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Spherical imaging
A continuous surrounding observation captured with a DuxCam M4 spherical camera at 5188 × 1979 pixels.
360° × 136.70°horizontal × vertical FoV
Spherical 3D perception dataset · 2026
Spheriverse
3D Scene Understanding from
Spherical Observations in the Wild
89,674Spherical images
644Image–LiDAR sequences
13Diverse regions
30+Benchmarked methods
Dataset in motion
A continuous spherical drive.
Explore one continuous real-world sequence through its complete surrounding view—the native visual domain of Spheriverse and the starting point for its 3D perception tasks.
Dataset at a glance
Built for real-world spherical spatial perception.
Spheriverse combines high-quality spherical imagery with dense LiDAR observations and spatial annotations across geographically and visually diverse driving environments.
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Dense LiDAR sensing
High-density range observations provide reliable geometric support for 3D annotation.
128 beamsHesai OT128 LiDAR
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Synchronized acquisition
Camera, LiDAR, and GNSS/INS streams are aligned within a unified acquisition platform.
20 s · 5 Hzper synchronized sequence
Scene diversity
Five major scene categories.
Thirteen fine-grained environments.
Traffic function, environmental context, spatial layout, illumination, and weather vary substantially across the collection.
Urban Core and Mixed-Use Zones
1.1Central Functional ZoneSequence 294 · 5 s
1.2Mixed Residential–Commercial DistrictsSequence 526 · 5 s
Expressway Corridors and Peri-urban Areas
2.1Cross-Regional Expressway CorridorsSequence 44 · 5 s
2.2Urban–Rural Interface ZonesSequence 354 · 5 s
Functional and Restricted Operational Areas
3.1Waterfront AreasSequence 28 · 5 s
3.2Construction ZoneSequence 541 · 5 s
Structurally Constrained Transportation Areas
4.1Elevated Bridge EnvironmentsSequence 64 · 5 s
4.2Underground Confined EnvironmentsSequence 693 · 5 s
4.3Multi-Level Structural SpacesSequence 324 · 5 s
Rural and Natural Passage Environments
5.1Rural RoadsSequence 364 · 5 s
5.2Forest TracksSequence 437 · 5 s
5.3Village Access LanesSequence 380 · 5 s
5.4Farm Field TracksSequence 419 · 5 s
Semantic annotations
One dataset,
three levels of understanding.
The hierarchy preserves detailed real-world concepts while providing standardized category spaces for reproducible evaluation.
33Fine-grained classes
→
14Intermediate classes
→
9Unified classes
Pedestrian
Vehicle
Cyclist
Building
Vegetation
Pole & Barrier
Road
Surface
Others
Unified benchmarks
Three tasks for spherical 3D scene understanding.
More than 30 representative methods are evaluated under common protocols with overall and scene-wise analyses.
Reference model
SphereOcc
An occupancy framework that integrates spherical geometry and semantic evidence into metric voxel representations through a geometry–semantics coupled cross-space mechanism.
13.91%mIoU+1.70 pp vs. prior best
24.65%GeoIoU+2.10 pp vs. prior best
Spherical geometry organization
Forms Cartesian–spherical voxel features by embedding sphere-aligned range–azimuth relations.
Semantic evidence re-querying
Uses RTZ-conditioned voxel queries to retrieve relevant evidence from source spherical image features.
Spatial statistics
Analyze the world by direction and distance.
Angular and radial partitions reveal strong spatial variation in class composition, reflecting the directional structure and long-range imbalance of real driving scenes.
Resources
Start exploring Spheriverse.
ManuscriptPaper PDFComing soon
ImplementationSource codeComing soon
Dataset releaseDownload dataComing soon
EvaluationBenchmark serverComing soon
Citation
Cite Spheriverse
Coming soon