Every9D-21M: Large-Scale Real-World 9D Canonicalization of Everyday Objects

Leonhard Sommer; Emil Akopyan; Adam Kortylewski

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arXiv · 2605.28270

Every9D-21M: Large-Scale Real-World 9D Canonicalization of Everyday Objects

Leonhard Sommer·Emil Akopyan·Adam Kortylewski

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Abstract

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 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-21M improves performance on ImageNet3D and PASCAL3D+, and generalizes to HANDAL substantially better than training on ImageNet3D. Data and code are available at https://github.com/GenIntel/Every9D.

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BibTeX RIS

Leonhard Sommer, Emil Akopyan, Adam Kortylewski. 2026-05-27. Every9D-21M: Large-Scale Real-World 9D Canonicalization of Everyday Objects. https://arxiv.org/abs/2605.28270

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