A Scene is Worth a Thousand Features: Feed-Forward Camera Localization from a Collection of Image Features
Authors: Axel Barroso-Laguna
Tommaso Cavallari
Victor Adrian Prisacariu
Eric Brachmann
Venue: ICLR 2026 Poster (2026)
Keywords: Visual Re-localization
Relative Pose Regression
Pose Estimation
Visual Localization
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose FastForward, a novel approach that achieves fast mapping and localization through a single feed-forward pass.

Abstract: Visually localizing an image, i.e., estimating its camera pose, requires building a scene representation that serves as a visual map. The representation we choose has direct consequences towards the practicability of our system. Even when starting from mapping images with known camera poses, state-of-the-art approaches still require hours of mapping time in the worst case, and several minutes in the best. This work raises the question whether we can achieve competitive accuracy much faster. We introduce FastForward, a method that creates a map representation and relocalizes a query image on-the-fly in a single feed-forward pass. At the core, we represent multiple mapping images as a collection of features anchored in 3D space. FastForward utilizes these mapping features to predict image-to-scene correspondences for the query image, enabling the estimation of its camera pose. We couple FastForward with image retrieval and achieve state-of-the-art accuracy when compared to other approaches with minimal map preparation time. Furthermore, FastForward demonstrates robust generalization to unseen domains, including challenging large-scale outdoor environments.

Published as a conference paper at ICLR 2026
A SCENE IS WORTH A THOUSAND FEATURES: FEED-FORWARD CAMERA LOCALIZATION FROM A COLLECTION OF IMAGE FEATURES
Axel Barroso-Laguna, Tommaso Cavallari* Victor Adrian Prisacariu Eric Brachmann" 1
Niantic Spatial University of Oxford
ABSTRACT
Visually localizing an image, i.e., estimating its camera pose, requires building a representation that serves visual map. The representation choose has direct consequences towards the practicability of our system. Even when starting from mapping images with known camera poses, state-of-the-art approaches still require hours mapping time in the worst case, and several minutes in the best. This work raises the question whether we can achieve competitive accuracy much faster. We introduce FastForward, a method that creates a map representation and relocalizes query image on-the-fly in single feed-forward pass. At the core, we represent multiple mapping images as a collection of features anchored in 3D space. FastForward utilizes these mapping features predict image-to- scene correspondences for the query image, enabling the estimation of its camera pose. We couple Fastaorward with image retrieval and achieve state-of-the-art accuracy when compared to other approaches with minimal map preparation time. Furthermore, FastForward demonstrates robust generalization to unseen domains, including challenging large-scale outdoor environments.
INTRODUCTION
Humans understand complex 3D scenes in seconds. With glance few images of any environ- ment, we can form mental map and reckon where each image was taken. This inherent ability to localize in a scene allows us navigate and understand our surroundings with ease. However, replicating this intuitive process within an algorithm, i.e., visual localizer, is challenging. Visual localizers provide camera location and orientation enabling real-time applications like navigation or Augmented Reality (AR), but they require more than just few seconds of looking at the scene to be able to do it (Brachmann et al. 2023).
One popular family of visual localization approaches requires knowing the structure of the scene, and therefore, before being able to locate an image in the enviromment, they build a 3D model of the scene (Humenberger al.) 2020 Sarlin et al., 2019. Sattler et al. (0066). Such structure- based localizers find correspondences between 3D scene points and 2D query image points and solve for the pose using algorithms like PnP-RANSAC (Gao et al., 2003 Fischler & Bolles 1981). These methods rely on structure-from-motiie (SIM) pipelines to build the 3D representation of the scene and the runtime of every scene depends highly on the number of images, ranging from minutes to hours for few hundred mapping images (Schonberger & Frahm 2016).
To address these limitations, scene coordinate regression (SCR) (Li al. 2020 Brachmann al. 2017 (023) and absolute pose regression (APR) (Kendall & Cipolla, 2017: Shavit et al. 202T Chen al. 2024 (022) methods optimize neural network to learn an implicit representation of the scene, inferring dense scene coordinates (SCR) or absolute poses (APR) from unseen query images. The mapping time corresponds to the network training time, which has been reduced to
minutes in recent approaches (Brachmann et al., 2023 Chen et al.] 2024). They offer accuracy comparable to structure-baat based localizers but require dense training coverage and generalize poorly to unseen areas. Alternatively, relative pose regression approaches (RPR) estimate poses between query and retrieved images without per-scene training or 3D map preparation (Balntas et al. 2018 Arnold et al. 2022 Zhou al. 2020). These approaches are attractive since they significantly

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Published as a conference paper at ICLR 2026
mapping images Pose error: 18cm, 2.8"
Query Image
1 mapping image Pose eitor. 73om, 6.7
mapping images Pose error: 71cm, 6.4"
Figure I:
We introduce FastForward, a network that predicts query coordinates a 3D scene space relative to a collection of mapping images with known poses. FastForward represents the scene as a random set features sampled from mapping images, and returns the estimate for a query w.r.. all mapping images in single when FastForward uses an increasing number of mapping images, as returned by image retrieval. Note that we always sample the number of mapping features, and hence, FastForward's query runtime and GPU memory demand remains roughly constant in all three examples.
pass. From left to right, show how results improve
reduce the mapping requirements by relying only on images and poses, which are obtainable via real-time systems, e.g., SLAM (Murai al. 2025). However, RPR methods generally lack the accuracy of structure- based or SCR competitors. Other works propose to mitigate the RPR's low accuracy by triangulating local point clouds from retrieved images (Sattler et al. 2017: Torii al. 2019), however, their post-processing steps result in significantly longer localization run-times than standard RPR methods.
In this work, driven by the motivation of reducing the overhead of mapping to a minimum, we propose FastForward, a novel approach that achieves fast mapping and localization through a single feed-forward pass. FastForward takes inspiration from recent foundation models (Kirillvv et al.,
2023 Wang et al.) 2024bb and scene representation networks (Jin et al. 2024a Sitzmann et al,, 2021 which have shown strong performance and outstanding generalization capabilities across tasks and datasets, and have pushed the boundaries what we thought was possible just a few years ago (Leroy et al.) 2024; Wu al., 2024). This success motivates our next question: What is the minimal map representation that enables accurate and efficient visual localization? We claim that a collection of image features encoding local visual appearance well their 3D locations within the scene is a powerful and convenient map representation. Our architecture design is inspired by DUS(3R (Wang et al., 2024b), but instead of taking two images as input, FastForward takes one query image as input as well as random sample of features from multiple posed mapping images. Thereby, we predict accurate query 3D coordinates directly in the map coordinate system, see Figure [ Since FastForward has access to collection of features spanning multiple mapping images, avoids the need for computing relative pose estimates between the query and multiple mapping images, one by one. Different from binocular RPR methods that rely on heuristics for scale-metric pose estimates (Arnold et al. 2022), FastForward transfers the correct scale directly from the mapping poses, even enabling generalize to scene scales not seen during training.
We summarize our contributions as follows: 1) We demonstrate that a scene representation consist- ing of only few hundreds mapping features is sufficient for fast and accurate visual localization. 2) We present FastForward. a simple yet effective architecture that enables localization an image relative collection of mapping features in single feed-forward pass. 3) A scene and scale normalization approach within the architecture that boosts the generalization capability in domains with different scale ranges for image localization.
2 RELATED WORK
Visual localization methods require knowing the structure of the environment, and hence, before being able to locate a new query image in the scene, they need to define how they represent the scene in which they want to localize.
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Structure-based Localization requires building a 3D model of the scene. These models are typi-
cally created by SfM software (Humenberger et al. 2020: Schonberger & Frahm 2016 Pan et al. 2024). At localization time, these approaches first establish correspondences between the query im-
age and the pre-built 3D model by keypoint matching (Lwee] 1999; Gaaroso-Laggum et al. 2019 Tone et al.] 2018)) and then, solving for the query pose with robust estimator (Barath et al. 2019 (020 Barath & Matas [022] Chum & Matas, 2005; Barroso-
2020 2022 Tian et al., [2020b
Laguna et al., )223). While these methods can be efficient at inference time (Lindenberger et al.)
2023 Wang et al., 2024c). feature triangulation with SiM can take several hours, or even days,
depending on the number of mapping images.
Scene Coordinate Regression methods regress the 3D coordinates in the scene space for the 2D pixels of a query image (Shotton et al.) 2013). The output and the input to the SCR algorithm already establish the 2D-33 correspondences. A robust estimator can be applied as in the case of fructure-based localization to compute the query pose. Traditionally, SCR relied on random forest (Shotton et al. 2013 Valentin et al., 2015 |Brachmann et al.) 2016 Cavallari et al. 2017 Cavallari ct al.] 2019). but in recent years, SCR improved their accuracy by employing convolutional neural networks (Brachmann & Rother 2021 Cavallari et al.) 2019; Li et al., 2020 Dong et al., 2022). The map representation of the scene is implicit, and in the case of a neural network, is encoded in its weights. One traditional limitation of SCR is the time train such networks. Recently, ACE (Brachmann et al.] 2023) proposed a patch-based training scheme that addressed that issue reducing the training time to minutes. GLACE (Wang et al. 2024a) improves the accuracy of ACE in large areas, but it also increases its training time to 25 minutes. NeuMap (Tang et al. (023) encodes scene into set of map codes and uses a coordinate regressor to estimate the query scene coordinates. Their regressor network is trained per dataset, and map codes trained per scene, taking considerable time to optimize. Furthermore, NeuMap requires a pre-built 3D model to initialize their system. Different from SCR methods, FastForward is pre-trained on a large-scale dataset, and requires no further scene-specifie training.
Relative Pose Regression systems aim at localizing a query image by regressing the relative pose between the query and the most similar (or top-K) mapping images (Ding et al., 2019 Zhou et al.) 2020 Winkelbauer et al.) 2021: Armold et al. 2022). Adding more mapping images enables more precise absolute positioning through milti-view triangulation (Laskar et al., 2017 Zhou et al. 2020 Winkelbauer et al.] (021). An attractive characteristic of RPR methods is that they do not require any scene-specific training. Our approach shares a core principle with RPR methods: it estimates the query pose relative map representation. However, while RPR methods rely single reference image, we represent the map as a collection of 3D-anchored mapping features.
Semi-generalized Relative Pose Estimation methods compute the absolute pose of query camera relative to a generalized camera composed of multiple mapping images with known poses (Bhayani al. 2021 Panek et al. 2024 2025). This formulation allows for the recovery of the absolute trans- lation scale from additional mapping images. An efficient implementation of this approach is the E solver, which utilizes five point correspondences between the query and one mapping image to estimate the essential matrix, and a single additional correspondence with a second mapping image to resolve the scale (Zheng Wu 2015). Such methods typically rely on RANSAC-wrapped geo- metric solvers operating on 2D-2D image pair matches rather than exploiting the multi-view struc- ture and relationships in a single ford-forward pass, leading to lower pose accuracy than FastForward in unstructured or challenging scenarios.
Foundation Models. Large neural networks have seen an enormous advancement thanks to the scalability of new architectures. These models, based transformer networks (Vaswani et al.) 2017]. are trained on large-scale datasets, and have proven to have very strong generalization capa- bilities well as outstanding performance. One example of these foundation models is DUS13R Wang et al.] 2024b). which takes two images as input and addresses different two-view problems by simplifying them into a single task: the prediction of aligned point maps. The simplicity and accuracy of DUS(3R have motivated many follow-up works. MASt3R (Leroy et al.) (024) builds on top of DUS(3 by adding descriptor head that improves correspondence accuracy through de- scriptor matching. MASt3R-SFM (Duisterhof et al. (024)) embeds MASt3R into an SfM pipeline, Stereo4D (Tin et al. 2024b) introduces an extension for dynamic scenes, and Wang & Agapito (2025); Yang et al. (2025): Elfiein et al. (002)) Wang et al. (2025b)a a) present modifications to the original DUSIR to enable multi-view 3D reconstruction. Viewformer (Kulhánek et al. 2022) uses transformer architecture that, given multiple posed images, creates code representation that can
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VIT Encoder
Decoder
Head
Query image
Map Representation
Random Sampling
VIT Encoder
Self and Cross Attention
No shared
weights
Selected Map Features
Shared weights
Ray Encod.
Decoder M
Head
VIT Encoder
Fi
Scale Scene Normalization
Scene scale 5
Mapping images and poses
Figure 2: FastForward Architecture. FastForward uses a VIT encoder to compute features of the query, 19, and the mapping images. To create the map representation M, randomly sample mapping features. Each mapping feature is augmented with a ray embedding that encodes its camera's is position and viewing direction. Mapping poses normalized by setting one pose the origin and defining the maximum translation any direction to one. FastForward performs self- and cross-attention between the query features and the map representation. The query head predicts the 3D coordinates of the query features the normalized space. The metric scale recovered by applying the scene scale factor (s). The predicted 2D-3D correspondences yield the final query pose (PQ). During training, a mapping head also predicts 3D coordinates for the mapping features, providing additional supervision.
be used either for novel view synthesis or image localization. Their work primarily designed for the novel view synthesis task and lags behind current localization baselines. Reloc3r (Dong et al.) 2024) recently demonstrated that a symmetric DUSt3R can significantly improve RPR's accuracy. However, Reloc3r still relies on two-view relative pose estimates. FastForward leverages more ro- bust multi-view scene representation, which allows to outperform RPR methods and even achieve competitive superior accuracy SCR and structure-based algorithms in certain scenarios.
3 METHOD
Given database of posed mapping images from scene, M {4x RB - L,... K} our objective is to estimate the position and orientation of a new query image, 19, with respect to M. We define the camera pose, Pq. the rigid transformation that maps coordinates from the camera space to the scene space. First, we use the images in the database to define the map representation, which is fed into a transformer network together with query features predict query 3D coordinates, as seen in Figure 2 define 2D-3D correspondences Fischler & Bolles 1981).
At inference time, we utilize the predicted 3D coordinates to compute the pose Pq through PAP-RANSAC (Gao al. 2003
3.1 MAP REPRESENTATION
We aim to minimize the computational and time requirements for localizing query image. One shared characteristic of modern visual localization systems is extracting neural network features from the mapping images. These features are then utilized to train a specialized neural network (SCR), triangulate 3D points (SfM), serve as input subsequent network that computes the relative pose (RPR). Such features are powerful representation of the scene, but they are also heavy to process if we were all of them directly when localizing new query image.
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Feature Sampling. Transformer models look at the whole image before updating its features, and therefore, contrary to previous CNN-based feature extractors, transformers provide features with a global context. For extracting features, adopt ViT encoder (Dosovitskiy et al., 2020), which tokenizes the images and extracts features F from n²_ The encoder produces rich features, but also some redundant features for visual localization because similar areas in the image might not provide new information. We show that just few features from the images are enough to represent the scene M. This is advantageous both during mapping and inference. During mapping, only the image feature extraction step is required. At inference time, it scales well to growing number of mapping images by fixing the size of the map representation N. i.e. the total number of features we use from the mapping images. Since we do not know which regions of the mapping images are relevant for a new query, we randomly sample on the set of mapping features as seen in Figure 2
Scene and Scale Normalization. Scale estimation is an ambiguous problem in 3D computer vi- sion. When only having access images, the information is limited to the 2D plane, and the true distance between the cameras remains unknown (Tateno et al., 2017]] Arnold et al., (022). Multi- image methods must distill the scale of the scene from the mapping poses to guarantee multi-view consistency. However, this is challenging when training across multiple datasets that display differ- ent scale ranges. To help the network generalize to new domains and exploit metric and non-metric training data, we adopt a simple yet effective scene normalization technique. We normalize the scene by defining one of the mapping images, 19, as the reference, and transform all other mapping images such that Ps- Po P This places the scene the origin of the coordinate system. The network is tasked to predict query coordinates in the normalized scene. Furthermore, as in Guizilini et al. (2025), we also normalize the scale of the mapping cameras. We compute the scene scale s as the largest camera translation in any of the spatial coordinates after scene normalization, i.e., max{|z] yl. |=]] where t [x, y, =]" is the translation component of the mapping pose Pa- We normalize all camera translations such that f [z/s, y/s, s/s]" Once the network predicts the 3D coordinates, we multiply them by to recover the true scale of the scene. In this way, we abstract the task of learning metric coordinates from the poses and images. As seen in the Appendix C.I scale normalization makes the network more robust to scale ranges not seen during training.
Ray Encoding. To inform the network about the origin of each mapping feature fly. we use a ray encoding that represents its 3D position and orientation in the normalized scene. Specifically, we use Fourier encoding (Mildenhall et al. 202T) to tokenize the mapping cameras. Each camera is parameterized
ray vector containing the origin & [z, y, 2]"", and its viewing direction r = (K,R+) [uey, Uij, 1)"", where Uij, Viy represent the center pixel of the feature token fiy. KR are the camera intrinsics, and l and R& are the translation and rotation component of the mapping image 1&_ Finally, we use an MLP layer to project the encoding to the same dimension as the feature vector f.j. obtaining the ray encoding R RNdx
3.2 ARCHITECTURE
Encoder-Decoder. As discussed, we utilize the ViT (Dosovitskiy et al. 2020) architecture to tok- enize the input images. We initialize the encoder with pre-trained DUSt3R model (Wang et al.) 2024b) and freeze its weights during training. We process the image tokens through multiple VIT blocks, composed of self-attention and MIP layers. An image I E H Wx3 results in feature map F E IR R where T = 16
x I6 and d = 1024. The map representation is generated by sam- pling N features from the collection of mapping features and fusing them with the ray encodings, = 1, .... N}. For the decoder, we use VIT blocks initialized from
H W
such that Fus = {R.. + fa DUSt3R and fine-tune them during training. The decoder incorporates cross-attention blocks be- tween the self-attention and the MLP layers. The cross-attention allows the network to reason about the structure of the scene and its relationship with the query image. This reasoning occurs within single forward pass, enabling the map representation to adapt based on the query image features. We obtain the final query and mapping features as:
Fe xdTxd) Decodero (I=(T)xd) (N(N)))) and ((Nxd) Decoder M
, p((xx))..
Heads. We follow recent works Waan et al. 2024b Leroy et al. 2024 Yang et al. 2025) and use DPT head (Ranftl al. 2021) 1) to obtain query 3D coordinates. We observed that adding the supervision for the mapping 3D coordinates leads to more accurate query predictions. However, unlike the query 3D points, which need to exploit and capture the spatial structure of the scene,
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the mapping 3D coordinates are primarily used as supervisory signal during training. Therefore, we use a single MLP layer as the mapping head. After computing the mapping and query 3D coordinates, we multiply them by the scale factor to recover the metric scale of the scene.
3.3 TRAINING
The training objective follows the regression of coordinates in 3D space proposed in (Wang et al, 2024b]Leroy Leroy al. 2024). We define the regression loss as the Euclidean distance between predicted (X;) and ground truth (X₂) 3D coordinates as: e Rey = Xx X2sl- The regression loss for the map- ping head constrained the coordinates corresponding to the sampled features that created the map representation, while the regression loss for the query head is computed on all pixels with valid ground-truth depth values. We adopt DUS(3R's confidence-basee to predict lower confidences in regions where predicting 3D coordinates might be challenging or ambiguous (e.g., sky, or translucent objects). The final training objective defined as:
2024b
loss, which allows the network
{ Conf
Σ
C,e Cv, i) log(Ci),
(Q. M. IED
where C is the confidence score for pixel i, D refers either to the pixels in the query image the map representation, and a is a hyper-parameter controlling the regularization (Wan et al., 2018).
Please refer to Wang et al. 2024b) for further details.
EXPERIMENTS
Absolute Pose Estimation. At inference time, compute the set of 2D-3D correspondences, which define 2D pixel locations in the query image (19) and their corresponding 3D points in the scene coordinate system defined by the map representation M. We filter correspondences with low confidence scores (C; < T) and set a maximum 5,000 correspondences in -AASACC Refer Appendix A for FastForward's additional inference, training and datasets details.
Competitors. Inspired by Reloc3r (Dong et al. 2024), we group competing methods into Seen and Unseen categories. This distinction refers to whether extensive map preparation is required before a query can be localized. All methods assume that mapping images and their corresponding poses available. While some datasets, such as Cambridge (Kendall al., 2015), require building SfM model to obtain these poses, others, like Wayspots (Brachmann et al. (023), provide them in real-time via on-device tracking systems. Even with available mapping poses, Seen methods still require triangulating a scene (structure- based) or training a neural network (SCR). The triangulation time for structure-bar based approaches dataset-specific, ranging from minutes to hours depending on the number of mapping images, whereas SCR methods can limit their training time to few minutes. In contrast, Unseen methods, such as RPR, only require curated list of hearest-neighhon for the query image. This image retrieval process can be performed very efficiently using compact image-level descriptors (Revaud et al. 2019 Arandjelovic et al. 2016), which reduces the mapping preparation time to minimum.
4.1 WAYSPOTS DATASET
The Wayspots dataset (Brachmann et al. 2023 is composed of ten scenes, each with two aligned scans for mapping and localization. It contains small outdoor places of interest, such sculptures, signs, or fountains. We compare FastForward against several state-of-the-art visual localization methods, and, as discussed, we group them into Seen and Unseen categories based on their map preparation requirements. The Seen group includes SCR methods like ACE (Brachmann et al.) 2023) and GLACE (Wang et al. 2024a). In the Unseen group, we report results for Reloc3r (Dong et al. 2024) and 2D-2D feature matchers, specifically ALIKED-LightGin 2023 Lindenberger et al. 2023 and RoMa (Edstedt et al. 2023) paired with the ES+1 solver (Zheng & Wu. 20155] Panek et al. 2025) from PoseLib (Larsson & contributors) 2020). For ALKD-LG, we report results when extracting 1,024 features on 640px images. We omit comparisons against MASt3R since Wayspots is a subset of the Map-free (Amold et al. 2022) training set, which was used to train it. All Unseen methods use the same top-20 neirest- retrieval system, which is computed in 3 seconds, requiring the extraction of image-level descriptors every 5 frames with
(ALKD-LG) (Zhao et al.)

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Published as a conference paper at ICLR 2026
Table 1: Median Pose Errors on Wayspots (Brachmann et al., 2023). We provide the median translation and the average median rotation errors of the dataset. We also report the mapping and latency times for each method. Best results in bold for the Unseen category.
Ct (m)
Cuhes
Bears
Winter
Insc.
Rock
Tend.
Map
Bench
Statue
Lawn
Avg.
Cp (*)
Latency
Mapping
Seen
ACE
0.05
0.04
4.76
0.10
0.03
1,63
0.07
0.05
5.50
1.11
1.33
9.08
0.1s
Smin
GLACE
0.06
0.03
5,03
0.10
0.03
1,69
0.07
0.06
5,97
1.30
1.43
8.87
0.1s
25min
ES+1 (ALKD-LG)
0.09
0.03
1.17
0.11
0.03
0.94
0.08
0.07
1,47
1.12
0.51
7.74
0.8s
36
Unseen
ES+1 (RoMa)
0.09
0.02
0.09
0.24
0.09
6.21
0.77
4.12
18.0s
38
Reloc3r
0.32
0.06
5.01
0.13
0.04
0.81
0.08
0.15
5.76
0.69
1.31
2.04
0.6s
3s
FastForward
0.08
0.03
0.47
0.14
0.04
0.15
0.07
0.06
0.54
0.10
0.17
1.75
0.5g
3s
Table 2: Accuracy on Wayspots (Brachmann et al., 2023). We report the accuracy under the 10cm, 10 threshold. FastForward achieves the highest number acceptable localizations for a
real-world application such as AR (Amold et al. 2022). Best results in bold for the Unseen group.
10cm, 10((%)
Cubes
Bears
Winter
Insc.
Rock
Tend.
Map
Bench
Statue
Lawn
Avg.
Storage
Seen
ACE
95.1
80.0
0,7
49.7
100.0
32,9
55.9
67.8
0.0
37.0
51.9
Weights
GLACE
89.6
86.4
47.9
100.0
37.0
58.3
64.1
0.0
40.4
52.4
Weights
ES+1 (ALKD-LG)
52,5
89.1
288
45.7
96.7
37.1
54.2
53.8
0.0
33.2
46,5
Images
Unseen
ES-1 (RoMa)
53.6
98.3
0,7
53.3
99.8
40.8
56.8
43,1
0.2
48.4
49.5
Images
Reloc3r
30.6
72.1
43.8
99.0
22.9
59.2
32.6
0.0
10.9
37.1
Imaags
FastForward
67.8
94.8
31,2
100.0
41.4
56.8
70.1
2,7
48.6
51.4
Tmaggs
GeM-AP (Revaud et al. 2019). In contrast, the SCR methods, ACE and GLACE, require 5 and 25 minutes, respectively, to train their scene-specific networks. In FastForward, we sample N = 3, 000 mapping features, corresponding to 20% of the total features our map representation M.
In Table [ we see that FastForward excels in translation estimation, reporting a median error of 0.17m, while all competitors show median errors above half meter. Furthermore, FastForward obtains the lowest mean median rotation error. FastForward achieves these srate-of-the-a results while reducing the mapping preparation times required by SCR methods and displaying the fastest localization time in the Unseen group. Table also shows the percentage of query frames under the 10cm, 10" threshold, which determines the acceptability of an estimate for a real-world appli- cation such as AR (Arnold et al.) [022] (Barroso-Laguna et al.) 2024]. FastForward outperforms all Unseen methods, including Reloc3r, which also adopts DUS s architecture and was designed for visual localization. E5+1 with ALKD-LG and RoMa paired with the E5+1 solver offer strong performance in scenes with good coverage but fail when the scene presents challenging conditions, such as far-away opposite viewpoints, e.g., Winter or Lawn scenes. Additionally, RoMa's ' high latency (18s) is ill-suited for real-time visual localization systems; therefore, we focus our following analyses on E5+1 (ALKD-L.G) given its good accuracy-latency trade-off. Regarding storage, Un- seen methods only require images and global descriptors, while SCRs store their network weights, e.g. 4MB (ACE), or 9MB (GLACE).
4.2 INDOOR6 DATASET
The Indooró dataset (Do al.) 2022) contains six indoor scenes that present challenges like repet- itive uncharacteristic areas and significant illumination changes. We also include the results of MASt3R + Kapture (Leroy et al. 2024, Humennerrer et al., 2020). an approach that requires an initial SIM model and uses MASBR rectly in its 3D point and matching heads instead of building a SIM model. Given the smaller scene area compared to the Wayspots dataset, we build retrieval system that returns the top-10 mapping images. We use the same retrieved images for MASt3R approaches, E5+1 (ALKD-LG). Reloc3r, and FastForward. Besides, reduce map representation size to N 1, 500.
2D-2D matcher, and variant of MASt3 that relies di-
Table 3 (left) presents the median pose errors and the percentage of accepted query frames under the 10cm, 10 and 20cm, 20 thresholds. FastForward achieves the highest acceptance rates for both
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Table Results for Indoor6 (Do al., 2022) and RIO10 (Wald al. 2020). Results on Indooró shows that FastForward achieves the highest accuracy among all competitors. In RIO10, MASt3R and FastForward report the best accuracies. We bold the best results in the Unseen group.
Indooró Dataset
RIO10 Dataset
Cg (m) Cy (*) 10cm, 20em,
Mapping
et (m) cy (*) 10cm, 20cm,
Mapping Latency
Kapture
0.03
0.5
89.0
93.6
</3.5
N/A
N/A
24.8
32.6
<-4h
4.5s
Seen
ACE
0.11
57.5
68.8
5min
3.58
58.7
11.0
16.2
Smin
0.1s
GLACE
0.04
0.6
86.3
92.0
25min
1,14
33,4
22.8
31.7
25min
0.1s
ES+1 (ALKD-LG)
0.04
0.6
80.9
89.8
8s
N/A
NIA
25.5
35.8
10s
0.4s
Unseen
MASOR
0.13
0.7
45.9
76.0
8s
0.17
5.5
45.1
58.2
10s
4.5s
Reloc3r
0.09
0.8
57.4
72.8
8s
0.47
9.4
21.4
32.9
10s
0.3
FastForward
0.04
91.5
98.0
8s
0.18
5.5
40.6
59.7
0.36
thresholds among all competitors. FastForward surpasses even MASt3R Kapture, method that requires extensive mapping preparation before localization. FastForward also outperforms ACE and GLACE while reducing the mapping preparation stage to mere seconds. In the 10cm, 10 regime, FastForward boosts the accuracy of MASt3R, Reloc3r and E5+1 (ALKD-LG) by 99%, 59% and 13%, respectively, improving significantly upon other RPR approaches. Since the Unseen methods use the 10p-10 instead of top-20 retrieved images as in Wayspots, their latencies are reduced. For instance, we see that FastForward and Reloc3r localizes a new query frame in only 0.3s.
4.3 RIO10 DATASET
Table 3 (right) presents the results on the RIO10 dataset (Wald et al., 2020). This dataset focuses on long-term indoor localization across ten scenes with changing conditions, such moved or replaced furniture. Since the test evaluation service only allows submissions every two weeks, we report results the validation set.
The dynamic nature of the RIO10 dataset poses significant challenges for methods relying on structure-based representations. As result, MASt3R + Kapture, despite being of the best- performing methods overall, experiences significant performance degradation in these scenes. While its accuracy slightly better than SCR approaches, its median errors could be computed because more than half of the query pose predictions lacked sufficient correspondences for PnP-RANSAC E5+1 (ALKD-LG) also estimates 2D-2D matches between the query and the mapping images, re- lying on the known structure of the scene. In some scenes, ALKD-LG failed to produce suffi- cient correspondences for the E5+1 solver for more than half of the query estimates; hence, as in MASI3R + Kapture, could not compute its median pose errors. MASt3R (Unseen) achieves the highest 10cm, 10 accuracy, while FastForward demonstrates the best 20em, 20 accuracy. This sug- gests that access full images, in MASt3R's s approach, may be beneficial when scene conditions change, as a sparse map representation might not capture enough fine details for optimal predictions. Nevertheless, FastForward outperforms SCR methods (ACE and GLACE), E5+1 (ALKD-LGG), and Reloc3r, demonstrating its robustness for long-term visual localization.
4.4 CAMBRIDGE LANDMARKS DATASET
The Cambridge dataset (Kendall et al. 2015) is an outdoor dataset consisting of six different places of interest Cambridge. We follow recent works (Dong et al. 2024, Brachmann et al., 2023) and report results for five of these scenes. On top of previous comparisons, we compare FastForward against several classical visual localization pipelines. In the Seen group, we include Active Search (AS) (Sattler et al. 2016) and hLoc (Sarlin et al. 2019 2020). In the Unseen group, we include several additional RPR methods (Turkoglu et al. 202 Arnold et al. 2022 Winkelbauer Dong al.) 2024). The entire retrieval index is computed in 30 seconds, requiring only the extraction of image- descriptors with the GeM-AP global descriptor. As in the Wayspots the t2p-20 retrieved images compute the query localization. More- over, we sample N = 3, 000 mapping features for our map representation M. Table reports the median pose errors, query latencies, and map preparation times.
et al. 202 1
outdoor benchmark,
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Table 4: Median Pose Errors on Cambridge Landmarks (Kendall al. 2015). Seen methods require triangulating the scene or training a scene-specific network before being able to localize a new query image. Unseen methods only require retrieval step to find the top mapping image candidates. The retrieval step can be performed for 1,000 images in under a minute (Revaud et al., 2019). We bold the best and underline the second best results of the Unseen group.
(m)) ez-
Great Court
King's 's College
Hospital
Shop Facade
Church
Average
Lateney
Mapping
AS (SIFT)
0.24/0.1
0.13 /0.2
0.20 0.4
0.04 0.2
0.08 0.3
0.14/0.3
0.4s
hooe (SP+SG)
0.16/0.1
0.12/0.2
0.15/0.3
0.04 0.2
0.07 0../
0.11/0.2
335mn
Seon
MASER Kapture
0.13
/0.1
0,15
0.04 0..2/0.2
0.04/0.1
/0,2/0.2
9.0s
ACE
0.44/0.2
0.30/0.4
0.3
0.06/0.3
0.20/0.6
0.26 00.4
0.1s
Samnn
GLACE
0.19/0.1
0.19/0.3
0.17/0.4
0.04 7..2
0.09/0.3
0.14/0.3
0.1s
25min
Relpuse GNN
3.20/2.2
0.48/1.0
1.14/2.5
0.48/2.5
1.52/3,2
1.37 2
NIA
Map-free
8.40/4.6
2.44/2.5
3.73/5.2
3.97
2..1
3.69
00.2s
ExReNet
9.79
2.3
3,54/3.5
0.72
2.30/3.7
3,74/3.3
~00.46
ES+1 (ALKD-LG)
0.32/0.1
0.16/0.3
0.3
0.05 0.3
0.09 /0.
0.18 00.
1.1s
~03
MASER
5.62/0.5
/0,7
4.71 10.7
1.14
/.4
/,7
9.0s
Reloc3r
0.96
0.41 /0.3
0.73 0.6
0.14
0.33/0.6
0.52/0.5
0.66
FastForward
0.62/04
0.24/0.4
0.26/0.5 0.5
0.08/0.4
0.14/0.5
0.27 /04
ALKD-LG paired with the E5+1 solver achieves the lowest median pose errors among the Un- seen methods. Notably, it surpasses some structure-based based localizers, such ACE, despite only requiring a retrieval step for mapping. As a method based on 2D-2D image matching, similar to AS or MAS13R Kapture, E5+1 (ALKD-L.G) relies heavily high structural consistency and dense map coverage. Unlike Wayspots or RIO10, the Cambridge dataset offers these favorable conditions, allowing explicit matching methods to excel; however, as shown in previous sections, they struggle in more challenging or sparsely mapped environments. In Appendix C. we explore various ES+1 (ALKD-LG) configurations and discuss the latency-accuracy trade-ofti ofts compared to FastForward. Meanwhile, FastForward obtains the second-best median errors among the Unseen methods, reducing the translation error of its closest competitor, Reloc3r, by 48%. MASt3R strug- gles in the large-scale Cambridge scenes since they display scale ranges that are not present in its training dataset. FastForward is trained on a subset of these datasets (refer to Appendix [A] for de- tails); however, its scale normalization strategy helps FastForward to generalize well to these unseen scale ranges. We extend the scale normalization discussion in Appendix C.T
4.5 UNDERSTANDING FASTFORWARD
Validation Examples. We present qualitative results of FastForward on the validation datasets in Figure For these visualizations, we use mapping images and a map representation with N=1,000 features, and highlight the image regions corresponding to the selected mapping features. For train- ing and validation, instead of using image retrieval as in the localization experiments, we randomly sample mapping images that overlap with the query image by at least 20% and not more than 85%. We use the overlapping scores provided in DUSt3R training pairs. This ensures larger scene cov- erage and encourages the network to learn to interpret mapping features from diverse locations. The ground-truth camera pose is shown in green for reference, while FastForward prediction is in blue. We also display the predicted 3D coordinates of the query points. Even though FastForward only uses a subset of mapping features at inference time, it still exhibits robustness comparable to DUS(3R. FastForward effectively handles repetitive patterns and symmetries by accessing only few mapping features, demonstrating the effectiveness of our map representations.
Mapping vs. Querying. Structure-based relocalizers generally offer fast query times once the scene representation is built. Consequently, structure-based based methods can amortize their high mapping costs after a certain number of queries. For instance, compared to the highly efficient ACE baseline, the break-even point occurs at approximately 600 relocalizations. Thus, for high- demand locations, structure-based relocalizers become computationally more efficient in the long run. However, FastForward enables instant, on-demand relocalization for custom maps or locations where usage is unpredictable. Service providers can leverage this flexibility to offer immediate coverage. opting to build structured maps only for spots that demonstrate high popularity. Finally, FastForward allows for a configurable trade-ofi off between runtime and accuracy by varying the num- ber of retrieved mapping images to meet the requirements of the application (see Appendix C.2
Runtime:

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Published as a conference paper at ICLR 2026
Figure 3: Qualitative Examples. The estimated camera pose from FastForward is shown in blue, the ground-truth pose in green, and the mapping camera poses in gray. We visualize the predicted 3D coordinates of the query points, as well as the image patches from which the mapping fea- tures are sampled. We FastForward effectively handles symmetries and non-discriminative patterns in the scenes. Besides, since FastForward is agnostic the scale of the scene, it can accurately predict poses scenes with arbitrary scales, as demonstrated in the MegaDepth (Li & Snavely 2018) example (bottom-leftt.
mapping images and map representation with N=1,000 features.
Limitations. Although building retrieval index is fast, e.g., under one minute for 2,500 images using GeM-AP (Revaud et al. 2019) on single V100 GPU, the time extract global descriptors with a growing number of images not negligible. In Table 7 (Appendix B.2), investigate version of FastForward that does not rely on image retrieval but selects reference mapping images at random uniformly in the Wayspots dataset. This setup is much more challenging reference images might be less relevant to the query. We observe the accuracy dropping from 51.4% to 47.8% (10em, 10%). Other RPR methods suffer similarly, for example, Reloc3r's accuracy drops from 37.1% to 19.7% without the retrieval step. Future work could explore alternative strategies for selecting mapping images to represent the scene.
More Details and Experiments in the Appendix. Training and inference details are in Appendix A Appendix...l
the results in the 7-Scenes dataset (Shotton et al.) (001)). Appendix [B.2 shows different map representation strategies that do not require retrieval, and hence, reduce the map- ping preparation time zero. We discuss the benefits of our scale normalization in Appendix C.I Furthermore, we study the impact of the number of mapping images and the size of the map repre- sentation N in Appendix C.2
We provide visual examples from the test set in Appendix C.4
CONCLUSIONS
We have introduced FastForward, a method that enables fast mapping and localization through a sin- gle feed-forward pass. We have demonstrated that visual localizer can reduce its mapping prepara- tion requirements to a simple retrieval step and still provide state-of-the-ard visual localizations. We have also shown that sparse collection of mapping features can serve as an effective and sufficient representation of the scene for accurate visual localization. Furthermore, we have demonstrated that simple yet effective scene and scale normalization techniques can significantly improve visual local- ization accuracy in out-of-domain scenes. We have shown the robustness of FastForward predictions in multiple indoor and outdoor datasets, where each of them displayed unique challenges such as large-scale ranges, varying illumination conditions, and dynamic scenes. Our experiments demon- strate that we can achieve both efficient and accurate visual localization with single feed-forward pass. FastForward outperforms state-of-fe oftte-of-thearr RPR methods both indoor and outdoor datasets, while achieving higher accuracy to SCR methods on indoor datasets and superior or comparable performance outdoor datasets.
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APPENDIX
A TRAINING & INFERENCE DETAILS
This section provides the training parameters and datasets we used to train FastForward. Besides, we also provide some complementary inference details to those in the main paper.
Training. FastForward is trained on mix of indoor and outdoor datasets. We train on subset of the datasets used in DUS (Wang et al. [024bb] |eroy et al. (024), specifically: ARKitScenes (Baruch et al. 2021). WildRGBD (Xia et al.] 2024), ScanNet++ (Yeshwanth et al. MegaDepth (Li & Snavely, 2018), BlenderMVS (aao et al. 2020), and Map-free (Arnold
2023
et al. 2022) (excluding the scenes in the Wayspots dataset (Brachmann et al. 2023).
During training, we fix the number of mapping images in M K = 5, but sample varying numbers of features to create different map representation configurations such that N E [250, 1000]. We initialize FastForward with the public 512-DPT weights from DUS:3R.
Only the decoder and the two heads are trained, while the encoder is frozen. We train FastForward by optimizing the loss in Equation 2 with the A (Loshchilov et al., 2017) optimizer for 615k iterations. We use batch size of 48 and a cosine learning rate scheduler with peak learning rate of le-4 and warmup of 30k iterations. We leverage f precision to improve GPU memory and computational efficiency. Training is performed on 8 A 1100400G GPUs and completes in 5 days.
We use the overlap scores from DUS(3R (Wang et al. 2024b) and MASt3R (Leroy et al. 2024 to
select the mapping images in M. We use a similar strategy to DUS(3R/MAS(3R where only mapping images that overlap with the query image are valid training candidates. We set the overlapping range to [0.2, 0.85]. For datasets without overlapping information, e.g.. WildRGBD (Xia et al., 2024),, we randomly sample the mapping images in M. We balance the outdoor and indoor datasets such that the model is trained with a similar number of indoor and outdoor examples.
Inference. At inference time, each image is resized to 512 in its largest dimension and center cropped to the closest aspect ratio used during training (Wang et al., 2024b). Before PnP-RANSAC, we filter query 3D point predictions that have low confidence value (C; < т, where r = 1, 5), and randomly subsample at least 5,000 correspondences. For the outdoor experiments, we use the top-20 retrieved mapping images, while the top-10 mapping images for the indoor environments.
B ADDITIONAL EXPERIMENTS
B.1 7-SCENES DATASET
We present in Table the median errors, accuracies, latencies, and mapping details for the 7-Scenes dataset (Shotton al. 2013). This dataset focuses short-term indoor localization and provides seven scenes with multiple mapping and query scans.
We observe that the Unseen methods perform competitively even in static scenes, where methods based on SfM localizers or SCR networks typically excel. Among the Unseen methods, FastForward achieves the highest acceptance rate for the 10cm, 10 threshold, while E5+1 (ALKD-LG), followed by FastForward, gets the best accuracy under the 5cm, 50 threshold. FastForward and Reloc3r obtain the lowest median translation error, while MASt3R slightly surpasses them in rotation error (-0.04%) The improvement in translation error demonstrates the benefit of having access to mapping poses at inference time, even in scenarios, i.e., indoor scenes, that were represented in the training set of all Unseen methods. Besides the accuracy, we also report the mapping times and storage requirements. For the retrieval system of the Unseen methods, we apply a frame rate of fifteen in the mapping scans before building the retrieval index, i.e., only one frame every fifteen is considered as a mapping candidate for the query image. The average number of images in the mapping scans is 3,700, which, after our fifteen-frame sampling, becomes 250 images. This sampling removes consecutive and redundant mapping frames and reduces the retrieval time to 7s. As discussed, the retrieval step is much faster mapping process than those required by Seen methods. FastForward, as all other Unseen methods, needs to store the mapping images and their global descriptors for retrieval. The storage cost, therefore, depends on the number of mapping images, but is generally
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Table 5: Results on 7-Scenes dataset (Shotton al. 2013). We report the accuracies, median errors, and mapping preparation times for each method. FastForward achieves the highest accuracies among the Unseen methods. The Unseen methods are based on a top-10 retrieval search, and thus they can run in just few seconds, unlike MASt3R + Kapture, GLACE, or ACE. In the Storage requirement, PC refers to Point Cloud, and Weights the scene-specific network weights. Best results in bold for the Unseen methods group.
//c
5om, 5
10cm, 10"
Latency
Storage
Map Preparation
Mapping Time
Unsen Seen
ACE
Kapture
0.01 /0.33
97.1
99.5
0.1s
Weights (4MB)
Images
Point Triangulation
Network Training
5min
GLACE
0.01 /0.36
Weights (9MB)
Network Training
25min
ES+1 (ALKD LG)
0.05 / 1.30
80.7
89.3
0.4s
Inuges
Retrieval
MASIR
0.07
71.9
Images
Retrieval
Relocár
0,04/1.02
64.3
85,9
0.3s
Images
Retricval
7s
FastForward (Ours)
1.04/1005
73.6
90.2
0.3s
Images
Retrieval
Table 6: Results Wayspots dataset (Brachmann et al. 2023). We provide the median rotation emros and the accuracy under the 10cm, 10 threshold. Additionally, we include the average median translation error and the mapping preparation time for each of the methods. ACE and GLACE train a network for each scene in Wayspots, while Reloc3r and FastForward compute a retrieval index that runs in 3 seconds for Wayspots scene on a V100 GPU. In contrast to Reloc3r, Fostrorward obtains comparable accuracy to SCR methods while reducing their mapping time. In addition, FastForward achieves the lowest rotation error. Best results bold for the Unseen category.
Cr (*)
ACE
GLACE
E5+1 (ALKD-LG)
Reloc3r
FastForward
Cubes
0.8
Bears
1.0
0.9
2.2
Winter
1.1
1.4
1.0
1.4
2.2
Inserip.
1.6
1.4
1.2
1.1
1.9
Rock
0.8
Tendrils
36.9
28.9
4.4
Map
1.1
0.9
1.1
1.0
Bench
0.7
0.7
0.7
1.0
0.7
Statue
14.3
13.0
1.6
1.9
3.9
Lawn
32.6
40.2
45.7
5.6
Avg.
8.9
2.0
(m)
1.33
1.43
0.51
1.31
0.17
10em, 10" (%)
51.9
52.4
46.5
37.1
Latency
0.1s
Mapping
5min
25min
lower than that of classical methods that store large point clouds with high- high-dimmessonal descriptors. Mapping images and point clouds can be sub-sampled to save storage if needed; however, the storage cost of SCR methods (at least for small areas) is generally the lowest with few MB. FastForward is competitive to even SCR when using the variant that uniformly samples mapping images instead of doing retrieval (see Table 7). In this case, only fixed set of 20 images needs be stored to represent entire scene.
B.2 WAYSPOTS DATASET
Additional Metrics. Table shows additional metrics to the Tables and from the main paper. We report the median rotation errors in the Wayspots dataset (Brachmann obtains the lowest rotation error among all competitors, even surpassing SCR methods while re- ducing their mapping preparation time from or 25 minutes to a few seconds. Furthermore, as discussed in the main paper, FastForward significantly improves the median translation error, ducing the second best error from 0.5 551m (ES+1 with ALKD-LG) to 0... This demonstrates that FastForward achieves more robust and stable localizations, particularly in challenging scenes. In the 10cm, 10 threshold, FastForward outperforms all Unseen methods and shows comparable accuracy state-of-the-art SRC localizers. The Wayspots dataset mapping poses from real-time SLAM
2023). FastForward
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Map Representation results on the Wayspots dataset (Brachmann al. 2023). We present the results of using different strategies to select the M mapping images for the map repre- sentation generation. All strategies use 20 mapping images and sample 20% of the features from each image. We also report state-of-the-art methods as reference. Random and uniform sampling require no mapping preparation and utilize constant map representation that can be reused for all query images, reducing the storage requirements and the localization time. Both strategies yield pose estimates with lower median translation errors than all competitors, and even outperform the accuracy of Reloc3r.
Table 7:
6, (m)
(9)
10cm, 10 (%)
Mapping Time
ACE GLACE E (ALKD-LG) w/ Retrieval
9.1
51.9
Smin
1.43
8.9
52.4
25min
0.51
7.7
46.5
3s
Reloc3r w/ Retrieval
1.31
2.0
37.1
3s
Fastadd
Retrieval
0.17
1.8
51.4
3s
Random
0.31
2.7
43.9
Os
Uniform
0.19
2.3
47.8
Os
on the phone without any post-processing. In contrast, evaluation poses were bundle-adjusted via COLMAP (Brachmann et al. 2023). Even though mapping poses are not perfect, e,g., they might suffer drift, FastForward pertorms very well, showing some robustness to inaccuracies in the map- ping process.
Map Representation. Table displays the results when using different strategies to select the map- ping images that constitute the map representation M. The retrieval strategy selects the top-K images based on global descriptor similarity; this is the baseline approach followed in all prior experiments. We also report results for random and uniform sampling of images along the mapping scan. While retrieval-based selection is the most accurate strategy, it requires precomputing global descriptors and finding the closest mapping candidates inference time. Random and uniform sampling strate- gies offer two main advantages: 1) the map representation can be computed once and reused for all query images, and 2) the mapping preparation step is climinated since no global descriptor extrac- tion is needed. However, the main disadvantage is that these methods are generally less accurate than the baseline retrieval strategy. Even though the retrieval system has possible limitations or fail- ures, FastForward shows strong robustness and accuracy comparable to retrieval-free methods like SCR approaches. Moreover, our random sampling strategy simulates a retrieval failure scenario, where the system returns images unrelated to the query. Even under these conditions, FastForward surpasses Reloc3r in accuracy and achieves lower translation errors than all competitors.
C FASTFORWARD ANALYSES
C.I SCALE NORMALIZATION
We train FastForward model without the scale normalization step detailed in Section 3.1 In this variant, we directly feed the metric translation vector to the network, allowing it to predict the 3D coordinates in the same scale as the mapping poses. Since FastForward's 's training directly optimizes metric 3D predictions, we remove from its training the datasets that do not provide metric ground- truth. Specifically, we remove MegaDepth (Li Snavely 2018) and BlenderMVS YYao et al.) 2020). In MegaDepth, the ground-truth comes from up-to-scale SPM reconstructions. BlenderMVS provides metric poses and depth maps depending on whether the images used to build the 3D models had GPS information. We follow MASt3R and treat this dataset as non-metric since not all scenes provide metric estimates. Our baseline model, i.e., FastForward with the scale normalization, does not require scaled poses during training. Hence, containing arbitrary scale ranges as long as they are consistent. Normalizing the translation vector within FastForward allows for more diverse and accessible training data.
augment our training set with datasets
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Table 8: Scale Normalization Ablation. We show the results of FastForward when the network directly digests the mapping poses without the scale normalization proposed in Section [.. The scale normalization improves the accuracy as well as the generalization capability of FastForward.
10em, 10 / 20cm. 20" (%)
Cambridge
Wayspots
Indoor6
RIO10
7-Scenes
w/o Scale Normalization
1,8/6,
47.0 / 66.1
83.4/977 91.5 / 98.0
35.9 55.2
89.1 / 93.7
FastForward (ours)
26.7 53.6
51.4/ 68.7
40.6 / 59.7
90.2 / 95.8
100
100
Accuracy (%)
Accuracy (%)
50
accig25cm,5 5
acci 10cm,10"
ace 100
accd 10cm,1 1"
20-
acog 10cm,
0
12
16
20
24
29
32
150
1000
2000
3000
Number of Mapping Images
Number of Mapping Features
Figure 4: Accuracy vs Number of Mapping Images. We show the accuracy under the 10cm, 10", 10cm, 19, and 25cm, 5 thresholds as we in- crease the number of mapping images in our map representation. We fixed the size of the map rep- resentation 768 mapping features.
Figure 5: Accuracy vs Number of Mapping Features. We fix the number of mapping im- ages to 20 images and show how the accuracies change we increase the number of mapping features that are used to create the map represen- tation of the scene.
In Table g "W/o Scale Norm." refers to FastForward without the scale normalization. We observe that the scale normalization is crucial when evaluating FastForward in the Cambridge Landmarks dataset. The Cambridge dataset consist of large-scale outdoor scenes. In these scenes, the map- ping images might be far from each other, and hence, the translation vectors fed into FastForward (W/o Scale Norm.) might contain larger scale ranges than those seen during training. MAS13R, even though trained with MegaDepth and BlenderMVS scenes, exhibited similar behavior in the datasets, Cambridge dataset (refer to Table 4). While performing very competitively in all indoor
MASt3R's accuracy Cambridge is only 0.5% (10em, 10 threshold). Since FastForward has ac- cess to mapping poses at inference time, we can easily mitigate this by normalizing all translation vectors to the unit sphere (see Section 3.I.. This strategy is straightforward but also very effective, e.g., the 10cm, 10 accuracy in the Cambridge dataset improves from 1.8% 26.7%. Thanks to this scale normalization, and the fact that FastForward relies on retrieval system to turn the global pose estimation problem into a local small-scale problem, FastForward can scale larger areas. Lastly, the results on the Wayspots and indoor datasets are comparable, with the scale-normalized version performing slightly better. This aligns with our expectations, as the scale ranges of these datasets were included in our training set.
C.2 MAP REPRESENTATION ABLATIONS
Number of Mapping Images. Figure f presents the results when increasing the number of images that are used create the map representation. We report the accuracy under the 10cm, 1 10cm, 10 and 25cm, 5" thresholds in the validation set of the Map-free dataset (Amold et al. 2022). This ex- periment follows the evaluation protocol used in the Wayspots dataset. We localize the query images with respect to the mapping scan, and select the mapping image candidates using a retrieval step. Unlike the training setup, overlap information is not required for the map representation generation. We fix the map representation size to N 768 mapping features, equivalent to sampling 100% of the features from a single mapping image. Le.. the map representation size remains constant re- gardless of whether we use 1 or 30 mapping views. Accuracy rates for all thresholds improve with an increased number of images in the map representation, demonstrating the network's 's ability to incorporate multi-view information despite only subset of the mapping features being used in the prediction. In our previous outdoor experiments, we used 20 mapping images, which we consider a good balance between accuracy and computational cost.
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FastForward Ground Truth
Retrieved mapping frames
Figure 6: Qualitative Examples. The estimated camera pose from FastForward is shown in blue, and the ground-truth pose in green. The complete mapping scan is visualized in gray, with only the mapping images selected by our retrieval step displayed. Additionally, we visualize the predicted 3D coordinates of the query points. FastForward is able to handle symmetries, opposing viewpoints, and illumination changes. Moreover, because FastForward operates in normalized scale space, it can handle scenes with significant scale variations, despite not being trained them (e.g., the King's s College scene from the Cambridge Landmarks dataset (Kendall et al. 2015)).
Number of Mapping Features. Figure J shows the results when varying the number of mapping features used create the map representation. All map representations are sampled from 20 map- ping images. Similar to the previous ablation study, increasing the size of the map representation benefits the accuracy of FastForward. Interestingly, FastForward is affected more by the number of mapping images than by the size of the map representation itself. Accuracy under the 10cm, 10º or 25cm, 5 thresholds remain almost constant when using 150 or 3,000 mapping features. How- ever, for finer thresholds (e.g., 10cm, 19), FastForward benefits from more mapping features. This suggesting that can trade off accuracy on the fine threshold for reduced storage or computation.
C.3 RUNTIME
FastForward utilizes the same feature encoder MASt3R and Reloc3r. However, FastForward offers two key advantages: 1) Given multiple mapping images, FastForward processes a fixed set of N features in the decoder, whereas MASt3R and Reloc3r require processing all the mapping-quer combinations. 2) FastForward directly provides the query 3D coordinates in the mapping scene, eliminating the need for any additional global alignment step.
FastForward extracts features from all mapping images, and hence, as in MAS13R or Reloc3r, its runtime depends the number of mapping views. For instance, in the outdoor configuration (top- 20 and N = 3, 000), which is the most computationally expensive setup, FastForward estimates the 3D coordinates of spondences, we fed 5,000 correspondences to IMP-RANSAC (Larsson & contributors 2020). which takes 0.1 seconds on average in a Wayspots scene predict the pose estimate. This time could be further reduced by caching the mapping features and avoiding recomputation at inference time. Be- sides, FastForward could potentially use pose head to directly predict the query pose as in Wang et al.] (2025b to avoid PnP. Nevertheless, FastForward provides a highly efficient solution for both mapping and localization. For example, in a Wayspots scene, retrieval takes only seconds, allowing for mapping and localization of new query in just 3.5 seconds.
query image in 0.4 seconds on V100 GPU. Given the 2D-3D corre-
C.4 QUALITATIVE EXAMPLES
We present qualitative results of FastForward across the different test datasets in Figure 6 As pre- viously mentioned, our map representation is constructed using 20 mapping images for outdoor
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Pose Estimation Strategies for MAS13R (Leroy et al., 2024). We report the median errors and accuracy at 10cm, 10 threshold for different strategies to compute the query pose with MASt3R. In the main paper, we report the results of the default approach proposed in MASt3R for the localization tasks. Their default approach uses the matching and 3D point heads to predict the 2D-3D correspondences and PnP as the pose solver, which corresponds to the Matching PnP entry in the table below. We provide the average time across all datasets to localize query image for the different strategies. We also report FostForward as reference. Best results bold for the MAS13R approaches.
Table 9:
Cambridge
Indoor6
RIO10
7-Seenes
e
Acc.
e./
Acc.
ex e+ Acc.
<
Acc.
Time
MASER Matching
PaP
0.9
0.5
0.13/0.7
45,9
0.17
45.1
0.07 / 1.0
71.9
5.6
D.Scale
0.37/
0.07
MAS13R Direct Reg
PaP
4.01/
0.7
43.8
0
35.1
0.08 / 1.2
69.2
4.7
Est.Mat. D.Scule
3.87
0.2
0.13/0.9
45.8
0.29/9.55
29.6
0.08 111
69.8
13.1
FastForward
0,27
26.7
0.04/0.6
91.5
0.18/5.5
40.6
0.04/1. 1.1
90.2
0.4
scenes and 10 for indoor scenes, with 20% of the features sampled from each image. The ground- truth camera pose is shown in green, and FastForward's 's in blue. The mapping scan trajectory is shown in gray, and only the mapping images selected by the retrieval step are visualized. We also display the predicted 3D coordinates of the query points. We observe that accessing only a sub- set of mapping features is sufficient for robust localization, even in challenging scenarios such as scenes with significant illumination variations, repetitive patterns (e.g.. white walls), symmetric ob- jects, or opposing viewpoints. Furthermore, FastForward can handle large-scale scenes, such as those in Cambridge, despite being trained outdoor data limited to Map-free (Arnold al.) 2022), MegaDepth (Li & Snavely 2018). and BlenderMMS Yao et al., 2020) datasets, which present small mid-scale ranges (Map-free) arbitrary scales (MegaDepth BlenderMVS). Moreover, in ad- dition to the robustness against unseen scale ranges, FastForward demonstrates outstanding perfor- mance on some traditional challenges, such as opposing shots. For example, the bottom-left image from the Wayspots dataset (Lawn) illustrates that FastForward is able to estimate an accurate pose even though the mapping scan was taken from an opposing viewpoint.
C.5 POSE ESTIMATION WITH MAST3R
FastForward directly predicts the query scene coordinates to establish 2D-3D correspondences, en- abling pose estimation with the PnP solver (Gao et al.) 2003). In contrast, MASt3R provides descriptor head for estimating the keypoint matches between the image pairs, thus supporting vari- ous correspondence estimation methods and pose solvers. In all previous experiments, we reported MASt3R's results using its default visual localization pipeline, which employs the PnP solver and 2D-3D correspondences derived from its matching and 3D point heads.
One alternative to PnP is to estimate the 2D-2D correspondences via only the matching head and then compute the Essential matrix. Since the Essential matrix up scale, the predicted depth maps from the 3D point head are used to recover the metric scale. We refer to this approach as Ess.Mat + D.Scale in Table 9 For more details, we refer to Leroy et al.) (2024) and Armold et al. (022)). Besides, MASt3R is also able to compute correspondences directly from the predicted point cloud, similar DUSt3R (Wang et al., (024b), without using the matching head. We refer this approach direct regression (Direct Reg). The direct regression approach can be paired with either the PnP or the Essential matrix solver.
As shown in Table 9 the PnP solver performs comparably to the Essential matrix solver, even with- out relying on the ground-trun camera calibration of the reference view. However, a key distinction between the solvers is the computational efficiency. The Essential matrix solver requires solving for the essential matrix for each mapping image, significantly increasing localization time compared to the single-run PnP approach. Furthermore, while the direct approach performs well on some
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Table 10: Pose Estimation Ablations for ALIKED-LG (Zhao al. 2023; Lindenberger al., 2023) with the E5+1 solver (Zheng & Wu, 2015). We report the median enrors, accuracy at 10cm, 10 threshold, and the latencies for the different feature extractor and RANSAC configurations. In the main paper, we report the results with 1,024 keypoints and 1,000 maximum RANSAC iterations as our baseline configuration. We also report FastForward as a reference. Best results in bold for the ES+1 (ALKD-LG) configurations.
ES+1 (ALKD-LG)
Wayspots Dataset
Cambridge Dataset
Num. Kpts
RANSAC
cc (m)
Gp (*)
M0em,
Latency (8)
<e (m)
ep
10cm, 10"
Latency
1,024
1,000
0.51
46.5
0.77
0.3
37.6
1.10
512
1,000
0.69
44.3
0.63
0.19
0.3
38.0
0.90
256
1,000
1.57
17.4
37,9
0.58
0.21
0.4
35.9
0,73
128
1,000
2,70
24.6
27.8
0.56
0,23
0.4
35.0
0,63
1,024
500
0.52
8,3
46,5
0.66
0.23
0.4
37,6
0.82
1,024
100
0.68
9.6
44.2
0.57
0,23
0.4
37,5
0.76
100
5.72
56.0
15.6
0.54
0.31
0.5
26.7
0.54
FastForward
0.17
1.8
51.4
0.49
0.27
0.4
26.8
0.49
datasets, it fails on more challenging scenes, particularly those with dynamic elements, such as in the RIO10 dataset.
C.6 POSE ESTIMATION WITH THE E5+1 SOLVER
As discussed in the main paper, the ES+1 solver recovers the absolute pose from 2D-2D correspon- dences between the query and two or more mapping images. In Table T0 we study the trade-offs be- tween accuracy and latency when pairing the E5+1 solver with the ALIKEEDLightGlue (ALKD-LG) feature matcher. Specifically, we vary the number of keypoints extracted by ALIKED and the max- imum number of RANSAC iterations in the E5+1 solver, analyzing how different configurations impact performance compared to our baseline configuration (1 (1,024 keypoints and 1,000 iterations).
We observe that in well-structured scenes like Cambridge, just few keypoints suffice for accurate pose estimation. Furthermore, latency can be improved by reducing the number of RANSAC iter- ations. Nevertheless, FastForward offers competitive results in Cambridge while remaining faster. We evaluated a lightweight configuration (64 keypoints and 100 RANSAC iterations) to test the latency limits ES+1 (ALKD-LG); however, this configuration reports higher errors and latency (0.54s) compared to FastForward (0.49s). In contrast, on the Wayspots dataset, reducing the number of keypoints significantly degrades performance. Wayspots contains challenging scenes where 2D- 2D matchers may struggle to find stable structures; consequently, optimizing for latency severely impacts the accuracy of the E5+1 (ALKD-LG) approach. In Wayspots, FastForward offers faster and more accurate pose estimates than any of the proposed E5+1 (ALKD-LG) configurations.
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Reviews:
[{"review_id": "5EE64lyAdT", "reviewer": "Reviewer_nKKy", "summary": "The paper consider the problem of visual localization, i.e., the task of estimating the camera pose of a given query image wrt. some scene representation. State-of-the-art localization approaches are typically based on some form of 3D representation of the scene. Depending on the exact representation, building these 3D representations can take minutes to hours. This work thus investigates a scene representation that can be build in seconds instead: Given a query image, image retrieval is used to find the most similar database images representing the scene. The pose of the query image is then predicted wrt. the retrieved database images in a single forward pass. Experiments on standard benchmark datasets show that the proposed approach outperforms other methods that follow a similar paradigm (not building an expensive 3D scene representation) but can potentially perform significantly worse than methods based on a 3D scene representation (see results for Cambridge Landmarks).", "questions": "What is the advantage of using FastForward over the simple E5+1 baseline? Please provide detailed experiments with the E5+1 baseline (using aliked features with the LightGlue matcher) by varying the number of extracted features and reporting the pose accuracy-run-time tradeoff and comparing it to FastForward.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The paper tackles an important practical problem and proposes a sound solution.\n\nThe method is evaluated on standard benchmark datasets and shows good performance compared to the baseline methods.\n\nThe paper is in general easy to read and it is easy to follow its argumentation.", "weaknesses": "My main concern are missing discussions of and comparisons with prior work:\n\n1. Minor: [Kulhanek et al., ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers, ECCV 2022] proposed a transformer architecture that takes N images and their known poses as well as either an additional image or a camera pose as input and, in a single forward pass, either predicts the pose of the additional image or a rendered view from the additional camera pose. In the main paper, they focus on the latter case, i.e., novel view synthesis, while their supp. mat. presents localization results. While their method is not competitive with the proposed one, it seems to me that the paper still should be included in the related work section.\n\n2. Relatively minor: [Torii et al., Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?, TPAMI 2021] (based on [Sattler et al., Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?, CVPR 2017]) asked a similar question to the one posed in the work under submission: Do we really need pre-computed 3D models for accurate visual localization? Their answer is that such models are not necessarily needed. Given the similarities in questions and answers, it seems to make sense to discuss this prior work.\n\n3. Major: An alternative to estimating the pose of a query image by estimating relative poses wrt. each retrieved database image is to compute the pose of the query wrt. multiple database images (in form of semi-generalized relative pose [Zheng & Wu, Structure from motion using structure-less resection, ICCV 2015] or semi-generalized homography [Bhayani et al., Calibrated and Partially Calibrated Semi-Generalized Homographies, ICCV 2021] estimation). The most simplest to implement approach is to estimate the pose of the query image by first estimating the relative pose to one database image from 5 point correspondences (e.g., by estimating the essential matrix) and then using a single additional point correspondence with another database image to recover the scale of the translation [Zhang & Wu, ICCV 2015]. This E5+1 solver has been implemented for quite some time in the PoseLib library and it is quite simple to integrate into a localization pipeline (essentially: perform image retrieval to identify relevant database images, compute 2D-2D matches between the query and the top-retrieved images, estimate the pose by calling PoseLib's E5+1 solver wrapped in RANSAC (PoseLib has this readily implemented). While not as accurate as methods based on 3D representations, it works quite well, as shown in [Panek et al., A Guide to Structureless Visual Localization, arXiv:2504.17636] (the paper compares multiple existing approaches for localization based on representing the scene by a set of images rather than a 3D model, i.e., methods that offer fast representation-computation times). Here are results on multiple datasets obtained by this approach using RoMa feature matching after retrieving the top-20 most relevant database images using EigenPlaces descriptors for retrieval:\n\n**Cambridge Landmarks**\n\nReporting median position errors [cm] / median orientation errors [deg] / accuracy at 10cm and 10deg / accuracy at 20cm and 20deg / accuracy at 25cm and 2deg:\n\n| method | Great Court | King’s College | Old Hospital | Shop Facade | St Mary’s Church | \n| --- | --- | --- |  --- | --- | --- |\n| FastForward | 62 / 4 / 5.1 / 15.5 / - | 24 / 4 / 15.5 / 42.9 / - | 26 / 0.5 / 17.6 / 41.8 / - | 8 / 4 / 64.1 / 92.2 / - | 14 / 0.5 / 28.3 / 75.7 / - |\n| E5+1 | 29 / 0.1 / - / - / 43.2 | 19 / 0.3 / - / - / 60.6 | 24 / 0.5/ - / - / 51.6 | 6 / 0.3 / - / - / 95.1 | 10 / 0.3 / - / - / 86.8|\n\n**Indoor 6**\n\nReporting median position errors [cm] / median orientation errors [deg] / accuracy at 5cm and 5deg / accuracy at 10cm and 10deg / accuracy at 20cm and 20deg\n\nResults for E5+1 are reported as the average over the scenes.\n\n|method | average | \n| --- | --- |\n| FastForward | 4 / 0.7 / - / 91.1 / 98.1 |\n| E5+1 |  1 / 0.15 / 94.9 / - / - |\n\nAs can be seen, this simple baseline performs similar to FastForward and does not require any specific learning. The main potential advantage that I can see for FastForward is that it might be faster at run-time due to requiring only a single forward pass. Then again, one can always extract only a limited number of features per image (e.g., I have good experience extracting 256 or 512 aliked features per image in the context of visual localization), which will make matching and pose estimation faster. As such, I believe that this would need to be shown through detailed experiments.", "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": "cRk1GDXKKp", "reviewer": "Reviewer_Ryg4", "summary": "This article presents a novel visual localization method called FeedForward, which overcomes some of the difficulties associated with traditional mapping approaches. Unlike conventional methods that build complete 3D models or train scene-specific networks, FeedForward uses a limited set of image features embedded in 3D space to estimate camera positions in a single feed-forward pass. The core idea is to represent multiple mapping images as a collection of features embedded in 3D space and use these mapping features to predict image-to-scene correspondences for the query image. It is show with various experiments that the method offers significant advantages in terms of efficiency, scalability and strong generalization capabilities.", "questions": "In the MAST3R/DUST3R paper, localization results are presented for the validation and test sets of the Map-free dataset. Although Figure 4 (supplementary) includes graphs illustrating accuracy as a function of the number of map images in the validation set, a fair comparison with other methods is difficult due to differences in thresholding compared to those used in Evaluation Leaderboard of the Map-free Visual Relocalization. To better assess the model's performance in this context, it is strongly recommended to also provide results obtained using the same evaluation protocol for single view and e.g. top 20.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Although the architectural design is inspired by DUSt3R, the idea of ​​taking a random sample of features from  multiple posed mapping images instead of considering a reference image as input to the encoder-decoder frame to predict accurate 3D coordinates of the query directly into the map's coordinate system is original and rather clever.\n\nThe authors demonstrate that a scene representation consisting of only a few hundreds mapping features obtained with their FeedForward method  is sufficient for accurate visual localization, where building such map is magnitude faster than using competitive methods (including traditional SfM, SCR methods or even Geometric Foundation models such as MAST3R) without  significantly drop in localization accuracy. \n\nThe authors employ a simple yet effective technique for generalizing to new domains by defining one reference mapping image and transforming all other images such that the scene is centered at the origin of the coordinate system. The method computes the scene scale as the maximum camera translation in any spatial coordinate after normalization, which enables metric recovery from normalized predictions using the computed scale factor. This technique allows, on the one hand,  to exploit both metric and non-metric training data and on the other hand to enhances the network's ability to adapt to different scale ranges. \n\nThe model was tested both on classical localization datasets such as Cambridge Landmark, Indoor6, RIO10, 7-Scenes and the more challenging extreme localization dataset Wayspot. \n\nThe article is well-written, well-structured, and easy to follow. I also appreciate that the author's added a section on the model's limitations.", "weaknesses": "The model was tested on various standard datasets such as Cambridge Landmark, Indoor6, RIO10, and 7-Scenes, which are relatively small-scale compared to the scene size. Although these tests provide valuable insights into the model's performance in a variety of settings, it remains unclear how the model would fare on larger-scale datasets like Aachen Day-Night and InLoc compared to e.g MASt3R (Section 4.4 of the corresponding paper) that present more complex and challenging appearance variations.  Evaluating the model's robustness and adaptability to such scenarios would be crucial to better understanding of the model's strengths and limitations in real-world deployment.\n\n\nAt inference time, the model estimates camera poses using PnP-RANSAC with 2D-3D correspondences, which involves an extra optimization step. Given that the query head generates 3D coordinates for each 2D point, it would be interesting to evaluate the accuracy of the pointmap (either evaluating the corresponding depth-map or the  3D reconstruction) directly, in comparison with other Geometric Foundation Model (GFM) methods, the latter ones also evaluated on a per-image basis. To compare to models relying on a pair of images, FeedForward could consider a single image as map, but a denser feature sets, while models predicting pointmaps jointly for multiple views (e.g VGGT) a leave-one-out approach could be employed to construct the map for FeedForward.", "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": "4cGJ4VD66D", "reviewer": "Reviewer_13TE", "summary": "This paper addresses scene coordinate regression for camera relocalization without per-scene model training or reconstruction. To this end, the authors first retrieve a set of mapping images with known poses, then extract and randomly sample ViT features from the mapping images, and finally fuse the mapping features with query features to predict the scene-coordinate map of the query image. The camera pose of the query image is recovered via PnP between the predicted scene-coordinate map and the 2D image coordinates. To demonstrate efficiency, the authors conduct experiments on multiple datasets and achieve results comparable to model-based methods, while being substantially more efficient due to the reconstruction-free design.", "questions": "- First, please refer to the weaknesses.\n- Second, how the proposed solution is robust to inaccurate camera pose of the mapping images?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "* The paper is well-motivated; building and storing maps is expensive, especially for large-scale scenes. Therefore, this work provides an alternative solution that could address this limitation.\n* The method maximizes reuse of existing public pretrained weights (DUSt3R), which makes reproduction and extension easier.\n* The additional reconstruction head on mapping images is reasonable and offers additional supervision during training.\n* The experiments are extensive and cover various scene scales and conditions.", "weaknesses": "* The contribution of this paper is limited:\n\n  * First, in terms of problem formulation, the paper still works on scene coordinate regression. Compared with previous scene-agnostic works, the main change is that only the poses are given for the mapping images, while the scene structure is reconstructed implicitly during inference.\n  * Compared with DUSt3R, the paper extends the encoder to handle multiple images with known poses by adding additional ray encodings to the tokens.\n  * The scene normalization is hard to claim as a contribution: aligning camera poses to an anchor keyframe is common practice in 3D vision; scale normalization is also fairly trivial since SfM reconstructions are scale-normalized by default, so scenes are effectively normalized—especially for model-based camera relocalization.\n\n* The motivation is to make camera localization more efficient; however, the method is still based on scene coordinate regression + PnP. As a result, per-query PnP still dominates computation. In contrast, 3D model-based methods only need to reconstruct the scene once, and the per-query cost of retrieval and PnP is low. Therefore, it is unclear whether the proposed solution is truly more efficient in terms of marginal cost, especially considering the training time.\n\n* The solution still relies on image retrieval, but the paper does not discuss the limitations and failure cases of retrieval—an area where scene-specific localization models can be advantageous. The authors do investigate uniformly sampling the mapping images; however, this alternative provides little new information since it is generally inferior and arguably invalid for the stated setting.", "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": "ndCLUdrlNt", "reviewer": "Reviewer_yDpi", "summary": "The paper introduces a method for visual mapping and localization. The development of this method is motivated by two observations: the recent success of vision models, such as DUSt3R, in generating 3D models and depth information from multiple images of a scene, and the comparatively long time current localization methods require to construct a scene representation that facilitates accurate, robust, and generalizable mapping.\n\nThe core of the method involves training a model to predict the 3D coordinates for pixels in a query image. During training, the model processes query features and sampled features derived from a subset of posed reference images. Each reference mapping feature is augmented with a ray embedding, which encodes the corresponding camera’s position and viewing direction. These augmented reference features are then matched with the query features using an attention mechanism to regress the 3D coordinates of the 2D query pixels. For inference, a small set of reference images is first retrieved from a database (using either an image retrieval process or random sampling). These retrieved images are processed alongside the query image to regress the 3D coordinates of query pixels. The resulting 2D-3D correspondences are subsequently utilized to estimate the query image's absolute pose via a PnP RANSAC procedure.\n\nThe proposed method is trained on a collection of indoor and outdoor datasets. It is evaluated on a separate collection of small- to medium-scale indoor and outdoor localization benchmarks, specifically the Cambridge Landmarks, Wayspots, and Rio10 datasets. The method is compared against existing approaches categorized by how they represent the target scene (the mapping process): (a) methods which use Structure-from-Motion (SfM) to explicitly represent the scene as a 3D model (b) Scene Coordinate Regression (SCR) methods, which encode the scene geometry implicitly within the model weights (require per-scene training) and (c) Relative Pose Regression (RPR) methods, which represent the scene via image encodings and their camera poses followed by direct regression of the relative pose between one or more fetched nearest neighbors (minimal time for mapping).", "questions": "1. Could the authors please provide a comparative table and discussion detailing the trade-offs across all baselines regarding runtime (mapping and querying), storage requirements, and localization accuracy?\n\n2. What are the authors' specific assumptions and engineering considerations required for scaling this method to significantly larger, kilometer-scale environments?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The method presents a novel approach to visual mapping and localization, which enables fast mapping time and the demonstrated ability to generalize to scenes not seen during training.\n2. The proposed method outperforms recent state-of-the-art RPR methods (minimal mapping time) and outperform SCR methods on several scenes from the Wayspots dataset\n3. The paper provides a clear and well-articulated motivation for the proposed architecture, grounding the work in the limitations of existing methods and the capabilities of modern 3D vision models.\n4. The paper is well structured and provides ablation study examining the impact of scale normalization and map size (number of reference images/features), providing insight into the method's core components and hyperparameter sensitivity.", "weaknesses": "1. The evaluation lacks a comprehensive discussion of the runtime and storage tradeoffs relative to other baseline methods. Specifically, the proposed approach relies on local feature matching and extraction, which introduces an overhead either at inference (runtime) or in storage (pre-computed features). This is a critical comparison point against RPR methods, which typically only require global feature matching, and against SCR methods. While SCR may incur a long initial training/mapping time, its inference time is generally very fast, making the accuracy/speed trade-off unclear. The paper also lacks the discussion of the one-off mapping effort versus the repeated query time to fully contextualize the value proposition of the proposed technique across all phases (training/mapping/querying).\n\n2. The evaluation is confined to small- to medium-scale scenes. Consequently, the method's scalability to larger, city-scale environments is not demonstrated. As structure-based methods typically show strong performance and robustness in such large-scale settings this comparative assessment (or at least discussion) is necessary.", "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)