CANIS: Generation-Assisted 3D Canonicalization via an Image-Semantic Bridge

• URL: http://arxiv.org/abs/2608.07256v1
• Date: Fri, 07 Aug 2026 14:13:00 GMT
• Title: CANIS: Generation-Assisted 3D Canonicalization via an Image-Semantic Bridge
• Abstract summary: CANIS is a category-agnostic, generation-assisted framework that introduces the semantic orientation prior to a frozen image-to-3D generative model into 3D canonicalization.<n>Canis also improves downstream 3D classification, part segmentation, and dense correspondence under arbitrary rotations.
• Score: 54.86517260361978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Canonicalizing 3D object orientation is fundamental to 3D understanding and analysis. Existing approaches often rely on geometric cues, although 3D canonicalization ultimately requires a semantically meaningful orientation. To address this gap, we propose CANIS, a category-agnostic, generation-assisted framework that introduces the semantic orientation prior of a frozen image-to-3D generative model into 3D canonicalization, without canonicalization-specific training or category-specific templates. Specifically, CANIS first renders the input object from candidate viewpoints, selects an informative view, and generates a proxy in a canonical orientation. During generation, a sparse structural latent encoded from the input guides the proxy to preserve the geometry of an object. CANIS then uses the selected image as a semantic bridge between the input and the proxy. Image patches identify semantic regions on the proxy, and depth back-projection locates the corresponding regions on the input. The resulting semantic anchors constrain geometric matching, from which we estimate the rigid transformation that canonicalizes the input. Experiments on synthetic benchmarks validate CANIS and its key components, while qualitative results on partial observations and OmniObject3D suggest its applicability to incomplete and real-world scans. CANIS also improves downstream 3D classification, part segmentation, and dense correspondence under arbitrary rotations. Project page: https://kenkenzaii.github.io/Canis.

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