. However, it assumes that the resulting predictions are all of similar accuracy, and it has no means to represent and predict the uncertainties in the depth predictions. In indoor environments, depth estimation is often unreliable in regions with glass walls, open doorways, or large, featureless walls. When fusing sequence of predictions, not accounting for uncertainty, inaccurate depth predictions adversely affect the localization process, leading to erroneous pose estimates.
-atesit-Specific Depth. Looo relies custom depth prediction network trained separately for each dataset or environment. This per-dataset training requirement poses significant challenges for scalability and robustness. Collecting sufficient depth data for retraining the depth network for every new environment is impractical, especially when rapid deployment is desired.
This paper addresses these shortcomings by introducing novel visual floorplan localization method that incorporates uncertainty estimation into monocular depth prediction and uses it to robustly fuse predictions from sequence of images. Also, our method enables to leverage pre-trained monocular depth models. Our contributions are as follows:
Uncertainty-Aware Depth Prediction: We model floorplan depth predictions as explicit probability distributions, assuming a Laplace distribution centered at the predicted depth with scale parameter given by the uncertainty estimate. This formulation allows us to represent the confidence associated with each prediction. This uncertainty- approach improves localization by weighting predic- tions according to their reliability in challenging regions, and it further provides principled weights for the post-processing optimization, yielding higher accuracy.
Leveraging Off- Sheet Depth Models: Rather than designing or training custom depth networks for each environment as Loc does, we directly employ state-of-the-an art monodepth models pre- trained on large-scale datasets (Yang et al., 2024b). Our formulation treats these models as plug- and-play modules, demonstrating that reliable localization can be achieved with any sufficiently strong depth predictor, without requiring enviromment-speen specific retraining.
The proposed UnLoc (see Fig. 1) achieves significant improvements in accuracy and robustness across multiple datasets compared to prior methods.
2 RELATED WORK
Visual localization is a fundamental problem in computer vision, addressed through various ap- proaches. Traditional methods include image retrieval techniques (Chum al., 2007: Jégou al., 2010; Arandjelovic et al., 2016; Keetha et al., 2023; Wei et al., 2024), which find the most similar images a database and estimate the pose of the query image based on the retrieved ones. Structure- from-Motion-basee approaches (Agarwal et al., 2011: Schonberger & Frahm, 2016; Sattler et al., 2016a; Panek et al., 2022) build 3D model of the environment and establish 2D-3D correspon- dences by matching local descriptors, computing camera poses using minimal solvers (Kukelova et al., 2008) and RANSAC (Fischler & Bolles, 1981) or its recent variants (Barath et al., 2020; Barath & Matas, 2021). Scene coordinate regression methods (Brachmann et al., 20 7a;b) leam to regress the 3D coordinates of image pixels, while pose regression techniques (Kendall et al., 2015; Kendall & Cipolla, 2017) networks to predict 6-DoF camera pose from input images directly. More recently, we saw methods that combine language- based retrieval (Chen et al., 2024b) in combi- nation with scene graph representations (Miao al., 2024; Zhang et al., 2025). These methods often
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rely on pre-built 3D models that are storage-intensiee and scene-specific, limiting their applicability in unseen environments.
To overcome this, floorplan-based localization methods have emerged, utilizing overhead images or floorplans to estimate the SE(2) pose of the camera (Workman et al., 2015; Tian et al., 2017). These approaches can localize images in new scenes long as a floorplan is provided. Floorplan localization is frequently associated with LiDAR sensors (Mostofi et al., 2014; Yin et al., 2019; Zim- merman et al., 2022), which are impractical for widespread mobile device use. Alternative methods reconstruct 3D geometry using depth cameras (Winterhalter et al., 2015) or visual odometry (Mur- Artal et al., 2015). Some approaches extract geometric features like room edges to align with the floorplan (Boniardi et al., 2019; Lin et al., 2019). However, these methods often assume known camera or room height, which is not always feasible.
Recent learning-based methods aim to use only RGB images for floorplan localization. Orienter- Net (Sarlin et al., 2023) localizes images in 2D public maps such as OpenStreetMap using neural matching, but focuses on outdoor environments. LaLaLoc (Howard-Jenkins et al., 2021) estimates the position of panoramic images in a floorplan by embedding map and image features into a shared space. LaLaLoc++ (Howard-Jenkins & Prisacariu, 2022) removes the known camera and ceiling height assumption by directly embedding the floorplan. LASER (Min et al., 2022) represents the floorplan set of points and uses PointNet (Qi et al., 2017) to embed the visible points for each pose, aligning them with image features in a shared space. PF-Net (Karkus et al., 2018) integrates localization within differentiable particle filtering framework, using learned similarity between images and corresponding map patches. Locc (Chen et al., 2024a) utilizes metric monocular depth prediction but requires training custom depth networks, which can limit generalizability, and as- sumes that all predictions are of the same quality.
Sequence-based localization methods enhance robustness by integrating information over time. Bayesian filtering (Dellaert et al., 1999; Chu et al., 2015; Karkus et al., 2018; Boniardi et al., 2019; Mendez et al., 2020) is commonly used to fuse sequential observations, like particle (Dellaert al., 1999) and histogram filters (Thrun, 2002). PF-Net (Karkus et al., 2018) employs differentiable partiele filter for localization but relies on learned observation models that may not generalize well. Loc (Chen et al., 2024a) uses sequential observations and integrates them by histogram filter.
Depth estimation provides valuable geometric information for localization (Chen et al., 2024a), Re- cent advances in pre-trained monocular depth (monodepth) models (Ranftl et al., 2021: Birkl et al., 2023; Guizilini et al., 2023; Yin et al., 2023; Hu et al., 2024; Yang et al., 2024a;b; Bochkovskii et al., 2024), trained on large-scale datasets, enable accurate metric relative depth predictions out-of-theebox, without per-scene training. In this work, we leverage such off-the-shelf networks for image- based floorplan localization, thereby avoiding custom model training and improving gen- eralization across domains.
Depth uncertainty is crucial for reliable use of depth predictions in downstream tasks. Neural net- work uncertainty is commonly categorized as aleatoric (data) or epistemic (model) (Kendall & Gal, 2017; Poggi al., 2020). Aleatoric uncertainty captures inherent observation noise by modeling a distribution over the network output. It is most relevant in regions of the observation space with higher noise and does not decrease with more data. A typical approach trains the network predict parameters of parametric distribution via likeliiel maximization (Nix & Weigend, 1994), usually adding negligible computational overhead (Kendall & Gal, 2017).
Epistemic uncertainty reflects model limitations by placing a distribution over the model parameters. most useful with small datasets or safety-critical tasks and decreases as more data becomes available. Estimating epistemic uncertainty generally incurs significant computation cost (Kendall & Gal, 2017). Common strategies include Monte Carlo Dropout (Srivastava et al., 2014), bootstrapped ensembles (Lakshminarayanan et al., 2017), and Bayesian neural networks (MacKay, 1992).
For pixel-wise monocular depth estimation, prior work has explored both types. Kendall & Gal (2017) propose method to estimate aleatoric and epistemic uncertainty jointly. Poggi et al. (2020) evaluate approaches of both types and propose a self-teaching model for aleatoric uncertainty. Liu et al. (2019) model aleatoric uncertainty by discretizing depth into bins. Roessle et al. (2022) pre- dict aleatoric uncertainty via Gaussian depth distributions with learned variance. In floorplan local- ization, Loc (Chen al., 2024a) estimates flcorplan depth assuming a uniform confidence. In contrast, we model alcatoric uncertainty explicitly to capture ambiguities from glass walls, door-
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Floneplan depth estimation
Uncertninny- matching
Histogram filter
30m
2(0,5)
Kijoott
20m0
I
attention
Lb,
10ka
monodepoh
probabilhette predictten
Hlorpiaa motching (detall view)
obscrvation
postestor
cucoder
ottention
likelloood
probobility
Figure 2: Main method overview. At timestep i, UnLoc aligns an image with gravity and processes it through a monodepth encoder. The extracted features, along with a binary mask from the gravity alignment, are used to predict the floorplan depth d, and uncertainty b, via masked attentions. These predictions form equiangular rays, allowing for uncertainty-awar matching with the floorplan's 's occupancy map. histogram filter fuses the observation likelihood with the integrated past belief.
ways, and occlusions. Although epistemic uncertainty could help for out-of-distribution scenes, we focus on aleatoric uncertainty for computational efficiency in real-time histogram filtering, leav- ing epistemic modeling for future work. Our Laplace-based formulation enables uncertainty-awan matching, improving robustness and convergence in challenging indoor environments.
FLOORPLAN LOCALIZATION WITH UNLOC
3.1. Method Overview. We estimate the 2D pose = [82,t: Sy,t, 86,2 within a known 2D floor- plan, where Sx,t and Sy,t denote position coordinates and sy,; represents orientation. Our approach takes as input sequence of t RGB images, relative poses between these images, gravity direction, camera intrinsics, and the geometric layout of the floorplan. To ensure out-of-the-box usability of the method, input floorplans are provided solely occupancy grid data without any semantic labels.
An overview our main method is shown in Fig, 2. Each image is aligned with gravity and pro- cessed by pre-trained encoder (Yang et al., 2024b) to extract features. These features, along with a binary mask from the gravity alignment, are input the floorplan depth, defined as the depth to the nearest occupied area in the floorplan. The predicted depth then used to construct equiangular rays, which are matched against the floorplan to produce localization estimate. Finally, we combine this estimate with the previous posterior localization using a histogram filter to compute the updated posterior estimate.
masked attention mechanism that predicts
Our approach differs from prior methods in two main ways: (1) we model floorplan depth plicit probability distribution to quantify uncertainty, enhancing sequential localization when fusing an off- the-lff pre-trained monodepth network instead of custom network
predictions; (2) that requires per-dataset training, thereby effectively leveraging models trained on extensive data without additional training efforts.
Real-world applications often involve images captured from hand-held or head-mounted devices, resulting in arbitrary orientations. To address this, we preprocess images to align them with gravity. We achieve alignment by utilizing the camera's 's roll (w) and pitch (0) angles to define rotation matrices. The rotation for roll is given R. (v)) and for pitch as (!).
3.2. Gravity Alignment.
The combined matrix from the gravity- frame to the camera frame is calculated as Reg (0) R. (1b). The inverse transformation, from the camera frame to the gravity-aligned frame, given by: Rge T Using these rotations, we compute a homography H warp the image into the gravite-aligned frame H = K Rge K-1, where K is the camera intrinsic matrix. This process also produces a binary mask indicating pixels that invalid after alignment due to the warping. The gravity-aligned RGB image and the mask are then used for subsequent feature extraction.
Note that the gravity direction and camera intrinsics usually accessible from sensors smart- phones and head- mounted devices. this information is not directly available, methods such as GeoCalib (Veicht et al., 2024) can be used to estimate both the intrinsies and gravity direction.
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3.3. Feature Extraction. Recent visual floorplan localization methods (Howard-Jenking et al., 2021: Howard-Jenkins & Prisacariu, 2022; Min et al., 2022; Chen et al., 2024a) rely on encoders pre-trained on ImageNet (Deng al., 2009) for image classification tasks, such ResNet-50 (He et al,,, 2016). However, we posit that encoders trained on tasks more closely related to floorplan depth estimation could yield better performance. To explore this, utilize encoders pre-trained dense monocular depth estimation tasks. These encoders, optimized on large-scale depth datasets, provide superior features for floorplan depth estimation without requiring additional depth training from scratch. From the state-of- thtee-of-th-aare ahrt models (Birkl et al., 2023; Guizilini et al., 2023; Yin et al., 2023; Hu al., 2024; Yang al., 2024a;b; Bochkovskii et al., 2024), we select the recent Depth Anything v2 model (Yang et al., 2024b) due to its state-of-the-art performance in both relative and metric depth estimation and low inference times compared to similarly accurate models (Bochkovskii et al., 2024). Specifically, we use the encoder fine-tuned for indoor environments. We extract features from its last layer and apply bilinear interpolation to match the spatial dimensions the subsequent masked attention mechanism requires. Note that the proposed pipeline is agnostic to the monodepth network, which could easily be replaced as better methods are published.
3.4. Masked Attention. We implement masked attention mechanism inspired by Chen et al. (2024a) to predict floorplan depth, using the interpolated features and gravity alignment mask as inputs. Our model predicts two ID vectors: de, representing floorplan depth estimates, and b, denoting the uncertainty associated with each estimate. The uncertainty quantification allows the model to account for varying confidence levels across regions in the image, particularly in challeng- ing areas with ambiguous visual cues or distant objects.
Before inputting to the attention mechanism, we reduce the channel dimensions of the interpolated encoder features using a convolutional layer. The resulting features serve as keys and values in the attention mechanism, while ID queries are formed through average pooling. Positional encodings for the queries are derived from their 1D coordinates, whereas for the keys and values, positional encodings are mapped from the corresponding 2D image coordinates. By applying the gravity align- ment mask, we focus the attention mechanism on observable regions of the image.
The output of the masked attention layer is fed into two parallel fully connected layers: one predict- ing the depth estimates de, and the other predicting the uncertainties b,. This dual output allows for uncertainty-awwe aware matching with the floorplan in subsequent steps.
3.5. Uncertainty-Awarr Matching. Using the predicted depth d, and uncertainty b,, we compute the observation likelihood over the entire floorplan. The predicted uncertainty 6, represents alcatoric uncertainty, capturing the inherent observation noise in floorplan depth estimation that arises from scene ambiguities such as glass surfaces, open doorways, and featureless walls. To formulate the observation model, we treat each predicted depth value as drawn from a probability distribution. We model the predicted floorplan depth and its uncertainty as our observation o, and define the observation likelihood as
p
s.) = II 1=1
1 b...
exp
(_ld.s dz ds bi.)
(1)
2
where de,, and be, are the predicted depth and uncertainty interpolated from de and 6, at ray angle a.j and R. is the number of rays. The corresponding floorplan depth dy (s,) computed from the floorplan ray length as
ds)))) (s:)) cos(a;))
(2)
where ry (8x) is the ray length from pose 8; direction aj. In Eq. 1, the observation likelihood is modeled as a product of independent Laplace distributions, with de, as location parameter and Br.j as scale parameter. We choose the Laplace distribution for two key reasons. First, its heav- ier tails compared to the Gaussian distribution provide robustness to larger prediction errors that commonly occur in challenging indoor scenes (see Sec. A.2.4 for empirical evidence). Second, it enables efficient closed-form likelihood computation essential for real-time histogram filtering.
When uncertainty bi, is high, the distribution becomes flatter, naturally weightiig unreliable observations in the pose estimation process. As result, the filter can rely more confident pre- dictions while remaining robust to uncertain ones. This uncertainty likelihood volume representing the observation likelihoods of all possible camera poses 8t.
matching yields a 3D
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Input image
Floorplan depth [m]
40
GT Loce Ours Uncertainny
20
w/2
w/2
w/2
0
Figure 3: Floorplan depth predictions (in meters) for images from the LaMAR HGE dataset. Top: input images. Bottom: depth predictions by LLoe (red) and proposed UnLoc (blue), with predicted uncertainties visualized. The horizontal axis represents the image column index, ranging from left (0) right (image width w). gray dotted line indicates the ground truth depth.
T- 100,0,
T=50, N=78
T-35, N=111
T=20, N=200
T=15, N-268 @ Im (%)
Model
SR Im (%) 1 RMSE (succ.)
RMSE (all)
SR@ Im (%)
@ @l (5)
@1m (8)
GT Depth
100.0
0.07
0.07
98.7
91.0
76.0
72.0
T.SSE
59.5
0.39
1.96
Loc fusion
94.6
0.12
0.51
94.6
69.4
46.0
41.8
FLoc mono
89.2
0.18
0.88
70.5
55.9
34.0
28.4
Loc mono'
86.5
0.14
0.80
70.5
57.7
35.5
32.1
+ Depth Anything v2
94.6
0.11
0.50
89.7
76.6
60.5
56.3
UnLoc w/o post-processing
97.3
0.16
0.28
92.3
20.5
UnL.oc
97.3
0.16
0.28
94.9
92.8
86.5
81.3
Table I: Sequential localization on the Gibson(t) dataset (Xia et al., 2018). We report the success and the RMSE over the successful and all sequences when the sequence length (T) 100. We report the success rate for all other lengths, considering a localization a success the accuracy of the last 10 frames is within im of the ground truth (GT). We also show the number of sequences tested (N) in each setting. In the first row, we report the localization accuracy with the GT depth. indicates that the F model was trained by us.
3.6. Histogram Filter:
We estimate the posterior probability of the pose over time using a histogram filter, similar to the one introduced by Chen et al. (2024a). At each time step, update the posterior by combining the likelihood with the prior belicf propagated through the motion model. To this, the posterior formulated 3D probability volume and expressed via Bayes' theorem as
p(s, or, t.) Ep(s, p(s: sc-1, p(o, st),
where Z is normalization constant, = [lz,e Ly La,r] is the vector of ego-motion measurements, and p(s; 5.-1. tx) is the transition probability. Let us highlight that the uncertainty 6, the depth prediction directly affects Eq. 3 through its last element: p(o; ss)) (see Eq. 1).
The motion model describes the evolution of the state given the ego-motion measurements and defined as follows:
S = 8 @ t + w1
(4)
where w/ = Wwr,e Wy.t, wa.e] represents Gaussian transition noise with covariance diag(o?, og oz), and @ denotes the state update operation. The transition probability can thus be written
p(s: S:-1, = exp ( stt x 'ss(s - - st.))t))
To efficiently implement the update, the multiplication with the transition probability Eq. 3 is handled using decoupled filters: translation and rotation. See Chen et al. (2024a) for additional

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Published as a conference paper at ICLR 2026
details the filter implementation. The resulting probability volume corresponds to the posterior probability. By sequentially applying the transition and observation updates, we obtain the posterior probability distribution over the camera pose at each time step.
3.7. Training. We train our model by minimizing the negative log-likelihood of the predicted depth and uncertainty with respect to the ground truth (GT) floorplan depth the GT pose. Specifically, we use the following loss:
+ |d: ds)s
La Ld= = Σ log(E:)
(5)
b
where d; and b, are the predicted depth and uncertainty for the 7th image column, (s) is the GT floorplan depth at the GT pose s, and D is the number of image columns for which depth is pre- dicted. This loss corresponds to the negative log- log-likelihood of a Laplace distribution, encouraging accurate depth predictions while accounting for uncertainty. The logarithmic term discourages infi- nite predictions for uncertainty.
3.8. Post- Pros-Procsssing Optimization. While our main method provides accurate camera poses, resid- ual drift and misalignment with local depth measurements may accumulate. To reduce such errors, we perform a lightweight post-processing optimization over the last k frames. We use k = 10 in our experiments. The goal is refine the trajectory by applying global rigid correction, parameter- ized by an SE(2) transformation composed of an in- plane rotation and translation. We initialize the trajectory by computing the last pose as šT arg max sT p(sT OT, tr) and then backpropagating k - 1 steps using the ego-motion measurements and the inverse motion model without noise as
&t $1+1 3 f,+1,
where θ is the inverse state update operation. Let the resulting, estimated trajectory over the last frames be denoted as {8:)2 We introduce a global SE(2) correction As acting on the Xl-plane of the camera poses, parameterized by angle E RR and translation p E IR? as follows:
(0. p) As p) S,, tE - k 1,7].
The optimization minimizes the uncertainty weighted sum of differences between predicted depths de, and floorplan depths de,j at the refined poses (0 p) across all rays of the last & frames as
T
R
Spsat (0, p)
Σ
1 _ bt.j
Ide. d, (5, (1.
(8)
1= k++1
This weighted L11 objective is designed so that frames with higher predicted uncertainty contribute less to the optimization, which is expected help the refinement remain robust noisy predictions. The use of an SE(2) correction keeps the refinement computationally lightweight while enforcing global consistency in the local window of frames. We solve for the optimal correction param- eters (8", (0") arg minø.p Epsse (f.. pp) using a gradient-based optimizer, with floorplan depths d (3, p)) updated at each iteration.
EXPERIMENTS
Datasets. We evaluate our method and the baselines on three datasets: Gibson (Xia et al., 2018), and two versions of LaMAR (Sarlin et al., 2022). The Gibson dataset (Xia et al., 2018) contains 118 synthetic scenes (each smaller than 300m?). Following Chen et al. (2024a), we use two subsets: Gibson (f) for training (24,779 sequences of 4 frames each), and Gibson (t) for testing (118 trajec- tories ranging from 280 to 5, 5,152 steps). features upright camera poses, low to medium occlusion, and large FoV of 108".
To evaluate in real-world settings, we use a subset of LaMAR (Sarlin et al., 2022), focusing on indoor sequences of the HGE building. This large-scale scene covers approx. 22,500 m² The dataset includes camera poses, degrees of occlusion from low to high, and a narrow FoV of 48 consists of 16 sessions totaling 5,187 images, split into 12 sessions for training (3,820 images), one for validation, and for testing. While prior work (Chen et al., 2024a) is only evaluated on a custom the entire scene.
LaMAR:
version of this scene, cropping challenging parts (e.g., long corridors),
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T=100, N=11 RMSE (sacc.)
T=50, N=24
T=35, N=35
T=20, N=63
T=15, N=85
Model
SR@ (%)
RMSE (all)
SR@ Im (5)
@ Il (%)
Il (%)
@ Im (
Depth
100.0
0.20
0.20
91.7
56.5
Original
FLLac mono
36.4
0.45
27.38
16.7
5.7
1.6
1.2
Depth Anything v2
100.0
0.38
0.38
66.7
23.8
UnLoc w/o post-processing
100.0
0.34
0.34
75,0
60.0
36.5
20.0
UnLoc
100.0
0.25
0.25
75.0
74.3
63.5
50,6
Denth
100.0
58.1
Coopped
Loc mono
75.0
0.55
1,93
29,7
3.7
"2.2
0.0
Depth Anything
16.1
Unl oe w/o post-processing
100.0
0.45
01:45
94.1
76.9
50.0
33.9
UnLoc
100.0
0.41
0,41
94.1
88.5
71.7
62.9
Table 2: Sequential localization on the LaMAR HGE dataset and on its cropped version used by Chen et al. (2024a). We report the success rates (SR) and RMSE over the successful and all sequences when the sequence length (T) is 100, and the SR for all other lengths, considering localization a success if the accuracy of the last 10 frames within of the GT. We also show the number of sequences tested (N). The first row reports the localization accuracy with the GT depth.
Gibson(t)
LaMAR HGE
Model
Extraction
Matching
Extraction
Matching
Lox fusion
0.030s
0.003s
Lox mono
0.015s
0.003s
Loc mono"
0.015s
0.003s
0.089s
0.797s
Depth Anything v2
0.185s
0.003s
0.179s
0,746s
UnLoc
0.174s
0.004s
0.185s
0.880s
Table 3: Runtime in secs on the Gibson (Xia et al., 2018) and LaMAR HGE datasets (Sarlin et al., 2022). We independently show the depth prediction time per frame and match it the floorplan.
Baseline. We compare to Loc (Chen et al., 2024a) as is the state-of-the-an art floorplan localization approach. We use their pre-trained models and also train them ourselves with the provided code to make them applicable to the LaMAR dataset. While we consider its purely monocular version LF& mono) as our main competitor, we also show results for L fusion, which uses both monodepth and multiview stereo. Let note that method can also easily benefit from multiview stereo. Additionally, to study the individual contribution of our uncertainty-awaat depth prediction, evaluate approach without post-processing.
Metrics. We report the success rate (SR) and consider sequential localization at X meters successful if the prediction is within radius of X meters over the last 10 frames. We compare SR for various numbers of frames. Also, we show the RMSE (over the last 10 frames) of our trajectory tracking in both succeeded and all runs.
Results on Gibson(t). Table reports results on the synthetic Gibson(t) dataset. We evaluate SR (%) and RMSE (in meters) over both successful and all sequences for length- sequences, and report SR for other lengths, considering localization successful if the accuracy over the last 10 frames is within meter of the ground truth (GT). The number of sequences is also shown. The first row gives the upper bound performance using GT depth. Note that
can still improve upon it.
Without prot-procsssing, our method already achieves substantial gains over both the original Loc and its variant using DepthAnything v2 for depth prediction, thanks to uncertainty modeling. Post- processing further boosts performance, especially on short sequences. For instance, on 15-frame se- quences, it yields 16-point SR improvement, even surpassing the GT--dethh version. Compared to Loc mono, our method achieves a 52.9 52.9-point SR gain. Importantly, UnLoc maintains an SR above 80% even on 15-frame sequences, whereas Loc (with either the original encoder or DepthAny- thing) requires at least 50 frames to reach this level. Excelling on short sequences is particularly relevant for real-world applications, where time-to-localize is critical.
For completeness, we also include LASER (Min et al., 2022) results (as reported in Chen et al. (2024a)), which lag behind both Loc and UnLoc by wide margin.
Results on LaMAR. Table 2 reports results on the LaMAR HGE dataset. Since F does not provide pre-trained model for this dataset, we evaluate it using model trained by LaMAR is a challenging real-world dataset with both small environments (offices and rooms) and large ones (halls and long corridors). The monodepth predictor of L struggles in these conditions, yield-
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Model
SR@ Im (%;; T=100)
(T=50) f (T=35)
(T=20) ^ (T=15) t
GT Depth
100.0
100.0
57.1
53.8
22.2
Loc mono
0.0
0.0
0.0
0.0
0.0
Unlooc w/o post-processing
50.0
20.0
0.0
15.4
0.0
UnLoc
50.0
40.0
57.1
30.8
16.7
Table 4: Localization on the LaMAR CAB dataset with models trained on LaMAR HGE. We report the success rates (SR) for sequence lengths 100, 50, 35, 20, and 15.
ing very low success rates even for long sequences. Replacing the encoder of Loc with Depth Anything v2 already yields more accurate results than the original model.
The proposed UnLoc, equipped with uncertainty modeling and envertainty-weighhee post- processing, brings substantial gains. Compared to Loc, the success rate rises from 1.2% to 50.6% (a 42.2-fold improvement) on 15-frame sequences and from 36.4% to 100% (a 2.7-fold improve- ment) 100-frame sequences. On the cropped dataset variant (Chen et al., 2024a), success rates increase by 25.0 to 80.8 percentage points, depending on the sequence length. Both uncertainty mod- cling and post-proced post-processing contribute significantly to these improvements. These results demonstrate that UnLoc markedly improves upon the state of the art in complex, realistic scenarios by leveraging off-the-shelf depth predictors with uncertainty estimation. The improvements complementary and each component contributes to the overall accuracy.
Figure shows examples of floorplan depth predictions and uncertainties. UnLoc consistently out- performs Loc on these challenging cases. Notably, in the last example, our method returns the correct depth while the ground truth incorrect.
Table 4 presents results on the LaMAR CAB building using models trained on the LaMAR HGE dataset. While this is not strict zero-shot scenario since the same sensor is used in differ- ent building it effectively demonstrates the generalization capabilities of UnLoc. Our approach achieves accurate localization this new environment, whereas Loc fails completely. We pro- vide additional results on this cross cross-domain task in the supp. mat (see Sec. A.2.2).
Runtime. Table presents the average runtime per frame for each method. As expected, using an off-the-shelff depth prediction network incurs higher computational cost than the custom network used in Chen et (2024a). On the Gibson dataset, all methods run efficiently, though our proposed method is slightly slower due to the more complex depth predictor. On the LaMAR dataset, all methods require approximately one second per frame. In practical applications, a lower frame rate is acceptable since new frames may not always provide significantly new information at high rates. Also, UnLoc excels on short sequences, achieving accurate localization with fewer frames. The post-processing takes, on average, 0.96 seconds
the end of the process.
Summary. The results on both synthetic and real-world datasets confirm that UnLoc outperforms the state of the art in terms of accuracy and robustness. We achieve significant improvements in sequential visual floorplan localization by addressing the limitations of the state of the art and lever- aging uncertainty-aware depth predictions from pre-trained models. UnLoc maintains real-time performance while enhancing scalability and generalization to new scenes.
4.1 ABLATION STUDIES
Monocular Depth Networks. We further examine the impact of different encoder networks on our method's performance using the LaMAR HGE dataset (Table 5). Specifically, we evalu- ate the general-purposs. encoder DINOv2 (Oquab et al., 2024), the monodepth encoder DepthPro (Bochkovskii et al., 2024), and three encoder variants of Depth Anything v2 (Yang et al., 2024b) large (L.), base (B), and small (S). The results show that the monodepth encoders DepthPro and Depth encoder DINOv2, with Depth Any- thing v2 (L) achieving the highest overall performance. This indicates that pre-trained monodepth encoders provide more effective features for floorplan depth prediction. Within the Depth Anything v2 models, performance scales positively with model size. Importantly, incorporating the proposed depth uncertainty estimation consistently improves success rates across all encoders. For instance, incorporating depth uncertainties with the hase model elevates its performance match that of the
Anything v2 (L) outperform the similarly-sizer general-

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Published as a conference paper at ICLR 2026
Model
T=100
T=50
T=35
T=20
T=15
DINOv2 (L) DINOv2 (L) w/ Uncertainty
90.9
45.8
20.0
9.5
3.5
100.0
54.2
31.4
15.9
5.9
DepthPro
90.9
62.5
40.0
17.5
4.7
DepthPro Uncertainty
100.0
70.8
51.4
31.7
14.1
Depth Anything V2 (L)
100.0
66.7
42.9
23.8
9.4
Depth Anything V2 (L) w/ Uncertainty
100.0
75.0
60.0
36.5
20.0
Depth Anything V2 (B)
90.9
62.5
28.6
12.7
2.4
Depth Anything V2 (B) w/ Uncertainty
100.0
66.7
42.9
30.2
12,9
Depth Anything V2 (S)
81.8
41.7
22.9
7.9
4.7
Depth Anything V2 (S) w/ Uncertainty
100.0
54.2
37.1
17.5
5.9
Table 5: Sequential localization with UnLoc using different encoders on LaMAR HGE (Sarlin et al., 2022). Results without processocss. Success rate for different sequence lengths (local- ization is a success if the last 10 frames are within 1m of GT). We compare DINOv2 (Oquab et al., 2024) with PrpthPro (Bochkovskii et al., 2024) and different pre-trained models of Depth Anything V2 (Yang et al., 2024b) (L: large, B: base, S: small), both with and without uncertainty estimation.
large variant. This demonstrates that uncertainty estimation effectively compensates for smaller model sizes, enhancing robustness without incurring additional computational costs.
Efficiency Analysis. Fig. illustrates the SR on the Gibson(t) dataset versus model size (left)
Our (L)
Ours (I))
Ours
Oure (B)
and runtime (right). To show a fair model com-
SWATT- 22-2 (95)
0.5
Oum (8)
Outs (9)
parison, we do not perform post-processing op- timization here. Our method improves sub- stantially over Loc even with smaller mod- els: Using the small Depth Anything v2 model, it achieves about 10 percentage point higher success rate compared to Lo fusion, while maintaining similar runtime. This highlights the efficiency of our approach in terms of ac- curacy versus computational resources.
0.7
Lox fusiom
Loe
0.6-
F PLae mose
Loc mocin
11.5
250
500
750
1000
1250
0,00
0.06
0.10
0.15
0.20
Medel size (MB)
Rantime
(6)
Figure 4: Efficiency Analysis. Performance of sequential localization versus model size (left) and runtime (right) 6000 GPU. The success rate (SR) defined as the percentage of sequences of length T = 25 for which the posterior remains within an error radius of Im in the 10 frames. The values are averaged over all test sequences from the Gibson(t) dataset.
NVIDIA Quadro RTX
CONCLUSION
We propose a visual floorplan localization method, UnLoc, that addresses key limitations of prior approaches by incorporating uncer-
tainty estimation into depth prediction and leveraging pre-trained monodepth models. Modeling depth predictions as probability distributions allows us to quantify uncertainty, leading to improved accuracy, especially in challenging environments. Experiments on both synthetic and real-world datasets demonstrate that our method significantly outperforms state-of-the-art approaches in terms of accuracy and robustness while operating efficiently. Our approach offers a practical and scalable solution for visual floorplan localization across diverse indoor environments.
REPRODUCIBILITY STATEMENT
We have taken several steps to ensure reproducibility. The code required for data preprocessing, model training, model evaluation, along with trained models, is publicly available at https:// github, com/ homm/matthin wueest /UnLoc. Training procedures are described in A.1.1 and dataset creation details are provided in A.1.2.
Acknowledgments. Francis Engelmann acknowledges support from SNSF PostDoe mobility fellowship. This research was also supported in part by an academic gift from NVIDIA and Meta. The authors gratefully acknowledge this support.
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A APPENDIX
This section provides further implementation details (Sec. A.1), as well as additional quantitative (Sec. A.2) and qualitative results (Sec. A.3).
A.1 ADDITIONAL IMPLEMENTATION DETAILS
A.1.1 TRAINING DETAILS
For our evaluations, train the models the full training split of the respective dataset for 50 epochs. The training set is shuffled at the beginning of each epoch to ensure variability, and the models are trained using batch size of four. We employ Adam (Kingma, 2014) with a fixed learning rate of 10-1
A.1.2 DATASET DETAILS: LAMAR HGE AND CAB
To tailor the LaMAR dataset (Sarlin et al., 2022) for our experimental requirements, we apply series of preprocessing steps and customizations, following a similar strategy to Chen et al. (2024a). The dataset originally includes images captured by iOS and Hololens devices. We restrict ourselves to the images recorded by the iOS devices from the mapping part of the dataset. We filter the dataset to retain only the indoor poses, excluding outdoor data. For the LaMAR HGE Complete dataset, we utilize all indoor camera poses recorded at the HGE location. In contrast, the LaMAR HGE Cropped dataset focuses smaller region, specifically reduced area around the center of the
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HGE floorplan. To avoid transitions between different floorplans in the LaMAR CAB dataset, restrict the data to a single floor, specifically floor F of the CNB building.
We use the official building floorplans basis and apply selected manual modifications. Room numbers, stairs, and doors connecting two corridors that are typically open are removed, while all other doors represented as solid walls. For the mapping of LaMAR 6 DoF poses to 2D floorplans we proceed as follows: We identify four significant building entrance doorways visible in the image sequences, positioned to span the entire floorplan area. For each doorway, we determine: (1) the LaMAR ground truth (x, y) position in meters when the camera crosses the threshold, and (2) the corresponding pixel location of that doorway in the floorplan. The pixel positions are manually marked by visually identifying doorway locations in the architectural floorplan and examining the images determine when the camera crosses each threshold. We then fit 2D affine transformation (6 parameters, 8 constraints from 4 point pairs) mapping meter coordinates to pixel coordinates using least squares. This transformation is applied to all ground truth positions. Yaw angles are extracted directly from the ground truth quaternions. While we lack independent ground truth for the 2D poses, multiple consistency checks suggest the alignment reliable: low affine fit residuals, trajectories align with building geometry, and raycast depths match observed structure. Using these 2D poses, the ground truth floorplan depths required for training are derived from the modified floorplans via the raycasting method from Chen et al. (2024a).
The splits are fixed prior to experimentation and are not adjusted in any way to favor any method. For LaMAR HGE, the split includes 12 sessions used for training, one for validation, and three for testing. The test sessions were selected with the following goals: First, to include the session illustrated in Figure 11 of Loc Chen al. (2024a) for qualitative comparison, and second, to ensure diversity across lighting and occlusion conditions. The three test sessions therefore span (i) daytime with moderate occlusion, (ii) daytime with heavy occlusion due to an ongoing exhibition, and (iii) nighttime with minimal occlusion. For LaMAR CAB, the split includes I session for training (only in case of fine-tuiing) and for testing. Here, all sessions had similar daytime and occlusion.
A.2 ADDITIONAL QUANTITATIVE RESULTS
A.2.1 SINGLE-FRAME LOCALIZATION
In this section, we evaluate the performance of each method in single-frame localization tasks. While our primary focus is sequential localization, analyzing single-frame performance provides valuable insights into the individual components of our approach.
Table 6 presents the results on the Gibson(t) (Xia et al., 2018), Structured3D (Zheng et al., 2020), and LaMAR HGE (Sarlin et al., 2022) datasets. We report the localization recall, i.e., the percentage of frames localized within specified accuracy thresholds. Across all datasets, making use of an off- the-shelf monodepth encoder instead of a custom depth predictor leads to substantial improvements in all metrics. Specifically, L with Depth Anything v2 outperforms the original Loc by significant margin in terms of both mean absolute error (MAE; in meters) and localization recall. This demonstrates the effectiveness of leveraging pre-trained depth models.
As anticipated, incorporating depth uncertaintics into the single-frame estimation does not have a large impact the Gibson(t) and Structured3D datasets. This be attributed to the fact that uncertainty estimation is particularly beneficial when combining multiple measurements over time, in sequential localization. However, the challenging LaMAR dataset, which features complex environments with varying occlusions and narrow fields of view, uncertainty estimation leads to improvements in recall across most thresholds. This suggests that uncertainty modeling helps with handling complex and real-world scenarios.
A.2.2 GENERALIZATION BETWEEN DATASETS
To demonstrate the generalization capabilities of our proposed UnLoc, we train models the LaMAR HGE dataset and assess their performance on LaMAR CAB. The evaluation includes a comparison between UnLoc and L mono across various scenarios, with and without fine-tuning on a limited number of frames (n=200) from the LaMAR CAB dataset. The results, presented in Ta- ble 7 and 8, demonstrate that UnLoc without any additional training on the target domain converges
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within 2m of the ground truth (GT) for trajectories of length T=100, outperforming the Loc mono variants. Furthermore, the results show that fine-tuning on a small amount of target-omaann data leads to performance increases in both the sequential and single-image localization. This indicates a certain adjustment to new datasets is still necessary for optimal results.
A.2.3 REAL-TIME CONSIDERATIONS FOR LARGE-SCALE FLOORPLANS
As shown in Table of the main paper, matching operations dominate the runtime for large floor- plans. To further reduce the computational cost on large-scale floorplans, we investigate two strate- gies: (1) decreasing the frequency of observation updates, performing them only after every AT transition updates, and (2) employing coarser grid for the matching with the floorplan.
Table presents the sequential localization performance on the LaMAR HGE dataset with less frequent observation updates. Evaluating our proposed UnLoc trajectories of length T=100, halving the observation frequency has no negative impact on localization accuracy. Even when observation updates are performed every 10th step, UnLoc achieves convergence in nearly every second case. Results are shown without post-processing optimization.
An ablation study on grid resolution is provided in Table 10. The uncertainty-aware matching stage of the UnLoc pipeline compares probabilistic floorplan depth predictions with occupancy infor- mation from the floorplan. The computational cost and mapping quality in this stage are directly influenced by the grid resolution. A finer grid enhances mapping quality but leads to increased com- putational cost. our experiments in the main paper, we assume grid resolution of 1m x 0. The results show that coarsening the grid to 0.25m x 0.25m reduces the matching time by factor of over six, at the expense of only slight decrease in localization performance.
A.2.4 DEPTH UNCERTAINTY DISTRIBUTION
We compare two approaches for modeling depth uncertainty in UnLoc: a Laplacian model (ours) and a Gaussian model, as proposed by Roessle et al. (2022). In the Gaussian model, the obser- vation likelihood in Eq. is modified to a product of independent Gaussian distributions instead of Laplacian distributions, with de,, as mean parameter and big as standard deviation parameter. Correspondingly, the loss function in Eq. 5 is adapted to reflect the negative log-likelihood of the Gaussian distribution rather than the Laplacian distribution.
Table 11 reports the sequential localization performance the LaMAR HGE dataset for vary- ing sequence lengths T. Although both models achieve perfect success rate for long sequences (T = 100), our Laplacian model consistently outperforms the Gaussian model on shorter sequences, demonstrating superior convergence behavior.
A.3 ADDITIONAL, QUALITATIVE RESULTS
A.3.1 OBSERVATION LIKELIHOOD
qualitative comparison of the predicted observation likelihoods between Loc and UnLoc on the Gibson(t) dataset is presented in Figure 5. As shown, our UnLoc approach consistently delivers more accurate predictions than L Moreover, UnLoc with uncertainty takes advantage of predicted low uncertainties in floorplan depth estimates, enabling it to more effectively reduce the observation likelihood for less probable states.
A.3.2 POSTERIOR PROBABILITY
Figure 6 compares the posterior probability evolution of UnLoc with those of L mono and Loc fusion across three trajectories from the Gibson(t) dataset. In all three cases, UnLoc outper- forms both baselines, achieving substantially faster convergence to the true pose. Notably, UnLoc converges within five or fewer frames in each trajectory, result that generally holds for around two thirds of the trajectories of the Gibson(t) dataset (without post-processing ) as shown in Table 1 of the main paper.
16

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Published as a conference paper at ICLR 2026
Depth pred.
Recall (%)
Model
MAE (m)) J Cos Sim
0.1m
0.5m
Im
Im 30'
2m
5m
10m
GT Depth
0.00
1,000
47.1
79.0
80.3
79.8
82.4
90.6
98.5
Gibsontt)
Loc fusion
0.28
0.980
15.6
53.6
59.1
S7.6
64.4
82.7
97.3
Loc mono
0.43
0.976
7.3
40.3
49.1
47.3
55.5
79.6
97.7
Loc mono"
0.42
0.973
8.2
39.2
46.5
44.6
54.5
79.7
97.7
Depth Anything v2
0.23
0,981
19.3
62.5
66.5
65.6
70.6
85.2
98.1
UnL.oc
0.25
0.981
19.7
61.1
64.7
63.8
68.7
84.4
97.6
GT Depth
0.00
1.000
32.2
63.4
65.0
64.3
68.6
81.6
96.4
LaMAR Strittuee3D
Pff.net (copied from Chien af. (3024n))
0.
11.3
3.2
0.9
LASER (copied from Chen al. (2024a))
0.7
6.4
10.4
8.7
Loc mono (copied from Chen et al. (2024a))
1.5
14.6
22.4
21.3
Loc mono'
0.51
0.971
27.7
26.5
35.1
62.6
94.3
Depth Anything v2
0.37
0.979
5,5
34.2
40.4
39.3
45.8
68.0
94.9
UnLoc
0.39
0.979
5.3
33.9
38.8
37.6
44.9
68.3
95.1
Depth
1,000
14.3
44.7
49.2
49.5
52.5
56.3
Loc mono"
3.29
0.930
0.0
0.1
0.8
0.8
1.2
3.4
8.6
+ Depth Anything v2
2.08
0.957
0.5
6.8
11.3
11.3
13.9
17.6
22.8
UnLoc
2.02
0.958
0.4
11.3
17.1
17.1
19.2
22.9
27.5
Table 6: Single-frame localization the Gibssn(t) (Xia al., 2018), LaMAR HGE (Sarlin al., 2022), and Structured3l) (Zheng et al., 2020) datasets. We report the mean absolute error (MAE) in meters and the cosine similarity (Cos Sim) of the depth feature embeddings assess the accuracy of the depth prediction. For camera localization accuracy, we report the recall (%) at various distance thresholds. The first row in each dataset section shows the localization performance using ground truth (GT) depth, providing an upper bound. indicates that the Loc model was trained by us.
T=100, N=2
T=50, N=5
T=35, N=7
T=20, N=13
T=15, N=18
Model
SR@2m (%)
RMSE (succ.)
RMSE (all)
SR 2m (%)
(22m (R6
@2m (8)
@2m (
GT Depth
100.0
0.09
0.09
100.0
57.1
53.8
22.2
F3Loc miono"
'..0"
nan
14.90
0.0
0.0
0.0
0.0
F3Loc mono" (ft: 5 cp.)
0.0
nan
6.86
20.0
0.0
0.0
0.0
F3Loc mono"
ep))
50.0
UnLoc
100.0
0.71
0.71
40.0
57.1
30.8
16.7
UnLoc (ft: s.p)))
100.0
0.42
0.42
100.0
85.7
30.8
33.3
UnLoe (ft: ep.)
100.0
0.35
0.35
100.0
71.4
38.5
22.2
Table 7: Sequential localization on the LaMAR CAB dataset (Sarlin et al., 2022) with models
trained on LaMAR HGE. We report the success rates (SR) and the RMSE over the successful and all sequences when the sequence length (T) is 100. We report the success rate for all other lengths, considering a localization a success if the accuracy of the last 10 frames is within 2m the ground truth (GT). We also show the number of sequences tested (N) in each setting. In the first row, report the localization accuracy with the GT depth. "ft" indicates the model was fine-tuned on the LaMAR CAB dataset (Sarlin al., 2022) for the specified number of epochs (ep.). indicates the Loc model was trained by us.
Depth pred.
Recall 1
Model
MAE (m) J
Cos Sim
0.1m
0.5m
Im
Im 30
2m
5m
10m
GT Depth
0.00
1.000
17.5
32.5
33.0
33,0
36.0
42.0
50.5
F3Loc m
2.79
0.892
0.0
0.0
0.5
0.0
1.0
3.5
12.5
F3Loc mono" (ft: cp.)
1.47
0.940
0.0
0.5
1.0
0.5
3.5
7.5
20.0
F3Loc mono" (ft: 10 ep.)
1.52
0.940
0.0
0.5
1.0
1.0
2.5
7.5
19.0
UnL.oc
1.39
0.970
0.0
1.0
2.5
2.5
3.5
9.0
24.0
UnLoc (ft: cp.)
0.78
0.974
0.0
4.0
9.0
8.0
11.0
18.0
30.0
UnLoc (ft: 10 ep.)
0.79
0.971
0.0
3.0
5.5
5.5
10.0
20.0
30.0
Table 8: Single-frame localization on the LaMAR CAB dataset (Sarlin et al., 2022) with models
trained on LaMAR HGE. We report the mean absolute error (MAE) in meters and the cosine simi- larity (Cos Sim) to assess the accuracy of the depth prediction. For camera localization accuracy, we report the recall (%) at various distance thresholds. The first row shows the localization performance using ground truth (GT) depth, providing upper bound. "ft" indicates the model was fine-tuned on LaMAR CAB for the specified number of epochs (ep.). indicates that the F Loc model was trained by us.
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Published as a conference paper at ICLR 2026
SR@
Im
(%)
Model
At=1
At=2
At=4
A==10
GT Depth
100.0
100.0
100.0
45.5
F3Loc mono*
36.4
27.3
18.2
0.0
UnLooe w/o post- posteprocessing
100.0
100.0
72.7
45.5
Table 9: Sequential Localization on LaMAR HGE dataset (Sarlin et al., 2022) with less frequent observation updates. We report the success rates (SR) for sequence length (T) of 100 and different interval lengths between observations (At), We consider localization success if the accuracy of the last 10 frames is within Im of the ground truth (GT). In the first row, we report the localization accuracy with the GT depth. indicates the Looc model was trained by us.
SR@ 1m (%)
1
Timing (s)
Grid resolution
T=100
T=50
T=35
T=20
T=15
Feat, extr. Matching
0.1m 0.1m
100.0
75.0
60.0
36.5
20.0
0.185
0.880
0.25m x 0.25m
90.9
70.8
57.1
30.2
15.3
0.185
0.140
Table 10: Sequential localization and timing of our UnLoc approach without post-processing the LaMAR HGE dataset (Sarlin et al., 2022) for different grid resolutions. We report the success rates (SR) various sequence lengths (T). We consider localization success if the accuracy of the last 10 frames is within Im of the ground truth (GT). We report the runtimes (feature extraction and matching) averaged all test sequences
NVIDIA Quadro RTX 6000 GPU.
Model
SR @ (%; T=100)
(T=50) f
(T=35) f (T=20)
(T=15)
UnLoc w/ Laplacian
100.0
54.2
37.1
17.5
4.7
UnLoc w/ Gaussian
100.0
41.7
20.0
6.3
0.0
Table 11: Sequential Localization with UnLoc using different uncertainty models. Comparison of depth uncertainty distributions on LaMAR HGE (Sarlin et al., 2022) without post-processing using a Depth Anything v2 (Small) depth network. Success rates (SR) are reported for varying sequence lengths T.
Ours wio Uncertainty
Ours w/ Uncertainty
Image
Floorplan
PLoc
Ground tuth Prediction
Figure 5; Observation likelihoods.
18

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Published as a conference paper at ICLR 2026
t-0
1-5
1- 15
20
35
Ground truth Prediction
Lo
PLac ftssion
Und.oc (ours))
P'Loc
FLoc fasion
Unl
1=0
t=2
t=
15
35
1
Pianc lusionn
UnLac (ours)
Figure 6: Posterior evolution.
19


Reviews:
[{"review_id": "Wov6BgVh6h", "reviewer": "Reviewer_wqUb", "summary": "The paper presents a method for floor plan localisation based on a sequence of input images. Its main contribution lies in introducing an uncertainty-aware depth estimation framework and replacing customised depth models with modern monocular depth networks. As a result, the proposed approach outperforms prior methods on two datasets, LaMAR and Gibson.\n\nStrength: The idea of incorporating uncertainty into the depth matching process is novel and technically well-motivated. The experimental results convincingly support the method’s effectiveness.\n\nWeakness: The evaluation lacks results on the Structured3D dataset, which is used by the closely related baseline F3Loc. Moreover, replacing a customised depth network with a modern monocular model, while beneficial, is a relatively straightforward modification.", "questions": "1. Could you include results on the Structured3D dataset?\n2. Are there any challenges in replacing the customized depth network with monocular depth networks?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The idea of introducing uncertainty into the depth matching process is novel and technically sound.\n2. The experiments are comprehensive and largely convincing.\n3. The paper is well written and easy to follow.", "weaknesses": "1. The results on the Structured3D dataset are missing, which is used in the closely related baseline F3Loc.\n2. Replacing the customized depth networks with modern monocular depth networks is relatively straightforward.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YQMX7GpRLG", "reviewer": "Reviewer_wgVS", "summary": "The paper proposes a method for visual floorplan localization that integrates uncertainty-aware monocular depth estimation. Specifically, the authors leverage large-scale pre-trained monocular depth models (e.g., Depth Anything) to (1) enhance generalization to unseen environments, and further (2) incorporate weighting on depth predictions based on their estimated confidence/reliability. Experimental results show substantial gains in both accuracy and robustness over existing methods.", "questions": "- Is this depth encoder being fine-tuned during training? If it is, wouldn’t this make the approach similar to F3Loc, which requires model training for each environment?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Simple yet sound idea that leads to performance improvements.\n- This presentation is clear and easy to follow, with figures that are mostly intuitive and effectively support the explanations.\n- The evaluation section is thorough – Table 1 and 2 showing sequence localization on Gibson and LaMAR HGE datasets, respectively. The author also reports the runtime which is crucial for localization tasks. Furthermore, the paper also ablates different depth estimation models and validates the effectiveness of the proposed uncertainty estimation.", "weaknesses": "- The overall technical contribution is somewhat limited, as the main novelty lies in replacing the F3Loc depth networks with SOTA monocular depth estimation models. Moreover, uncertainty modeling in depth estimation has already been explored in many prior works [1, 2].\n- Lack of discussion about the uncertainty in the related works or preliminary. The author simply throws the equation at Sec3.5 without sufficient explanation of its derivation or references to prior studies that motivated it.\n\n[1] Kendall and Garl, What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?\n\n[2] Poggi et al., On the uncertainty of self-supervised monocular depth estimation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QT1AthdUxO", "reviewer": "Reviewer_Lj7y", "summary": "This paper presents UnLoc (Uncertainty-aware Localization), a method for sequential camera localization within 2D floorplans. The input of the method is: \n1/ a sequence of t RGB images,\n2/ relative poses between these images,\n3/ gravity direction \n4/ camera intrinsics, and\n5/ geometry layout of the floorplan\n\nIt aims to address two key shortcomings of previous state-of-the-art methods: the lack of uncertainty modeling in depth predictions and the need for custom, environment-specific depth networks for each environment (e.g. in $F^3Loc$). The paper reports strong performance compared to previous methods on LaMAR HGE dataset.", "questions": "n/a", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "(+) Strong performance shown in the experiment section, although the paper mainly focuses on LaMAR HGE dataset\n\n(+) Compared to the previous method, such as $F^3Loc$, the proposed method is more general, in the sense that it does not rely on per-scene depth estimator, which significantly improves the usability of such methods\n\n(+) the writing is the paper is good -- it is relatively easy to the reviewer to follow\n\n(+) Using a depth encoder seems new to me and makes a lot of sense", "weaknesses": "(-) Biggest concern from me is the lack of novelty and suitability in the sense of the ICLR (learning representation) community. The paper is a 3D computer vision problem in pose estimation in a room. The main contributions / problems to improve from previous methods are 1/ the lack of uncertainty modeling in depth predictions and 2/ the need for custom, environment-specific depth networks for each environment. These are very specific novelties for this particular task, instead of any major novelty for the learning representation community\n\n(-) The proposed method feels like using / combining a few existing tools/engineering techniques (DepthAnything, Rotate/Translate the images, Floorplan matching, and heavy post-processing etc) and made it work for a particular task. This feels more suitable for submission to a 3DV conference, CVPR or robotics conference, instead of a machine learning conference\n\n(-) The performance of the method will be limited by DepthAnything's accuracy, so this paper essentially trades accuracy ceiling with generalizability\n\n(-) the experiments focus heavily on a small dataset", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YAJHmZyQ4d", "reviewer": "Reviewer_PYiR", "summary": "This paper proposes combining pre-trained depth models with uncertainty modeling to improve the accuracy of floor-plan localization. Experiments are conducted on multiple datasets, including both real-world and synthetic scenes.", "questions": "I am particularly curious about how this work is evaluated on the LaMAR dataset. To my knowledge, LaMAR does not include publicly available floor plans. Could the authors clarify where the floor plan data used in this paper originated? The description in the Appendix is quite brief and would benefit from a more detailed explanation. \n\nMoreover, LaMAR does not provide ground-truth poses for the test set, and the paper does not specify the session divisions used. Since the evaluation is based on estimating 2D poses on floor plans, it remains unclear how these poses were submitted or compared within the LaMAR benchmark to produce the results shown in Table 2. Additionally, even if the official floor plans were obtained, how were the 6Dof camera poses from LaMAR aligned with the 2D pose in the floor plans? If you use custom's align algorithm to calculate the ground-truth label of 2D poses, how can you ensure that this ground-truth itself is reliable?\n \nA detailed explanation of the data processing pipeline and evaluation protocol would be very helpful. \n\nI also noted that F3Loc did not include such evaluation details—it only released the link to the Gibson Floorplan Localization Dataset. This raises further concerns about the fairness and transparency of comparing F3Loc and the proposed method on the LaMAR HGE/CAB benchmark. Overall, this area still lacks openly available datasets and standardized baselines, making reproducibility a critical issue at this stage.\n\nIf the authors could provide a more comprehensive description of the evaluation methodology and experimental setup on LaMAR, I would consider improving my overall rating. Furthermore, I strongly encourage the authors to release the processed version of the LaMAR data used in this work. Since the paper involves substantial preprocessing of the raw LaMAR dataset, referring to it as “widely used” without sharing those processed resources seems somewhat inappropriate and limits the community’s ability to validate the claimed improvements in accuracy and reproducibility.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 1, "strengths": "1. The paper is generally well written and easy to follow.\n\n2. It makes sense to leverage uncertainty information for localization.\n\n3. The proposed method achieves state-of-the-art performance on two floor-plan localization benchmarks.", "weaknesses": "1. The paper mainly integrates existing methods and applies them to a relatively narrow task—floor-plan localization. While the topic is relevant, the contribution appears incremental rather than conceptually novel. This paper does not introduce any new contribution in terms of problem formulation; it focuses on a narrow task with clearly defined inputs and outputs.\n\n2. As shown in Table 6, F3Loc + Depth Anything V2 already achieves higher accuracy than UnLoc on several datasets. This suggests that much of the performance gain may stem from the use of off-the-shelf pre-trained depth foundation models rather than from the new proposed method in this paper. The paper, therefore, reads more as an application of pre-trained depth models to a specific downstream task than as a novel algorithmic/theory contribution. \n\n3. The discussion on uncertainty is underdeveloped. Uncertainty in neural network predictions can generally be categorized into aleatoric and epistemic uncertainty [1], yet this paper lacks a theoretical explanation or analysis of which type is modeled or how it contributes to performance improvements.\n\n4. The data preprocessing and benchmark setup are not sufficiently transparent. The paper appears to introduce newly processed data and custom split strategies from public datasets, which raises concerns about reproducibility. It is unclear whether the test set division used in this work aligns with the official benchmark split and whether the authors may have chosen a split that yields the best performance. Providing clear details on dataset handling and evaluation would significantly improve the credibility of the results. This paper does not provide the recommended Reproducibility statement and the Use of Large Language Models (LLMs) statement. \n\n\n[1] Alex Kendall et al., What uncertainties do we need in Bayesian deep learning for computer vision?, NeurIPS 2017.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)