CanoVerse: 3D Object Scalable Canonicalization and Dataset for Generation and Pose

3D learning systems implicitly assume that objects occupy a coherent reference frame. Nonetheless, in practice, every asset arrives with an arbitrary global rotation, and models are left to resolve directional ambiguity on their own. This persistent misalignment suppresses pose-consistent generation, and blocks the emergence of stable directional semantics. To address this issue, we construct CanoVerse, a massive canonical 3D dataset of 320K objects over 1,156 categories – an order-ofmagnitude increase over prior work. At this scale, directional semantics become statistically learnable: CanoVerse improves 3D generation stability, enables precise cross-modal 3D shape retrieval, and strengthens zero-shot point-cloud orientation estimation even for out-of-distribution data. This is achieved by a new canonicalization framework that reduces alignment from minutes to seconds per object via compact hypothesis generation and lightweight human discrimination, transforming canonicalization from manual curation into a high-throughput data generation pipeline. Project page: https://github.com/123321456-gif/Canoverse