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. We address this gap with Every9D-22M, a dataset of 9D pose annotations for 22.8M real-world images from 114K object-centric videos spanning 630 everyday object categories, making it nearly two orders of magnitude larger than any prior real-world 9D pose dataset in at least one of category, object, or image count. To achieve this scale, we leverage object-centric videos by reconstructing object-level point clouds via multi-view geometry and aligning similar instances into a shared canonical coordinate frame. Canonical poses are manually annotated for only a small set of reference objects (fewer than 0.01% of all images) and propagated to the remaining instances via cross-instance alignment. All propagated canonical poses are then verified from multiple viewpoints. We further introduce cross-category orientation rules that induce category-level symmetries, enabling symmetry-aware evaluation. Beyond establishing dedicated training and evaluation splits as a benchmark for 9D pose foundation models, we show that training on Every9D-22M generalizes better to other datasets than training on existing datasets, and outperforms prior foundation models in rotation accuracy on all benchmarks except the viewpoint-biased ImageNet3D, where joint training with ImageNet3D surpasses them as well. Data and code are available at https://github.com/GenIntel/Every9D.

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