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

Li Jin; Yuchen Yang; Weikai Chen; Yujie Wang; Dehao Hao; Tanghui Jia; Yingda Yin; Zeyu Hu; Runze Zhang; Keyang Luo; Li Yuan; Long Quan; Xin Wang; Xueying Qin

arXiv · 2603.07144

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

Li Jin·Yuchen Yang·Weikai Chen·Yujie Wang·Dehao Hao·Tanghui Jia·Yingda Yin·Zeyu Hu·Runze Zhang·Keyang Luo·Li Yuan·Long Quan·Xin Wang·Xueying Qin

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Abstract

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 \methodName{}, a massive canonical 3D dataset of 320K objects over 1,156 categories -- an order-of-magnitude 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 unlocks 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. The Canoverse dataset will be publicly released upon acceptance. Project page: https://github.com/123321456-gif/Canoverse

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Li Jin, Yuchen Yang, Weikai Chen, Yujie Wang, Dehao Hao, Tanghui Jia, Yingda Yin, Zeyu Hu, Runze Zhang, Keyang Luo, Li Yuan, Long Quan, Xin Wang, Xueying Qin. 2026-03-07. CanoVerse: 3D Object Scalable Canonicalization and Dataset for Generation and Pose. https://arxiv.org/abs/2603.07144

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