Boxer: Robust Lifting of Open-World 2D Bounding Boxes to 3D
Detecting and localizing objects in space is a fundamental computer vision problem. While much progress has been made to solve 2D object detection, 3D object localization is much less explored and far from solved, especially for open-world categories. To address this research challenge, we propose Boxer, an algorithm to estimate static 3D bounding boxes (3DBBs) from 2D open-vocabulary object detections, posed images and optional depth either represented as a sparse point cloud or dense depth. At its core is BoxerNet, a transformer-based network which lifts 2D bounding box (2DBB) proposals into 3D, followed by multi-view fusion and geometric filtering to produce globally consistent de-dupl...
| Authors | Daniel DeTone, Tianwei Shen, Fan Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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