WildDet3D: Scaling Promptable 3D Detection in the Wild
Understanding objects in 3D from a single image is a cornerstone of spatial intelligence. A key step toward this goal is monocular 3D object detection--recovering the extent, location, and orientation of objects from an input RGB image. To be practical in the open world, such a detector must generalize beyond closed-set categories, support diverse prompt modalities, and leverage geometric cues when available. Progress is hampered by two bottlenecks: existing methods are designed for a single prompt type and lack a mechanism to incorporate additional geometric cues, and current 3D datasets cover only narrow categories in controlled environments, limiting open-world transfer. In this work w...
| Authors | Weikai Huang, Jieyu Zhang, Sijun Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 April 2026 |
| Last updated at source | 17 April 2026 |
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- allenai/WildDet3D-DataDataset · cited by
Models and datasets referencing this paper
- allenai/WildDet3D-DataDataset
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