OpenCVL: An Open, Diverse, and Large-Scale Dataset for Fine-Grained Cross-View Localization

Abstract: Fine-grained Cross-View Localization (CVL) estimates the precise position and orientation of a ground-level image by aligning it with geo-referenced aerial imagery, offering a scalable alternative to Global Navigation Satellite Systems (GNSS) in challenging urban environments. Existing datasets rely on data collected with high-end sensor suites, which inherently limit image diversity and scalability. While in-the-wild images are abundant, their noisy geo-tags make them unsuitable for reliable evaluation. To bridge this gap, we introduce OpenCVL, a large-scale, diverse, and open dataset containing 617,388 ground-aerial image pairs spanning 41 cities across four European countries. All images are sourced from permissive platforms, ensuring long-term accessibility and supporting open and reproducible research. The training set combines images captured with high-end sensors with diverse in-the-wild imagery. We further develop a data curation framework that filters and corrects pose annotations to construct reliable in-the-wild evaluation data. In addition, OpenCVL includes dedicated cross-area and snowy test sets to assess generalization and robustness. Experiments with a state-of-the-art CVL model on OpenCVL show that incorporating noisy in-the-wild data consistently improves performance on clean test sets, suggesting a promising direction for scaling CVL with diverse real-world imagery.

• The paper presents OpenCVL, a diverse and large-scale dataset featuring 617,388 ground-aerial image pairs across 41 European cities, designed to enhance fine-grained cross-view localization (CVL).
• The dataset includes accurately labeled sensor-derived data from ZOD and diverse crowd-sourced imagery from Mapillary, allowing experimentation with mixed-quality annotations and demonstrating that controlled incorporation of noisy labels can improve localization performance.
• Results on OpenCVL show that diverse data augments CVL systems, achieving up to 6.72 m mean localization error on the cross-area split, although challenges remain in handling unstructured environments, notably with 7.90 m and 26.07° as mean localization and orientation errors, respectively, in the in-the-wild setting.

Dataset rationale and scope

Fine-grained cross-view localization (CVL) estimates the planar position and yaw of a ground-level camera by aligning a perspective image with georeferenced aerial imagery. Unlike coarse ground-to-aerial retrieval, the task requires metric localization within the aerial tile and explicit orientation estimation. This distinction makes annotation quality central: a label that is adequate for a 50-m geolocation benchmark is not necessarily suitable for meter-scale pose evaluation. OpenCVL addresses this data problem by combining accurate sensor-derived supervision with large quantities of heterogeneous, crowd-sourced imagery (2608.25274).

The dataset contains 617,388 ground–aerial image pairs spanning 41 cities in Sweden, Poland, Norway, and the Netherlands, with coverage exceeding 7,000 square kilometers. Ground images originate from the Zenseact Open Dataset (ZOD) and Mapillary, while aerial imagery is obtained from national mapping agencies. The sources are distributed under permissive licenses or public-use terms, in contrast to several established CVL benchmarks whose imagery is derived from restricted Google Maps or Google Street View sources.

OpenCVL is structured around a deliberate trade-off between annotation precision and visual diversity. ZOD contributes vehicle-mounted images captured with calibrated cameras, LiDAR, and high-end GNSS. Mapillary contributes images captured by pedestrians, cyclists, and drivers using heterogeneous cameras and mounting configurations. The latter data are substantially more diverse but inherit noisy GNSS and structure-from-motion poses. The paper’s central empirical claim is that these noisy labels can nevertheless be useful for training, provided their influence is controlled and evaluation labels are independently corrected.

Figure 1: OpenCVL combines ZOD and Mapillary ground imagery with open aerial imagery across 41 cities in four European countries.

The aerial tiles cover $100 \times 100$ m regions at ground sampling distances between 0.04 and 0.16 m per pixel. The ground-camera position is sampled within a central $40 \times 40$ m region, yielding offsets of up to 20 m along each map axis. This construction follows the evaluation geometry used in prior fine-grained CVL work while expanding the distribution of camera viewpoints, environmental conditions, and geographic contexts.

Annotation construction and evaluation design

The evaluation protocol separates reliable supervision from reliable assessment. The training set contains 238,212 ZOD images and 341,540 Mapillary images, for a total of 579,752 pairs. The remaining data are divided into validation, cross-area, snowy, and in-the-wild test sets containing 14,756, 18,504, 3,015, and 1,361 pairs, respectively.

ZOD evaluation poses are verified by projecting both the camera poses and associated LiDAR point clouds into aerial imagery. Samples exhibiting discrepancies between building boundaries, LiDAR structure, and projected camera location are removed. The cross-area split uses regions not overlapping with training data, whereas the snowy split measures robustness to winter appearance changes. Because winter images are also present during training, the snowy benchmark does not measure strict cross-season generalization; it measures robustness to a difficult but observable environmental condition.

Figure 2: ZOD evaluation poses are checked through geometric agreement between projected LiDAR, camera locations, and aerial structures, including snowy scenes.

The in-the-wild test set requires a more elaborate correction pipeline. Mapillary images are first retrieved near ZOD coverage using raw GNSS positions. Candidate pairs are retained only when local feature matching between a Mapillary image and a nearby ZOD anchor produces geometrically verified overlap. The ZOD LiDAR point clouds are globally registered using high-end GNSS poses. Matches between the two RGB images then induce 2D–3D correspondences from Mapillary pixels to globally positioned LiDAR points.

A Perspective-n-Point estimate initializes the Mapillary pose, which is jointly refined with camera intrinsics and distortion parameters through Levenberg–Marquardt optimization in COLMAP. Samples with excessive reprojection error are discarded, and the remaining poses undergo manual verification both through LiDAR projection into the Mapillary image and through aerial-image inspection.

Figure 3: Mapillary pose correction uses ZOD–Mapillary feature matches and globally registered ZOD LiDAR to estimate and refine camera pose and calibration.

This procedure is important methodologically because it does not treat crowd-sourced metadata as ground truth. Raw GNSS and Mapillary’s OpenSfM poses are used for candidate retrieval and comparison, but not accepted uncritically for fine-grained evaluation. The resulting in-the-wild benchmark therefore measures performance under unconstrained image formation while retaining substantially more accurate pose annotations.

Training methodology

The principal baseline is Loc$^2$, a local-correspondence-based CVL model that predicts dense ground–aerial correspondences and estimates a 3-DoF pose through depth lifting and scale-aware Procrustes alignment. The model receives camera intrinsics and monocular depth estimates from Depth Anything V2. For ZOD images, predicted depth scales are further aligned with LiDAR measurements.

To accommodate camera heterogeneity, all ground images are resized to a common resolution and their intrinsics are adjusted accordingly. This design separates camera-specific geometric calibration from the learned matching representation: the feature-matching stage can operate across cameras, while intrinsics enter during geometric lifting.

The training objective applies a fixed global weight to Mapillary losses. The authors evaluate weights of 0.1, 0.3, 0.5, and 0.7, finding that 0.3 performs slightly best. This is a relatively simple treatment of label uncertainty: it does not estimate per-image uncertainty, model structured GNSS error, or distinguish different failure modes in Mapillary metadata. Its value is nevertheless demonstrated empirically.

Results on OpenCVL

The contrast with KITTI is substantial. A Loc$^2$ model trained on KITTI achieves mean localization errors between 15.57 and 20.07 m across OpenCVL splits without an orientation prior, while its mean orientation errors range from 70.72° to 83.30°. The KITTI-trained model therefore fails to transfer from a narrow, vehicle-centric distribution to OpenCVL’s heterogeneous viewpoints.

Training on ZOD alone reduces mean localization error to 7.55 m on the cross-area split, 8.04 m on the snowy split, and 8.61 m on the in-the-wild split. Mean orientation errors are 16.20°, 8.87°, and 35.59°, respectively. The in-the-wild orientation result is notably worse than the ZOD-derived benchmarks, showing that camera placement, viewing direction, and capture platform remain major sources of ambiguity even after pose correction.

Adding Mapillary data without reweighting improves mean localization to 6.87, 8.04, and 8.16 m on the three test sets. With the 0.3 Mapillary loss weight, the results become:

Training configuration	Cross-area mean loc.	Snowy mean loc.	In-the-wild mean loc.	In-the-wild mean orientation
OpenCVL, ZOD only	7.55 m	8.04 m	8.61 m	35.59°
ZOD + Mapillary, unweighted	6.87 m	8.04 m	8.16 m	25.87°
ZOD + Mapillary, weight 0.3	6.72 m	7.80 m	7.90 m	26.07°

The strongest result is that noisy, diverse Mapillary supervision improves performance not only on the in-the-wild split but also on clean ZOD-derived evaluation. Relative to ZOD-only training, the weighted model reduces mean localization error by 0.83 m on cross-area evaluation and by 0.71 m in the wild. Its mean orientation error on the snowy split decreases from 8.87° to 5.09°. This contradicts the simple expectation that noisy crowd-sourced poses necessarily degrade fine-grained localization when mixed with accurate supervision. The implication is that distributional diversity can outweigh moderate annotation noise, although the gain depends on attenuating the noisy loss.

The results also expose the difficulty of the in-the-wild setting. Even the best reported model has a mean localization error of 7.90 m and a mean orientation error of 26.07°. Thus, OpenCVL does not merely provide additional training samples; it changes the benchmark from predominantly vehicle-centered matching to a setting in which sidewalk views, oblique viewpoints, unusual camera heights, and opposite travel directions create genuine geometric ambiguity.

The auxiliary baseline comparison reinforces the importance of local correspondences. Loc$^2$ obtains mean localization errors of 6.72, 7.80, and 7.90 m on cross-area, snowy, and in-the-wild sets. CCVPE obtains 11.95, 10.81, and 16.24 m, while HC-Net obtains 13.87, 12.82, and 12.66 m. HC-Net’s orientation errors are particularly high, ranging from 85.89° to 99.73°. The paper attributes this weakness to homography-based preprocessing and the use of a fixed manually selected transformation for images with varying fields of view and unknown orientation. The comparison supports correspondence-based geometric estimation, but it also depends on implementation choices: CCVPE uses a fixed 120° horizontal field of view, and HC-Net uses fixed homography parameters.

Figure 4: HC-Net preprocessing warps each perspective image into a bird’s-eye-view approximation using a fixed homography design.

Cross-dataset transfer to KITTI

OpenCVL also provides evidence that its diversity is not useful only within its own benchmark. Without an orientation prior, the full OpenCVL-trained model reaches 11.25 m mean and 9.40 m median localization error on the KITTI cross-area split, compared with 11.71 m and 9.11 m for a KITTI-trained model. ZOD-only OpenCVL training performs worse, at 12.04 m mean and 10.34 m median. The result is notable because Mapillary labels are noisy and OpenCVL has a different geographic and camera distribution from KITTI.

Orientation transfer is less successful. The OpenCVL-trained model has 71.17° mean and 46.96° median orientation error, compared with 55.18° and 33.41° for KITTI training. The error histogram contains more approximately 180° failures, indicating confusion between forward and opposite driving directions. This behavior is consistent with the fact that KITTI’s road-oriented distribution provides strong domain-specific cues for directional disambiguation.

Figure 5: Orientation-error histograms reveal more opposite-direction predictions when the model is trained on OpenCVL and evaluated on KITTI.

The paper further evaluates OpenCVL as pretraining data for KITTI. With a $\pm 10^\circ$ orientation prior, KITTI-only training yields 1.13 m mean localization error on the same-area split and 5.60 m on cross-area. Joint OpenCVL–KITTI training slightly improves these values to 1.12 and 5.43 m but increases cross-area orientation error from 3.32° to 4.06°. Sequential OpenCVL pretraining followed by KITTI fine-tuning performs best: 0.93 m same-area mean localization, 0.61 m median localization, 1.62° mean orientation, and 5.07 m cross-area mean localization. The result suggests that heterogeneous pretraining and target-domain adaptation are more compatible than directly mixing datasets with different orientation-label conventions and priors.

Effectiveness of pose correction

The pose-correction framework is evaluated by fine-tuning a ZOD-pretrained model with alternative Mapillary annotations. Without Mapillary fine-tuning, the validation subset produces 8.05 m mean localization error and 26.73° mean orientation error. Fine-tuning with raw GNSS reduces these values to 7.41 m and 22.09°. OpenSfM poses yield 7.20 m and 23.44°. Corrected poses achieve 6.92 m and 17.98°, with corresponding median errors of 5.14 m and 7.01°.

Mapillary annotation	Mean localization	Median localization	Mean orientation	Median orientation
No fine-tuning	8.05 m	5.75 m	26.73°	10.57°
Raw GNSS	7.41 m	5.73 m	22.09°	7.70°
OpenSfM	7.20 m	5.23 m	23.44°	9.27°
Corrected pose	6.92 m	5.14 m	17.98°	7.01°

These measurements establish two distinct points. First, even noisy Mapillary labels can provide useful adaptation signals. Second, better labels produce materially better downstream supervision, particularly for orientation. The corrected-pose improvement over raw GNSS is 0.49 m in mean localization and 4.11° in mean orientation. The paper therefore substantiates its claim that Mapillary imagery is unsuitable as a reliable fine-grained test set without additional geometric curation.

Limitations and open questions

OpenCVL’s geographic scope is broad relative to existing fine-grained CVL datasets but remains confined to four European countries. Its apparent advantages may therefore partially reflect European urban morphology, mapping conventions, and weather distributions. The dataset also uses orthophotos rather than a uniform true-orthographic representation; orthophotos can retain facade visibility and residual projection effects, especially in dense urban areas. The authors note that one city provides both representations, but the experiments do not quantify their effect.

The Mapillary correction pipeline depends on proximity to ZOD coverage, sufficient cross-image visual overlap, reliable ZOD LiDAR registration, local-feature matching, reprojection-error filtering, and manual verification. Consequently, it does not establish a general procedure for correcting arbitrary crowd-sourced images in regions without high-quality anchor trajectories. The 1,361-image in-the-wild test set is accurate by construction but may be selection-biased toward images that can be registered to ZOD.

The fixed global Mapillary loss weight is another unresolved assumption. A single value of 0.3 is selected empirically, and no per-sample uncertainty model or robustness analysis over alternative weighting schedules is reported. It remains open whether uncertainty-aware objectives, robust estimators, or semi-supervised treatment of the noisy samples would improve the trade-off between diversity and label fidelity.

Finally, localization and orientation remain coupled in an uneven way. OpenCVL training transfers reasonably for KITTI position estimation but produces substantially more opposite-direction predictions. The unresolved question is whether this failure is best addressed through orientation-aware data balancing, explicit directional cues, calibrated uncertainty, or a representation that separates scene correspondence from travel-direction inference.

Conclusion

OpenCVL contributes an open and large-scale benchmark for fine-grained CVL whose principal distinction is the combination of accurate sensor-derived data with diverse in-the-wild imagery. Its pose-correction framework converts a subset of noisy Mapillary observations into usable evaluation data through ZOD image matching, LiDAR-based 2D–3D registration, nonlinear pose refinement, and manual verification.

The experiments show that diversity is not merely an evaluation burden: when appropriately downweighted, Mapillary data improves localization on clean, snowy, in-the-wild, and cross-dataset tests. At the same time, the remaining 7.90 m mean localization and 26.07° mean orientation errors on the in-the-wild split demonstrate that unconstrained CVL remains substantially harder than conventional vehicle-based benchmarks. OpenCVL consequently functions both as a training resource and as a diagnostic benchmark for the limits of current correspondence-based localization systems (2608.25274).