Every9D-21M: Large-Scale Real-World 9D Canonicalization of Everyday Objects
Estimating the 9D pose of everyday objects from a single real-world image remains challenging. This is largely due to the lack of large-scale supervision. Most existing datasets either rely heavily on synthetic renderings or provide limited coverage of real-world objects: the largest real-world 9D pose dataset to date contains only 17K annotated objects across 9 categories. We address this gap with Every9D-21M, a dataset of 9D pose annotations for 21.8M real-world images from 109K object- centric videos spanning 700 everyday object categories - two orders of magnitude larger than prior real-world 9D pose benchmarks in both image and category count. To achieve this scale, we leverage objec...
| Authors | Leonhard Sommer, Emil Akopyan, Adam Kortylewski |
|---|---|
| Primary category | cs.CV |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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