One-shot 3D Object Canonicalization based on Geometric and Semantic Consistency

3D object canonicalization is a fundamental task, essential for various downstream tasks. Existing methods rely on either cumbersome manual processes or priors learned from extensive, per- category training samples. Real- world datasets, however, often exhibit long- tail distributions, challenging existing learning- based methods, especially in categories with limited samples. We address this by introducing the first one- shot category- level object canonicalization framework that operates under arbitrary poses, requiring only a single canonical model as a reference (the "prior model") for each category. To canonicalize any object, our framework first extracts semantic cues with large language models (LLMs) and vision- language models (VLMs) to establish correspondences with the prior model. We introduce a novel joint energy function to enforce geometric and semantic consistency, aligning object orientations precisely despite significant shape variations. Moreover, we adopt a support- plane strategy to reduce search space for initial poses and utilize a semantic relationship map to select the canonical pose from multiple hypotheses. Extensive experiments on multiple datasets demonstrate that our framework achieves state- of- the- art performance and validates key design choices. Using our framework, we create the Canonical Objaverse Dataset (COD), canonicalizing 32K samples in the Objaverse-LVIS dataset, underscoring the effectiveness of our framework on handling large- scale datasets. Project page at https://github.com/JinLi998/CanonObjaverseDataset