Open Problems

Aerial, Satellite & BEV Perception

Robust Cross-View Perception and Localization Without Paired Ground-Level Street Imagery

Barrier to removeOpen
Strong candidate · 4/5 runs4 papers report this25% from 2025+

Generated automatically from the limitations stated in 4 papers (ECCV, EMNLP, CVPR), listed under Evidence. It is not a paper, and it does not come from papers submitted to CSPaper.

The problem

Current cross-view visual geolocalization, 3D scene reconstruction, and BEV perception models strictly depend on dense, spatially calibrated ground-level panoramas paired with satellite imagery or pre-existing 3D city meshes. In rural, sparsely populated, or underdeveloped regions, ground-level street-view coverage is either completely absent or uncalibrated. This reliance on dense ground-level imagery structurally restricts cross-view perception algorithms to major metropolitan areas with dedicated mapping fleets.

Why it matters

Enables visual localization, BEV understanding, and 3D urban mapping in unmapped rural regions, developing nations, and disaster-affected zones where ground-level panoramic survey infrastructure does not exist.

Ways to approach it

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  1. 1

    Missing-modality robustness benchmark: Systematically evaluate existing cross-view retrieval, BEV mapping, and visual geolocalization models under controlled degradations of ground-view density, missing orientation calibration, and complete absence of street-view data. Measure the decay of Top-1/Top-5 retrieval recall and BEV layout IoU as paired ground data is progressively removed across diverse geographic regions.

  2. 2

    Unpaired and weakly-supervised cross-view alignment: Train cross-view feature representations using overhead satellite/aerial imagery matched against sparse, uncalibrated crowdsourced photos (e.g., Mapillary / OpenStreetMap) without requiring rigid 1-to-1 spatial calibration or high-overlap sky constraints. Measure geolocalization recall and cross-view retrieval accuracy against fully calibrated baseline models.

  3. 3

    Overhead-only ground geometry and facade estimation: Evaluate learning-based models that predict ground-level appearance, road layouts, and facade structures directly from multi-angle satellite imagery without relying on city-specific ground priors or pre-existing 3D meshes. Measure structural Chamfer distance and facade semantic accuracy against full cross-view baseline methods.

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Why it might fail

If overhead-only satellite resolution is fundamentally insufficient to resolve ground-level ambiguities (e.g., under building overhangs or dense tree canopies), or if large foundation vision models trained on Internet-scale uncurated web images make explicit cross-view spatial alignment trivial without specialized research architectures.

Evidence

Each paper's own statement of the limitation, verbatim.

Nearest existing work

Related open problems

Aerial, Satellite & BEV Perception

Effect to explainOpen

Benchmarking and Mitigating UAV Perception Failure Under Rapid Motion Blur, Scale Shifts, and Occlusion

Current perception models deployed on unmanned aerial vehicles achieve low absolute accuracy on standard downstream tasks (e.g., recall under 7.5% in scene understanding, under 40% in action recognition, and under 30% m_vIoU in tracking). These failures are driven by real-world flight conditions—rapid camera motion blur, extreme resolution and altitude shifts, severe occlusion, and small target sizes—that standard benchmarks do not systematically couple. As a consequence, aerial vision pipelines remain brittle and cannot be reliably trusted for autonomous navigation, search-and-rescue, or monitoring in complex flight regimes.

Strong candidate · 4/5 runs3 papers report this33% from 2025+

Aerial, Satellite & BEV Perception

Barrier to removePartly addressed

Robustness and Degradation Benchmarking of Map-Conditioned Perception Under Imperfect OpenStreetMap Priors

Contemporary BEV perception, aerial localization, and landmark-guided navigation models increasingly condition their visual representations on OpenStreetMap (OSM) vector geometry and metadata. In practice, OSM data exhibits severe geographic disparities, missing road classes, topological errors, and incomplete landmark tags. Because existing methods assume clean and complete vector maps as an operational precondition, system performance degrades unpredictably in under-mapped or rural areas, preventing deployment outside densely curated metropolitan regions.

Possible candidate · 3/5 runs3 papers report this100% from 2025+
Generated automatically, not curated by hand. Automated prior-work checks catch about a third of existing work, so treat this problem as a lead to investigate.