Relative camera pose is a basic geometric cue for visual re-localization and scene understanding. When using images alone, visual evidence may not sufficiently support reliable estimation of 3D-consistent motion. The challenge is especially acute in unfamiliar and ever-changing environments for real-time applications, where a system must infer spatial structure under tight time constraints from what it sees rather than rely on scene-specific reconstruction or training. This raises a fundamental question: how can live 3D camera motion be estimated accurately for re-localization in unseen environments while drawing on enough scene context to resolve pose ambiguity? We introduce MultiLoc, a multi-view-guided relative pose regressor trained at scale to achieve spatial and geometric consistent representations for robust visual re-localization in unseen scenarios with high inference speed. Specifically, MultiLoc creates a minimal 3D-sub-scene representation of the environment and efficiently transforms it into 3D spatially and geometrically consistent features in a single forward pass, enabling precise pose estimates with high inference speed gains. Across diverse indoor, outdoor, and in-the-wild visual re-localization benchmarks---Indoor6, Cambridge Landmarks and WaySpots---MultiLoc consistently outperforms state-of-the-art relative pose regression methods. We also highlight that MultiLoc, a pose regressor, also performs competitively with inference-heavy and structure-based approaches while retaining sub-second inference and generalizing to unseen scenes. Given a small posed support set, it also surpasses relative pose regression, feature-matching, and non-regression methods on several relative camera pose benchmarks. Code will be released.
Figures & tables
Figure 1: One forward pass, no scene-training or map. Given a query and K reference images with 6-DoF poses—a camera constellation, or 3D sub-scene (left)—MultiLoc achieves sub-scene consistent spatial and geometric representations by jointly fusing multi-view images and the available poses. By doing so, just with a single forward pass, MultiLoc matches the accuracy of feature matching (MC) and scene-coordinate regression (SCR) methods while offering one of the fastest inference speed across all the baselines on Indoor-6 (right).
Figure 2: MultiLoc Architecture overview. The retrieved images’ extrinsics are embedded via our camera encoders into camera tokens y and added with learnable camera tokens l , while all images pass through DINO-v2 to obtain patch tokens. All the tokens are then concatenated and processed by alternating-attention (AA) transformer blocks. Finally, camera tokens are used to predict relative poses between query and reference images using the camera head and metric scale is recovered by the pre-computed scene-scale s . The red arrows denote the flow of camera tokens.
Method
ACID
MegaDepth-1500
ScanNet-1500
5∘
10∘
20∘
5∘
10∘
20∘
5∘
10∘
20∘
Efficient LoFTR
–
–
–
52.80
69.19
81.18
19.20
37.00
53.60
RoMa
57.98
68.53
76.62
60.92
75.63
85.69
34.13
55.28
71.69
DUSt3R
21.50
35.95
49.70
27.90
46.00
63.30
23.81
45.91
65.57
MASt3R (Ess.)
54.42
66.23
75.26
42.79
62.55
77.61
35.25
57.64
74.48
Pair
NoPoSplat
48.60
61.70
72.80
–
–
–
31.80
53.80
71.70
Table 1: Relative camera pose estimation results on ACID, MegaDepth-1500, and ScanNet-1500. Non-PR denotes non-pose-regression methods, and PR relative pose regression. Shaded rows use multiple views; unshaded rows are pairwise. Red indicates the best and bold the second-best result.
Figure 3: Visual re-localization on the Indoor-6 benchmark. (a) Top row shows the query and retrieved reference images . Bottom row shows the Indoor6 reconstruction, where MultiLoc’s predicted pose closely matches the ground truth in position and orientation. Blue frustums indicate reference poses. (b) Median translation / rotation error averaged over six scenes (cm / ∘ ; lower is better) and per-query latency; bold is best within each category. Per-scene results are in appendix.
Figure 4: MultiLoc v/s ReLoc3r trajectory error depiction on GreatCourt sequence of Cambridge dataset. Gray dashed lines indicate the translational error between ground truth and predicted poses. MultiLoc achieves significantly lower error by leveraging 3D sub-scene context from multiple images and their pose, whereas ReLoc3r’s reliance on pairwise images lead to higher drift.
Category
Method
GreatCourt
Kings
OldHosp
ShopFac
StMary
Avg
Map. Time
Latency
MC
MASt3R + Kapture
0.10 / 0.10
0.08 / 0.12
0.13 / 0.24
0.03 / 0.13
0.04 / 0.12
0.08 / 0.14
>10min
0.85 s
E5+1 (RoMa)
0.37 / 0.16
0.21 / 0.35
0.29 / 0.64
0.07 / 0.27
0.11 / 0.37
0.21 / 0.36
<1min
7.30 s
SCR
ACE
0.44 / 0.20
0.31 / 0.40
0.30 / 0.60
0.06 / 0.30
0.20 / 0.60
0.26 / 0.42
>2min
∼ 0.10 s
GLACE
0.19 / 0.10
0.19 / 0.30
0.18 / 0.40
0.05 / 0.20
0.10 / 0.30
0.14 / 0.26
>9min
∼ 0.10 s
RPR
ReLoc3r
1.22 / 0.73
0.42 / 0.36
0.62 / 0.55
0.13 / 0.58
0.34 / 0.58
0.55 / 0.56
<1min
0.23 s
GeLoc3r
1.07 / 0.52
0.41 / 0.34
0.64 / 0.54
0.15 / 0.64
0.27 / 0.58
0.51 / 0.52
<1min
–
Table 2: Visual re-localization benchmark on Cambridge Landmarks with median errors as (m / ∘ ). Green, blue, and red cells indicate the best, middle, and worst comparative results, respectively.
Categ.
Method
Bears
Cubes
Insc
Lawn
Map
SqB
Statue
Tendrils
Rock
WinterSign
Avg
MC
E5+1 (RoMa)
2.2 / 0.72
8.0 / 0.85
7.9 / 1.00
11.6 / 3.29
7.7 / 0.92
12.3 / 0.74
59.3 / 1.48
22.2 / 3.59
3.1 / 0.73
170.0 / 1.55
30.4 / 1.49
SCR
ACE
3.6 / 1.15
5.3 / 0.76
10.0 / 1.50
120.0 / 37.00
7.5 / 1.10
5.7 / 0.70
595.0 / 15.70
166.0 / 45.70
2.9 / 0.74
423.0 / 1.19
134.0 / 10.55
GLACE
3.0 / 0.90
5.9 / 0.80
10.0 / 1.30
115.0 / 26.30
7.2 / 1.00
5.7 / 0.70
517.0 / 10.30
155.0 / 32.20
3.2 / 0.80
465.0 / 1.30
128.7 / 7.56
RPR
ReLoc3r
13.1 / 3.63
69.4 / 2.14
33.0 / 3.28
41.5 / 11.03
16.4 / 2.00
27.7 / 1.73
331.2 / 11.00
82.9 / 15.31
8.3 / 1.34
607.0 / 5.15
123.1 / 5.66
MultiLoc (ours)
4.0 / 0.77
8.3 / 0.93
9.2 / 1.09
16.0 / 2.84
6.9 / 1.12
9.9 / 0.75
89.0 / 1.46
34.0 / 3.55
4.9 / 0.72
65.0 / 2.61
24.7 / 1.58
Table 3: Visual re-localization benchmark on the WaySpots dataset in median translation / rotation error as (cm / ∘ ). MASt3R+Kapture is omitted because it’s superset, Map-Free, was used to train it.
Figure 5: Visual Localization performance (average across all scenes) and inference speed comparison across varying numbers of input views (k) on the Cambridge Landmarks.
Visual Reloc.
Camera Pose Est.
State
Cambridge
WaySpots
ACID
ScanNet-1500
Frozen (no pose)
41.0 / 0.374
66.6 / 1.80
22.55 / 40.85
38.82 / 60.31
MultiLoc (with pose)
29.5 / 0.35
24.7 / 1.39
76.56 / 87.15
62.34 / 76.32
Table 4: Median pose error [cm / ∘ ] for visual re-localization (all 5 Cambridge, 10 WaySpots sequences) and AUC@5/10 for camera pose estimation (ACID, ScanNet-1500). : frozen (no pose intrusion); : trained MultiLoc. Red denotes best, blue second best result.
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 6: Ablation study for MultiLoc’s architecture . Using learnable camera token only for the last image (top) leads to unstable training and sudden gradient jumps. Whereas, using learnable tokens for all the images in a sequence (bottom) and regressing upon them reduces this instability.
Methods
GreatCrt
KingsClg
OldHosp
ShopFac
StMarys
Avg Rot ( ∘ ↓ )
Avg Trans. (m ↓ )
Umeyama Align.
0.44 / 0.37
0.33 / 0.41
0.39 / 0.70
0.09 / 0.33
0.23 / 0.51
0.46
0.30
Motion Avg.
0.45 / 0.25
0.32 / 0.33
0.40 / 0.52
0.08 / 0.28
0.24 / 0.36
0.35
0.30
Scale ( s )
0.45 / 0.26
0.32 / 0.32
0.39 / 0.53
0.08 / 0.29
0.23 / 0.37
0.35
0.29
Appendix
Table 5: Scale Recovery benchmark of MultiLoc (ours) on Cambridge dataset for visual re-localization. Median translation error is in meters (m) and median rotation error in degrees ( ∘ ).
Figure 7: RoMA comparison. Pairwise (top) vs. best-reference (bottom) matching. While additional views increased inliers from 2437 to 2999 (top left in red), pose error, max(trans,rot), rose from 1.90 ∘ to 3.21 ∘ showing failure to exploit multi-view cues.
Category
Method
Scene1
Scene2a
Scene3
Scene4a
Scene5
Scene6
Avg
MC
MASt3R + Kapture
3.4 / 0.31
3.4 / 0.15
2.9 / 0.28
2.5 / 0.39
4.2 / 0.45
2.3 / 0.23
3.1 / 0.30
E5+1 (RoMa)
2.2 / 0.43
2.1 / 0.23
1.5 / 0.37
3.0 / 0.68
3.8 / 0.65
1.3 / 0.31
2.3 / 0.45
SCR
ACE
17.7 / 2.40
8.0 / 0.80
9.0 / 1.60
4.6 / 0.90
21.6 / 4.00
5.2 / 0.90
11.0 / 1.80
GLACE
3.8 / 0.70
3.9 / 0.40
2.9 / 0.60
2.3 / 0.50
6.1 / 0.90
2.1 / 0.40
3.5 / 0.60
FastForward
–
–
–
–
–
–
4.0 / 0.60
RPR
ReLoc3r
8.0 / 0.60
11.0 / 0.78
4.0 / 0.44
9.0 / 0.87
29.0 / 1.60
3.0 / 0.52
10.7 / 0.80
Appendix
Table 6: Visual re-localization benchmark on the Indoor-6 dataset. We report median translation / rotation error (cm / ∘ ); lower is better. The best result within each category is in bold . “–” denotes results not yet available.
Category
Method
GreatCourt
Kings
OldHosp
ShopFac
StMary
Avg
MC
HLoc (SP+SG)
0.16 / 0.10
0.12 / 0.20
0.15 / 0.30
0.04 / 0.20
0.07 / 0.20
0.11 / 0.20
MASt3R + Kapture
0.10 / 0.10
0.08 / 0.12
0.13 / 0.24
0.03 / 0.13
0.04 / 0.12
0.08 / 0.14
E5+1 (RoMa)
0.37 / 0.16
0.21 / 0.35
0.29 / 0.64
0.07 / 0.27
0.11 / 0.37
0.21 / 0.36
SCR
ACE
0.44 / 0.20
0.31 / 0.40
0.30 / 0.60
0.06 / 0.30
0.20 / 0.60
0.26 / 0.42
GLACE
0.19 / 0.10
0.19 / 0.30
0.18 / 0.40
0.05 / 0.20
0.10 / 0.30
0.14 / 0.26
FastForward
0.62 / 0.40
0.24 / 0.40
0.26 / 0.50
0.08 / 0.40
0.14 / 0.50
0.27 / 0.40
Appendix
Table 7: Visual re-localization benchmark on the Cambridge Landmarks dataset in median translation / rotation error as (m / ∘ ). Green, blue, and red cells indicate the best, middle, and worst comparative results, respectively.
Figure 8: Failure cases when retrieved images are not co-visible. The following cases illustrate scenarios where co-visibility-based retrieval features fail to identify appropriate reference images. We demonstrate using top-5 of 10 such reference images per query. Such instances result in significant translation ( purple dashed line ) and rotation errors between the ground truth and predicted camera poses.
Indoor visual relocalization plays a critical role in emerging spatial and embodied AI applications. However, prior research was predominantly devoted to low-level vision schemes, struggling to perceive scene semantics and compositions, which limits both interpretability and applicability. In this paper, we explore the issue of how to organize rich object information in a scene, including semantics, layout, and geometry, into a structured map representation, thereby utilizing object units exclusively to drive the camera relocalization task. To this end, we propose OpenReLoc, a camera relocalization system designed to provide scene understanding and accurate pose estimation capabilities. Leveraging recent foundation models, we first introduce a multi-modal mechanism to integrate open-vocabulary semantic knowledge for effective 2D-3D object matching. Additionally, we design object-oriented reference frames as position priors, paired with a reference frame selection strategy based on the Distance-IoU (DIOU), enabling extension to scalable scenes. Moreover, to ensure stable and accurate pose optimization, we also propose a dual-path 2D Iterative Closest Pixel loss guided by object shape. Experimental results demonstrate that OpenReLoc achieves superior relocalization recall and accuracy across various datasets. Our source code will be released upon acceptance.
Zhaopeng Cui, Jiarui Hu, Jingbo Liu +7
1State Key Lab of CAD&CG, Zhejiang University · 2Ant Group
Standard visual localization methods typically require offline pre-processing of scenes to obtain 3D structural information for better performance. This inevitably introduces additional computational and time costs, as well as the overhead of storing scene representations. Can we visually localize in a wild scene without any off-line preprocessing step? In this paper, we leverage the online inference capabilities of feed-forward 3D reconstruction networks to propose a novel map-free visual localization framework L3. Specifically, by performing direct online 3D reconstruction on RGB images, followed by two-stage metric scale recovery and pose refinement based on 2D-3D correspondences, L3 achieves high accuracy without the need to pre-build or store any offline scene representations. Extensive experiments demonstrate L3 not only that the performance is comparable to state-of-the-art solutions on various benchmarks, but also that it exhibits significantly superior robustness in sparse scenes (fewer reference images per scene).
Given a compact semantic scene graph, long-term indoor video relocalization estimates a map-frame trajectory after lighting and furniture changes. Visual methods rely on appearance and become unreliable under these changes; localizing one frame at a time from object classes and geometry instead leaves sparse, ambiguous evidence. We introduce VideoReloc, whose adaptive clips use odometry to gather spatial evidence until object and motion criteria are met, adapting query length to the observed scene. Its run-level decision rechecks conflicting placements using evidence accumulated across connected clips, stabilizing the trajectory beyond adjacent-clip tracking. Hypothesis-first registration proposes poses from object triplets and verifies each using clip-wide object centers and box surfaces. Orientation-aware refinement uses box faces, gravity and wall directions to resolve ambiguity in camera orientation and refine the full pose. This reframes sparse-map relocalization as verification of spatially extended video queries, moving discriminative support from stored appearance to temporal context and permitting a 100 kB map of class-labelled boxes. On RIO10 and ReplicaCAD, the all-frame localization success rate at 1 m/10∘ is 73.5% and 61.1% under causal evaluation, rising to 90.6% and 74.8% with clip closure. The evaluated per-frame scene coordinate regressors reach up to 47.6% and 49.8%, respectively, with maps of 12.6-42 MB. Project page: https://videoreloc.github.io
Qianru Li, Xuyang Chen, Xuqin Wang +6
Technical University of Munich, Munich, Germany · Huawei Hilbert Research Center (Dresden), Germany